[{"data":1,"prerenderedAt":3906},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":23,"resources-listing":304,"navbar-pricing-featured":3136},{"id":4,"title":5,"date":6,"dismissible":7,"extension":8,"link":9,"link2":10,"linkText":11,"linkText2":10,"meta":12,"stem":20,"variant":21,"__hash__":22},"banners\u002Fbanners\u002Fkafka-company-2025.md","Native Apache Kafka Service Is Coming Soon to StreamNative Cloud. Join the waitlist and get $1,000 in credits.","2026-04-01",true,"md","\u002Fnative-kafka-service-waitlist",null,"Join Waitlist",{"body":13},{"type":14,"value":15,"toc":16},"minimark",[],{"title":17,"searchDepth":18,"depth":18,"links":19},"",2,[],"banners\u002Fkafka-company-2025","default","IMIJszQOOWTfA_DV33eYUA5jqV7DrX1FWbBTBZfNvWc",{"id":24,"title":25,"authors":26,"body":28,"category":288,"createdAt":10,"date":289,"description":290,"extension":8,"featured":7,"image":291,"isDraft":292,"link":10,"meta":293,"navigation":7,"order":294,"path":295,"readingTime":296,"relatedResources":10,"seo":297,"stem":298,"tags":299,"__hash__":303},"blogs\u002Fblog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025.md","StreamNative Recognized as a Contender in The Forrester Wave™: Streaming Data Platforms, Q4 2025",[27],"David Kjerrumgaard",{"type":14,"value":29,"toc":275},[30,38,46,50,66,72,77,80,86,101,108,114,117,123,126,133,139,142,145,156,162,168,171,174,177,183,190,193,196,203,206,209,223,228,232,236,240,244,248,250,267,269],[31,32,34],"h3",{"id":33},"receives-highest-possible-scores-in-both-the-messaging-and-resource-optimization-criteria",[35,36,37],"em",{},"Receives Highest Possible Scores in BOTH the Messaging and Resource Optimization Criteria",[39,40,42],"h2",{"id":41},"introduction",[43,44,45],"strong",{},"Introduction",[47,48,49],"p",{},"Real-time data has become the backbone of modern innovation. As artificial intelligence (AI) and digital services demand instantaneous insights, organizations are realizing that streaming data is no longer optional – it's essential for delivering timely, context-rich experiences. StreamNative's data streaming platform is built precisely for this reality, ensuring data is immediate, reliable, and ready to power critical applications.",[47,51,52,53,62,63],{},"Today, we're excited to announce that Forrester Research has named StreamNative as a Contender in its evaluation, ",[54,55,57],"a",{"href":56},"\u002Freports\u002Frecognized-in-the-forrester-wave-tm-streaming-data-platforms-q4-2025",[35,58,59],{},[43,60,61],{},"The Forrester Wave™: Streaming Data Platforms, Q4 2025",". This report evaluated 15 top streaming data platform providers, and we're proud to share that ",[43,64,65],{},"StreamNative received the highest scores possible—5 out of 5—in both the Messaging and Resource Optimization criteria.",[47,67,68,69],{},"***Forrester's Take: ***",[35,70,71],{},"\"StreamNative is a good fit for enterprises that want an Apache Pulsar implementation that is also compatible with Kafka APIs.\"",[47,73,74],{},[35,75,76],{},"— The Forrester Wave™: Streaming Data Platforms, Q4 2025",[47,78,79],{},"Being recognized in the Forrester Wave is a proud milestone, and for us, it highlights how far StreamNative has come in enabling enterprises to unlock the power of real-time data. In the sections below, we'll dive into what we believe sets StreamNative apart—from our modern architecture and cloud-native design to our open-source foundation and real-time use cases—and how we see these strengths aligning with Forrester's findings.",[39,81,83],{"id":82},"trusted-by-industry-leaders",[43,84,85],{},"Trusted by Industry Leaders",[47,87,88,89,92,93,96,97,100],{},"Companies across industries are already leveraging StreamNative to drive real-time outcomes. Global enterprises like ",[43,90,91],{},"Cisco"," rely on StreamNative to handle massive IoT telemetry, supporting 245 million+ connected devices. Martech leaders such as ",[43,94,95],{},"Iterable"," process billions of events per day with StreamNative for hyper-personalized customer engagement. And in financial services, ",[43,98,99],{},"FICO"," trusts StreamNative to power its real-time fraud detection and analytics pipelines with a secure, scalable streaming backbone.",[47,102,103,104,107],{},"The Forrester report notes that, “",[35,105,106],{},"Customers appreciate the lower infrastructure costs that result from StreamNative’s cost-efficient, Kafka-compatible architecture. Customers note excellent support responsiveness…","”",[39,109,111],{"id":110},"modern-cloud-native-architecture-built-for-scale",[43,112,113],{},"Modern, Cloud-Native Architecture Built for Scale",[47,115,116],{},"From day one, StreamNative was designed with a modern architecture to meet the demanding scale and flexibility requirements of real-time data. Unlike legacy streaming systems that often rely on tightly coupled storage and compute, StreamNative's platform takes a cloud-native approach: it decouples these layers to enable elastic scalability and efficient resource utilization across any environment. The core is powered by Apache Pulsar—a distributed messaging and streaming engine—enhanced with multi-protocol support (including native Apache Kafka API compatibility) to unify diverse data streams under one roof. This means organizations can consolidate siloed messaging systems and handle both high-volume event streams and traditional message queues on a single platform, without sacrificing performance or reliability.",[47,118,119,120,107],{},"Forrester's evaluation described that “",[35,121,122],{},"StreamNative aims to provide a high-performance, multi-protocol streaming data platform: It uses Apache Pulsar with Kafka API compatibility to deliver cost-efficient, real-time applications for enterprises. It appeals to organizations that want a flexible, low-cost streaming solution, due to its focus on scalability and resource optimization, while its investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.",[47,124,125],{},"Our cloud-first, leaderless architecture (with no single broker bottlenecks) and tiered storage model were built to maximize throughput and cost-efficiency for real-time workloads. By separating compute from storage and leveraging distributed object storage, StreamNative can retain huge volumes of event data indefinitely while keeping compute costs in check—effectively providing a flexible, low-cost streaming solution.",[47,127,128,129,132],{},"This modern design not only delivers high performance, but also ensures fault tolerance and geo-distribution out of the box, so enterprises can trust their streaming data is always available and durable. As Forrester’s evaluation noted, StreamNative ",[35,130,131],{},"\"excels at messaging and resource optimization\" and “Its platform supports use cases like real-time analytics and event-driven architectures with robust scalability.","” Our architecture provides the strong foundation that today's real-time applications demand, from ultra-fast data ingestion to seamless scale-out across hybrid and multi-cloud environments.",[39,134,136],{"id":135},"open-source-foundation-and-pulsar-expertise",[43,137,138],{},"Open Source Foundation and Pulsar Expertise",[47,140,141],{},"StreamNative's DNA is rooted in open source innovation. Our founders are the original creators of Apache Pulsar, and we've built our platform with the same open principles: freedom, flexibility, and community-driven innovation. For developers and data teams, this means adopting StreamNative comes with no proprietary lock-in—instead, you get a platform built on open standards and a thriving ecosystem. We offer broad API compatibility (Pulsar, Kafka, JMS, MQTT, and more) so that teams can work with familiar interfaces and integrate StreamNative into existing systems with ease.",[47,143,144],{},"StreamNative is the primary commercial contributor to the Apache Pulsar project and its surrounding ecosystem. We invest heavily in Pulsar's ongoing improvements our investments in Pulsar's open-source ecosystem and performance optimization bolster StreamNative's value. We also foster a vibrant community through initiatives like the Data Streaming Summit and free training resources.",[47,146,147,148,151,152,155],{},"Forrester's assessment noted that StreamNative’s “",[35,149,150],{},"events-driven agents, extensibility, and performance architecture are solid,","” and we're continuing to build on that foundation. ",[43,153,154],{},"We're actively investing in expanding our tooling for observability, governance, schema management, and developer productivity","—areas we recognize as critical for enterprise adoption and where we're committed to accelerating our roadmap.",[47,157,158,159],{},"Being open also means embracing an open ecosystem of technologies. StreamNative actively integrates with the tools and platforms that matter most to our users. We partner with industry leaders like Snowflake, Databricks, Google, and Ververica to ensure our streaming platform works seamlessly with data warehouses, lakehouse storage, and stream processing frameworks. Forrester’s evaluation observed that StreamNative’s ",[35,160,161],{},"\"investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.\"",[39,163,165],{"id":164},"powering-real-time-use-cases-across-industries",[43,166,167],{},"Powering Real-Time Use Cases Across Industries",[47,169,170],{},"One of the greatest validations of StreamNative's approach is the success our customers are achieving with real-time data. StreamNative's platform is versatile and use-case agnostic—if an application demands high-volume, low-latency data movement, we can power it. This flexibility is why our customer base spans industries from finance and IoT to major automobile manufacturers and online gaming. The common thread is that these organizations need to process and react to data in milliseconds, and StreamNative is delivering the capabilities to make that possible.",[47,172,173],{},"Cisco uses StreamNative to underpin an IoT telemetry system of colossal scale, connecting hundreds of millions of devices and thousands of enterprise clients with real-time data streams. The platform's multi-tenant design and proven reliability allow Cisco to offer its customers a live feed of device data with unwavering confidence. In the financial sector, FICO has built streaming pipelines on StreamNative to detect fraud as transactions happen and to monitor systems in real time. With StreamNative's strong guarantees around message durability and ordering, FICO can catch anomalies or suspicious patterns within seconds. And in digital customer engagement, Iterable relies on StreamNative to process billions of events every day—clicks, views, purchases—so that marketers can trigger personalized campaigns instantly based on user behavior.",[47,175,176],{},"Our customers uniformly deal with mission-critical data streams, where downtime or delays are unacceptable. StreamNative's fault-tolerant, scalable infrastructure has proven equal to the task, handling scenarios like bursting to millions of events per second or seamlessly spanning multiple cloud regions. Forrester's report recognized StreamNative for supporting event-driven architectures with robust scalability—which for us is a reflection of our platform's ability to meet the most demanding enterprise requirements.",[39,178,180],{"id":179},"continuing-to-innovate-ursa-orca-and-the-road-ahead",[43,181,182],{},"Continuing to Innovate: Ursa, Orca, and the Road Ahead",[47,184,185,186,189],{},"While we are thrilled to be recognized in Forrester's Streaming Data Platforms Wave, we view this as just the beginning. StreamNative's vision has always been bold: to ",[43,187,188],{},"provide a unified platform that not only handles today's streaming needs but also anticipates the emerging requirements of tomorrow",".",[47,191,192],{},"One key area of focus is the convergence of streaming data with advanced analytics and AI. As Forrester points out in the report, technology leaders should look for platforms that natively integrate messaging, stream processing, and analytics to provide AI agents with real-time, contextualized information. We couldn't agree more. Our award-winning Ursa Engine and Orca Agent Engine are aimed at extending our platform up the stack—bridging the gap between data streams and data lakes, and between event streams and intelligent processing.",[47,194,195],{},"Our new Ursa Engine introduces a lakehouse-native approach to streaming: it can write events directly to table formats like Iceberg on cloud storage, eliminating entire classes of ETL jobs and making fresh data instantly available for analytics queries. By integrating streaming and lakehouse technologies, we help customers collapse data silos and accelerate their AI\u002FML pipelines.",[47,197,198,199,202],{},"Beyond analytics integration, we are also enhancing StreamNative with more out-of-the-box processing and governance capabilities. In the coming months, we plan to introduce new features for lightweight stream processing and transformation, making it easier to build reactive applications directly on the platform. We're also expanding our ecosystem of connectors and integrations, so that whether your data lands in Snowflake, Databricks, or an AI model, StreamNative will seamlessly feed it. ",[43,200,201],{},"We're investing significantly in enterprise features including security, schema registry, governance, and monitoring tooling","—capabilities that are essential for mission-critical deployments and where we're committed to continued improvement.",[47,204,205],{},"This recognition from Forrester energizes us to keep innovating at full speed. We're sharing this honor with our amazing customers, community, and partners who drive us forward every day. Your feedback and real-world challenges have helped shape StreamNative into what it is today, and together, we will shape the future of streaming data. Thank you for joining us on this journey—we're just getting started, and we can't wait to deliver even more value as we continue to evolve our platform. Onward to real-time everything!",[207,208],"hr",{},[31,210,212],{"id":211},"streamnative-in-the-forrester-wave-evaluation-findings",[43,213,214,215,222],{},"StreamNative in ",[43,216,217],{},[54,218,219],{"href":56},[43,220,221],{},"The Forrester Wave™",": Evaluation Findings",[224,225,227],"h5",{"id":226},"recognized-as-a-contender-among-15-streaming-data-platform-providers","• Recognized as a Contender among 15 streaming data platform providers",[224,229,231],{"id":230},"received-the-highest-scores-possible-50-in-both-the-messaging-and-resource-optimization-criteria","* Received the highest scores possible (5.0) in both the Messaging and Resource Optimization criteria",[224,233,235],{"id":234},"cited-as-the-primary-platform-for-enterprises-wishing-to-implement-pulsar","• Cited as the primary platform for enterprises wishing to implement Pulsar",[224,237,239],{"id":238},"noted-for-excelling-at-messaging-and-resource-optimization","• Noted for excelling at messaging and resource optimization",[224,241,243],{"id":242},"customers-cited-lower-infrastructure-costs-and-excellent-support-responsiveness","• Customers cited lower infrastructure costs and excellent support responsiveness",[224,245,247],{"id":246},"recognized-for-supporting-event-driven-architectures-with-robust-scalability","• Recognized for supporting event-driven architectures with robust scalability",[207,249],{},[251,252,254,255,258,259,189],"h6",{"id":253},"forrester-disclaimer-forrester-does-not-endorse-any-company-product-brand-or-service-included-in-its-research-publications-and-does-not-advise-any-person-to-select-the-products-or-services-of-any-company-or-brand-based-on-the-ratings-included-in-such-publications-information-is-based-on-the-best-available-resources-opinions-reflect-judgment-at-the-time-and-are-subject-to-change-for-more-information-read-about-forresters-objectivity-here","**Forrester Disclaimer: **",[35,256,257],{},"Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change",". *For more information, read about Forrester’s objectivity *",[54,260,264],{"href":261,"rel":262},"https:\u002F\u002Fwww.forrester.com\u002Fabout-us\u002Fobjectivity\u002F",[263],"nofollow",[35,265,266],{},"here",[207,268],{},[251,270,272],{"id":271},"apache-apache-pulsar-apache-kafka-apache-flink-and-other-names-are-trademarks-of-the-apache-software-foundation-no-endorsement-by-apache-or-other-third-parties-is-implied",[35,273,274],{},"Apache®, Apache Pulsar®, Apache Kafka®, Apache Flink® and other names are trademarks of The Apache Software Foundation. No endorsement by Apache or other third parties is implied.",{"title":17,"searchDepth":18,"depth":18,"links":276},[277,279,280,281,282,283,284],{"id":33,"depth":278,"text":37},3,{"id":41,"depth":18,"text":45},{"id":82,"depth":18,"text":85},{"id":110,"depth":18,"text":113},{"id":135,"depth":18,"text":138},{"id":164,"depth":18,"text":167},{"id":179,"depth":18,"text":182,"children":285},[286],{"id":211,"depth":278,"text":287},"StreamNative in The Forrester Wave™: Evaluation Findings","Company","2025-12-16","StreamNative is recognized in The Forrester Wave™: Streaming Data Platforms, Q4 2025. Discover why Forrester highlights StreamNative's high-performance messaging, efficient resource use, and cost-effective Kafka API compatibility for real-time innovation.","\u002Fimgs\u002Fblogs\u002F693bd36cf01b217dcb67278f_Streamnative_blog_thumbnail.png",false,{},0,"\u002Fblog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025","10 mins read",{"title":25,"description":290},"blog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025",[300,301,302],"Announcements","Real-Time","Forrester","sOeeJtEO3O-IIfTPJjY1AFOMawZ_rf8FOH8A98NEKgU",[305,346,391,475,514,553,576,619,638,689,729,777,816,853,870,911,948,993,1039,1084,1125,1171,1209,1253,1303,1348,1386,1424,1485,1535,1588,1606,1661,1688,1751,1768,1784,1831,1848,1864,1880,1895,1911,1926,1942,1985,2099,2202,2230,2253,2298,2329,2362,2384,2432,2454,2493,2532,2585,2612,2654,2698,2733,2758,2793,2822,2845,2871,2894,2947,2988,3012,3038,3063,3089],{"id":306,"title":307,"body":308,"createdAt":10,"date":329,"description":330,"duration":10,"extension":8,"featured":7,"formTitle":331,"hubspotId":332,"image":333,"isDraft":292,"isUpcoming":292,"link":10,"meta":334,"navigation":7,"order":294,"path":335,"readingTime":10,"relatedResources":10,"seo":336,"speakers":337,"startDate":10,"stem":339,"submitText":340,"successDescription":341,"successTitle":342,"tags":10,"time":10,"videoUrl":343,"__hash__":344,"resourceType":345},"webinars\u002Fwebinars\u002Fagentic-ai-mcp-server.md","Agentic AI Meets Real-Time Streaming: Introducing the StreamNative Remote MCP Server",{"type":14,"value":309,"toc":327},[310,313,324],[47,311,312],{},"As AI systems become more autonomous, they need real-time, secure ways to interact with data.",[47,314,315,316,319,320,323],{},"Join our webinar to explore ",[43,317,318],{},"StreamNative Remote MCP Server (Public Preview)",", which provides a managed ",[43,321,322],{},"Model Context Protocol (MCP) endpoint"," for StreamNative clusters—no local deployments required.",[47,325,326],{},"See how to connect AI agents, copilots, and automation frameworks with real-time data, learn about cluster-level capabilities, and get a glimpse of upcoming organization-level MCP support. Perfect for anyone building AI-powered assistants, diagnostics, or automation on streaming data.",{"title":17,"searchDepth":18,"depth":18,"links":328},[],"2026-02-01","Join our webcast to explore StreamNative Remote MCP Server (Public Preview), which provides a managed Model Context Protocol (MCP) endpoint for StreamNative clusters — no local deployments required.","Watch the Webinar","62af7d89-3c8f-40a1-b414-7aec003b1d7f","\u002Fimgs\u002Fwebinars\u002F698d94549d8d5fa55dab76c3_Webinar-LinkedIn.png",{},"\u002Fwebinars\u002Fagentic-ai-mcp-server",{"title":307,"description":330},[338,27],"Kundan Vyas","webinars\u002Fagentic-ai-mcp-server","SUBMIT","Enjoy the webinar recording.","Thank You!","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=OmHCbeoHhPg","3bX0CO3yKUyGgMFFZYbgDbb1UYjrh2UDxlUrnKTA_2k","Webinar",{"id":347,"title":348,"body":349,"createdAt":10,"date":377,"description":378,"duration":10,"extension":8,"featured":292,"formTitle":331,"hubspotId":379,"image":380,"isDraft":292,"isUpcoming":292,"link":10,"meta":381,"navigation":7,"order":294,"path":382,"readingTime":10,"relatedResources":10,"seo":383,"speakers":384,"startDate":10,"stem":386,"submitText":340,"successDescription":387,"successTitle":388,"tags":10,"time":10,"videoUrl":389,"__hash__":390,"resourceType":345},"webinars\u002Fwebinars\u002Fstreamnative-volt-active-data.md","StreamNative + Volt Active Data: Real-Time Intelligence from Ingest to Action",{"type":14,"value":350,"toc":374},[351,354,358],[47,352,353],{},"Join StreamNative and Volt Active Data for an on-demand webinar exploring how real-time data streaming and in-memory transactional processing work together to power low-latency, mission-critical applications at scale.",[31,355,357],{"id":356},"what-youll-learn","What You'll Learn",[359,360,361,365,368,371],"ul",{},[362,363,364],"li",{},"How StreamNative's high-throughput event streaming pairs with Volt Active Data's millisecond decisioning",[362,366,367],{},"How to execute stateful, ACID-compliant business logic on live data streams",[362,369,370],{},"Use cases in telecommunications, financial services, and industrial IoT",[362,372,373],{},"Architecture patterns for building ingest-to-action pipelines with real-time intelligence",{"title":17,"searchDepth":18,"depth":18,"links":375},[376],{"id":356,"depth":278,"text":357},"2026-01-29","Learn how StreamNative and Volt Active Data combine real-time data streaming with in-memory transactional processing to power low-latency, mission-critical applications at scale.","95a4cbb3-0c8a-4f07-b631-ff812970e089","\u002Fimgs\u002Fwebinars\u002Fstreamnative-volt-active-data.png",{},"\u002Fwebinars\u002Fstreamnative-volt-active-data",{"title":348,"description":378},[338,385],"Dheeraj Remella","webinars\u002Fstreamnative-volt-active-data","You now have full access and can watch the session anytime.","Enjoy the Webinar!","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=4jASff-CV34","o1SmaGC1-atDavEWGqb8SlemToNCQQua-9dttsErtNw",{"id":392,"title":393,"authors":10,"body":394,"createdAt":10,"date":289,"description":463,"downloadLink":464,"extension":8,"featured":7,"formTitle":10,"hubspotId":465,"image":466,"isDraft":292,"link":10,"meta":467,"navigation":7,"order":294,"path":56,"readingTime":10,"redirectDelay":468,"relatedResources":10,"seo":469,"stem":470,"submitText":10,"successDescription":10,"successTitle":10,"tags":471,"__hash__":473,"resourceType":474},"reports\u002Freports\u002Frecognized-in-the-forrester-wave-tm-streaming-data-platforms-q4-2025.md","StreamNative is Recognized in The Forrester Wave™: Streaming Data Platforms, Q4 2025",{"type":14,"value":395,"toc":457},[396,400,403,409,413,433,442,446],[39,397,399],{"id":398},"about-this-report","About This Report",[47,401,402],{},"Forrester Research evaluated 15 top streaming data platform providers in The Forrester Wave™: Streaming Data Platforms, Q4 2025.",[47,404,405,406,189],{},"StreamNative received the ",[43,407,408],{},"highest possible scores — 5 out of 5 — in both the Messaging and Resource Optimization criteria",[31,410,412],{"id":411},"key-findings","Key Findings",[359,414,415,421,427],{},[362,416,417,420],{},[43,418,419],{},"High-Performance Messaging",": StreamNative's Apache Pulsar-based architecture delivers industry-leading messaging performance for real-time applications.",[362,422,423,426],{},[43,424,425],{},"Resource Optimization",": The platform's cloud-native, decoupled storage and compute architecture enables efficient resource utilization and elastic scalability.",[362,428,429,432],{},[43,430,431],{},"Kafka API Compatibility",": Native Kafka protocol support allows organizations to consolidate diverse streaming workloads on a single platform.",[434,435,436,440],"blockquote",{},[47,437,438],{},[35,439,71],{},[47,441,76],{},[31,443,445],{"id":444},"who-should-read-this-report","Who Should Read This Report",[359,447,448,451,454],{},[362,449,450],{},"Engineering leaders evaluating streaming data platforms",[362,452,453],{},"Architects designing real-time data infrastructure",[362,455,456],{},"Decision makers comparing Apache Kafka and Apache Pulsar solutions",{"title":17,"searchDepth":18,"depth":18,"links":458},[459],{"id":398,"depth":18,"text":399,"children":460},[461,462],{"id":411,"depth":278,"text":412},{"id":444,"depth":278,"text":445},"Get the Forrester Wave™ report on Streaming Data Platforms, Q4 2025. 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You're not alone. As a forward-thinking IT leader, you know that Kafka has revolutionized data streaming, but at what price? Don't let operational costs prevent you from leveraging Kafka's full power. Our white paper reveals:",[359,1181,1182,1185,1188],{},[362,1183,1184],{},"Innovative strategies to minimize Kafka costs",[362,1186,1187],{},"Techniques to maintain high performance for critical use cases",[362,1189,1190],{},"Real-world case studies of successful Kafka optimizations",[47,1192,1193],{},"Join the ranks of forward-thinking IT leaders who are maximizing their Kafka investment. Download our whitepaper today and start your journey towards a more efficient, cost-effective Kafka infrastructure.",[47,1195,647],{},{"title":17,"searchDepth":18,"depth":18,"links":1197},[],"Download Cost Effective Kafka whitepaper today and start your journey towards a more efficient, cost-effective Kafka infrastructure.","https:\u002F\u002F6585952.fs1.hubspotusercontent-na1.net\u002Fhubfs\u002F6585952\u002FWhitepapers\u002FThe%20Economics%20of%20Kafka%20Whitepaper.pdf","6941f1b7-676e-45dd-9d3f-4e02ab14382e","\u002Fimgs\u002Fwhitepapers\u002F66e40829b1d8eda611d9ee6a_Cost-effective-Kafka.jpg",{},"\u002Fwhitepapers\u002Fcost-effective-kafka-strategies-for-cost-reduction-and-increased-efficiency","10 min. read",{"title":1173,"description":1198},"whitepapers\u002Fcost-effective-kafka-strategies-for-cost-reduction-and-increased-efficiency",[685,990],"87y8nBh-zQA0t9vIJMvZP-O50F0xE55Bo-tv11jflq4",{"id":1210,"title":1211,"body":1212,"createdAt":10,"date":1238,"description":1239,"duration":1240,"extension":8,"featured":7,"formTitle":10,"hubspotId":1241,"image":1242,"isDraft":292,"isUpcoming":292,"link":10,"meta":1243,"navigation":7,"order":294,"path":1244,"readingTime":10,"relatedResources":10,"seo":1245,"speakers":1246,"startDate":10,"stem":1248,"submitText":10,"successDescription":10,"successTitle":10,"tags":1249,"time":10,"videoUrl":1251,"__hash__":1252,"resourceType":345},"webinars\u002Fwebinars\u002Ftransforming-indoor-experiences-with-real-time-data-processing-an-innerspace-case-study.md","Transforming Indoor Experiences with Real-Time Data Processing: An Innerspace Case Study",{"type":14,"value":1213,"toc":1235},[1214,1216,1219,1233],[39,1215,357],{"id":356},[47,1217,1218],{},"Kundan Vyas, Product Manager, StreamNative, and Matt MacGillivray, Co-Founder, R&D at Innerspace, will present a series of use cases for data processing and reasons for using Pulsar Functions, covering the following topics:",[359,1220,1221,1224,1227,1230],{},[362,1222,1223],{},"Introduction to InnerSpace",[362,1225,1226],{},"Challenges in Real-Time Data Processing",[362,1228,1229],{},"Solution: Transition to StreamNative's Managed Apache Pulsar Service",[362,1231,1232],{},"Technical Implementation and Key Integrations",[47,1234,647],{},{"title":17,"searchDepth":18,"depth":18,"links":1236},[1237],{"id":356,"depth":18,"text":357},"2024-08-19","Join us and explore the seamless integration of real-time data streaming with advanced analytics to drive business insights and product recommendations.","52:31","befdd837-6872-4af3-b7f7-eb10989fe02d","\u002Fimgs\u002Fwebinars\u002F66c377eb74998bdd60d3a0d0_StreamNative%20x%20Innnerspace%20Webinar.png",{},"\u002Fwebinars\u002Ftransforming-indoor-experiences-with-real-time-data-processing-an-innerspace-case-study",{"title":1211,"description":1239},[338,1247],"Matt MacGillivray","webinars\u002Ftransforming-indoor-experiences-with-real-time-data-processing-an-innerspace-case-study",[472,1250,301],"Functions","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=0Rg70ahR7wE","TheraH2NFyi2HdXkp_3lbB_KQICKPLWoXFoZ4QYwh7M",{"id":1254,"title":1255,"body":1256,"createdAt":10,"date":1288,"description":1289,"duration":1290,"extension":8,"featured":7,"formTitle":10,"hubspotId":1291,"image":1292,"isDraft":292,"isUpcoming":292,"link":10,"meta":1293,"navigation":7,"order":294,"path":1294,"readingTime":10,"relatedResources":10,"seo":1295,"speakers":1296,"startDate":10,"stem":1297,"submitText":10,"successDescription":10,"successTitle":10,"tags":1298,"time":10,"videoUrl":1301,"__hash__":1302,"resourceType":345},"webinars\u002Fwebinars\u002Fharnessing-real-time-data-streams-with-streamnative-and-databricks.md","Harnessing Real-Time Data Streams with StreamNative and Databricks",{"type":14,"value":1257,"toc":1285},[1258,1260,1263,1266,1269,1283],[39,1259,1092],{"id":1091},[47,1261,1262],{},"In this session, experts from StreamNative and Databricks will demonstrate how customers can leverage StreamNative Cloud to efficiently stream data through Lakehouse Storage into Databricks. We will showcase how this powerful combination enables businesses to harness the full potential of their data in real time.",[47,1264,1265],{},"Learn how to use Databricks' advanced analytics and Large Language Models (LLMs) to generate accurate and personalized product recommendations. 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Built on the Apache Pulsar framework, Ursa provides unparalleled performance, flexibility, and ease of deployment. In this whitepaper, you'll discover how to meet modern data demands with cost efficiency.",[47,1313,1314],{},"Key Topics Include:",[359,1316,1317,1320,1323,1326,1329],{},[362,1318,1319],{},"Infinite Scalability : Scale your data streams seamlessly with Ursa. Leverage cloud-native features to grow without limits, ensuring your system can handle any data load.",[362,1321,1322],{},"Lower Total Cost of Ownership: Enjoy lower TCO with Ursa's cost-efficient architecture. By running directly on commodity object stores like AWS S3, GCP GCS, and Azure Blob Storage, we help you save on storage and operational costs.",[362,1324,1325],{},"Seamless Deployment Anywhere: Deploy Ursa effortlessly wherever Kubernetes runs. Our solution fits your existing infrastructure, allowing for quick and smooth integration.",[362,1327,1328],{},"Apache Kafka API-Compatible: Built for compatibility, Ursa supports the Apache Kafka API, providing a versatile data streaming engine that ensures a smooth transition and interoperability with your current systems.",[362,1330,1331],{},"Support for Lakehouse Table Formats: Ursa stores streams in lakehouse table formats, such as Hudi, Iceberg, and Delta Lake, making it easier to manage and analyze your data efficiently.",[47,1333,1334],{},"Ready to optimize your data streaming for modern cloud environments? Discover how your organization can experience infinite scalability, lower costs, and seamless deployment.",{"title":17,"searchDepth":18,"depth":18,"links":1336},[],"2024-07-01","Download the Ursa whitepaper now and discover how your organization can experience infinite scalability, lower costs, and seamless deployment.","https:\u002F\u002F6585952.fs1.hubspotusercontent-na1.net\u002Fhubfs\u002F6585952\u002FWhitepapers\u002FURSA%20Whitepaper.pdf","2400aab0-f637-4ae0-83d9-b4b442c9f0f8","\u002Fimgs\u002Fwhitepapers\u002F6683b52841bc291de71193e2_URSA-Whitepaper-Thumbnail.jpg",{},"\u002Fwhitepapers\u002Fursa-whitepaper-the-kafka-api-compatible-data-streaming-engine-built-on-the-lakehouse",{"title":1305,"description":1338},"whitepapers\u002Fursa-whitepaper-the-kafka-api-compatible-data-streaming-engine-built-on-the-lakehouse",[686,472],"waCgawbCwws39AWgbCAu2CwAtJRRkaLTKM2FIwxnTPQ",{"id":1349,"title":1350,"body":1351,"createdAt":10,"date":1372,"description":1373,"duration":1374,"extension":8,"featured":7,"formTitle":331,"hubspotId":1375,"image":1376,"isDraft":292,"isUpcoming":292,"link":10,"meta":1377,"navigation":7,"order":294,"path":1378,"readingTime":10,"relatedResources":10,"seo":1379,"speakers":1380,"startDate":10,"stem":1381,"submitText":340,"successDescription":387,"successTitle":388,"tags":1382,"time":10,"videoUrl":1384,"__hash__":1385,"resourceType":345},"webinars\u002Fwebinars\u002Fstreamnative-roadmap-webinar-for-q3-2024.md","StreamNative Roadmap Webinar for Q3 2024",{"type":14,"value":1352,"toc":1369},[1353,1355,1358],[31,1354,1092],{"id":1091},[47,1356,1357],{},"Discover the future of StreamNative in our Q3 2024 Roadmap Webinar! Our product managers Karan Malhi and Kundan Vyas will walk you through exciting updates and new features, including:",[359,1359,1360,1363,1366],{},[362,1361,1362],{},"Ursa Engine RoadmapKafka compatibility",[362,1364,1365],{},"No Keeper: Learn about the advancements in removing ZooKeeper and BookKeeper to optimize performance and lower TCO.",[362,1367,1368],{},"Lakehouse Integration: Explore the upcoming integrations designed to streamline your data lakehouse experience. 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Our product managers Karan Malhi and Kundan Vyas will walk you through exciting updates and new features.","56:18","7f1c62d1-bfdf-421c-a0ca-cf74cbc28815","\u002Fimgs\u002Fwebinars\u002F667ecab3d34d0b862e643677_on%20demand%20banner.png",{},"\u002Fwebinars\u002Fstreamnative-roadmap-webinar-for-q3-2024",{"title":1350,"description":1373},[847],"webinars\u002Fstreamnative-roadmap-webinar-for-q3-2024",[686,1383,685,908,990],"BookKeeper","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=SUAOgkTS13U","yPdUggzHFw_8BeFdKpIZ1v320wtpr1f1bY78RjZ1gx8",{"id":1387,"title":1388,"body":1389,"createdAt":10,"date":1412,"description":1393,"duration":1413,"extension":8,"featured":7,"formTitle":10,"hubspotId":1414,"image":1415,"isDraft":292,"isUpcoming":292,"link":10,"meta":1416,"navigation":7,"order":294,"path":1417,"readingTime":10,"relatedResources":10,"seo":1418,"speakers":1419,"startDate":10,"stem":1420,"submitText":10,"successDescription":10,"successTitle":10,"tags":1421,"time":10,"videoUrl":1422,"__hash__":1423,"resourceType":345},"webinars\u002Fwebinars\u002Funlock-the-power-of-real-time-insights-with-our-exclusive-webinar.md","Unlock the Power of Real-Time Insights",{"type":14,"value":1390,"toc":1410},[1391,1394,1397,1408],[47,1392,1393],{},"In today’s fast-paced digital ecosystem, real-time data is king. Whether you’re a seasoned data professional or just dipping your toes into the world of streaming technology, this webinar is your gateway to unlocking the full potential of data streaming.",[47,1395,1396],{},"Agenda:",[359,1398,1399,1402,1405],{},[362,1400,1401],{},"Insights from the Experts: Gain valuable insights from industry veterans as they dissect the ever-evolving landscape of data streaming technologies.",[362,1403,1404],{},"Deep Dive into Gigaom Radar Report: Get an exclusive look at the Gigaom Radar report for Streaming Data Platforms. Discover key findings, emerging trends, and actionable recommendations to drive your organization forward.",[362,1406,1407],{},"Interactive Q&A Session: Have burning questions about data streaming? Don’t miss the chance to engage directly with our experts and get answers tailored to your specific needs.",[47,1409,647],{},{"title":17,"searchDepth":18,"depth":18,"links":1411},[],"2024-06-12","52:00","3717be02-7d56-4c4b-b3c8-d8b7f10a094f","\u002Fimgs\u002Fwebinars\u002F667da4e575eea91bcd3be3e2_Screenshot%202024-06-07%20at%208.34.34%E2%80%AFAM.png",{},"\u002Fwebinars\u002Funlock-the-power-of-real-time-insights-with-our-exclusive-webinar",{"title":1388,"description":1393},[847],"webinars\u002Funlock-the-power-of-real-time-insights-with-our-exclusive-webinar",[301],"https:\u002F\u002Fyoutu.be\u002FXB4U5aYgt_A","wMBcExT_UodSdKjQNdpGusJSa_N-Pr7QeMZkCXWsTO8",{"id":1425,"title":1426,"body":1427,"createdAt":10,"date":1470,"description":1471,"duration":1472,"extension":8,"featured":7,"formTitle":10,"hubspotId":1473,"image":1474,"isDraft":292,"isUpcoming":292,"link":10,"meta":1475,"navigation":7,"order":294,"path":1476,"readingTime":10,"relatedResources":10,"seo":1477,"speakers":1478,"startDate":10,"stem":1479,"submitText":10,"successDescription":10,"successTitle":10,"tags":1480,"time":10,"videoUrl":1483,"__hash__":1484,"resourceType":345},"webinars\u002Fwebinars\u002Fdecoding-kafka-challenges-addressing-common-pain-points-in-kafka-deployments.md","Decoding Kafka Challenges: Addressing Common Pain Points in Kafka Deployments",{"type":14,"value":1428,"toc":1468},[1429,1432,1434,1454,1457],[47,1430,1431],{},"In the realm of distributed event streaming, Apache Kafka stands tall as a powerful and versatile platform, enabling real-time data processing, messaging, and event-driven architectures. However, deploying and managing Kafka at scale comes with challenges and pain points. Join us for a deep dive into these challenges and how Apache Pulsar addresses them.",[47,1433,1396],{},[359,1435,1436,1439,1442,1445,1448,1451],{},[362,1437,1438],{},"Exploring Common Pain Points in Kafka DeploymentsUnderstanding Multi-Tenancy Challenges",[362,1440,1441],{},"The Repartitioning Dilemma",[362,1443,1444],{},"Data Retention Challenges",[362,1446,1447],{},"Messaging Queueing Challenges",[362,1449,1450],{},"Geo-Replication Challenges",[362,1452,1453],{},"Conclusion and Q&A",[47,1455,1456],{},"Key Takeaways:",[359,1458,1459,1462,1465],{},[362,1460,1461],{},"Understanding the complexities of Kafka deployments",[362,1463,1464],{},"Learning how Apache Pulsar addresses common pain points",[362,1466,1467],{},"Insights into effective strategies for Kafka deployment and management",{"title":17,"searchDepth":18,"depth":18,"links":1469},[],"2024-02-23","Apache Kafka stands tall as a powerful and versatile platform, enabling real-time data processing, messaging, and event-driven architectures. However, deploying and managing Kafka at scale comes with challenges and pain points. 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Organizations worldwide have embraced Kafka for its scalability, durability, and reliability. However, as with any complex technology, deploying and managing Kafka at scale comes with challenges and pain points.",[47,1495,1496],{},"This whitepaper is designed to guide you through the common pain points of Kafka deployments and how Apache Pulsar addresses those. We will delve into the common pain points enterprises, developers, and operations teams face when implementing and maintaining Kafka clusters. From operational complexities to performance bottlenecks, we'll explore the challenges that can arise during Kafka usage and provide insights into effective strategies for mitigation.",[47,1498,1499],{},"Key Pain Points and Solutions:",[359,1501,1502,1505,1508,1511,1514],{},[362,1503,1504],{},"Unlock Multi-Tenancy: Seamlessly manage diverse teams and applications with Pulsar's native multi-tenancy.",[362,1506,1507],{},"Empower Dynamic Scaling: Bid farewell to repartitioning woes. 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In this talk, we’ll get you up to speed with the latest in streaming SQL with Flink and demo how you can integrate with Apache Pulsar to build unified, elastic data processing pipelines.",[47,2179,647],{},[434,2181,2182],{},[47,2183,2184],{},[54,2185,2187],{"href":2186},"\u002Fvideo-webinar\u002Fapache-flink-x-pulsar-virtual-meetup-streaming-sql-at-uber-and-facebook-day-one","See Day 1 of the Apache Flink x Pulsar Virtual Meetup ",{"title":17,"searchDepth":18,"depth":18,"links":2189},[2190,2193],{"id":1998,"depth":18,"text":2001,"children":2191},[2192],{"id":2113,"depth":278,"text":2114},{"id":2057,"depth":18,"text":2058},"The meetup is a two-day virtual event co-hosted by Uber, Ververica, and StreamNative, this video is the recording of our Day-2 sharing","01:17:38",{},"\u002Fwebinars\u002Fapache-flink-x-pulsar-virtual-meetup-streaming-sql-at-uber-and-facebook-day-two",{"title":2101,"description":2194},"webinars\u002Fapache-flink-x-pulsar-virtual-meetup-streaming-sql-at-uber-and-facebook-day-two",[472,1658],"YMBk5U5-_2YWb41pCoAnMQH7HWJ1vzAJOEwrAyLFAJM",{"id":2203,"title":2204,"body":2205,"createdAt":10,"date":2087,"description":2209,"duration":2220,"extension":8,"featured":292,"formTitle":10,"hubspotId":2090,"image":2221,"isDraft":292,"isUpcoming":292,"link":10,"meta":2222,"navigation":7,"order":294,"path":2223,"readingTime":10,"relatedResources":10,"seo":2224,"speakers":2225,"startDate":10,"stem":2226,"submitText":10,"successDescription":10,"successTitle":10,"tags":2227,"time":10,"videoUrl":2228,"__hash__":2229,"resourceType":345},"webinars\u002Fwebinars\u002Fapache-pulsar-in-action-by-david-kjerrumgaard-book-launch.md","\"Apache Pulsar in Action\" by David Kjerrumgaard - Book Launch",{"type":14,"value":2206,"toc":2218},[2207,2210,2213,2216],[47,2208,2209],{},"Learn How to Maximize the Value of your Data With an Event-Driven Architecture",[47,2211,2212],{},"Today, many companies are undergoing digital transformation in order to maximize the business value of their data. They are pivoting away from a pure streaming workload to unified batch and streaming workloads, migrating from monolithic architecture to microservices, and looking for new solutions to unlock new use cases. Apache Pulsar’s cloud-native capabilities and unified messaging model makes it uniquely positioned to meet business’ emerging needs.",[47,2214,2215],{},"David Kjerrumgaard’s book \"Apache Pulsar in Action\" is your ultimate guide to getting started with Apache Pulsar. Join David for this virtual book launch to discover why companies are adopting Pulsar over traditional messaging systems and how Pulsar can benefit your organization.",[47,2217,647],{},{"title":17,"searchDepth":18,"depth":18,"links":2219},[],"01:05:09","\u002Fimgs\u002Fwebinars\u002F63aac930a125273371b005da_OG_webinar-_Apache%20Pulsar%20in%20Action_%20by%20David%20Kjerrumgaard%20-%20Book%20Launch.webp",{},"\u002Fwebinars\u002Fapache-pulsar-in-action-by-david-kjerrumgaard-book-launch",{"title":2204,"description":2209},[27],"webinars\u002Fapache-pulsar-in-action-by-david-kjerrumgaard-book-launch",[472,1482],"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=NDaahGOI7NQ&list=PLqRma1oIkcWhfmUuJrMM5YIG8hjju62Ev&index=4?autoplay=1","wDqaIWFdpOG-EPzPIoc9OTGljPVTlOaGEvKCEQJ-8w0",{"id":2231,"title":2232,"body":2233,"createdAt":10,"date":2087,"description":2237,"duration":2243,"extension":8,"featured":292,"formTitle":10,"hubspotId":2090,"image":2244,"isDraft":292,"isUpcoming":292,"link":10,"meta":2245,"navigation":7,"order":294,"path":2246,"readingTime":10,"relatedResources":10,"seo":2247,"speakers":2248,"startDate":10,"stem":2249,"submitText":10,"successDescription":10,"successTitle":10,"tags":2250,"time":10,"videoUrl":2251,"__hash__":2252,"resourceType":345},"webinars\u002Fwebinars\u002Fbuild-an-event-driven-architecture-with-pulsar-01-13-22.md","Build an Event-Driven Architecture with Pulsar - 01\u002F13\u002F22",{"type":14,"value":2234,"toc":2241},[2235,2238],[47,2236,2237],{},"Learn how Apache Pulsar’s cloud-native capabilities, scalability, and reliability as a messaging and event-streaming platform are driving its popularity and adoption for companies moving to microservices and the cloud.",[47,2239,2240],{},"You will learn how to build Pulsar applications and how to produce and consume Pulsar messages with Python, Java and Golang.",{"title":17,"searchDepth":18,"depth":18,"links":2242},[],"01:24:57","\u002Fimgs\u002Fwebinars\u002F63aacad2141e18e819fe145c_OG_webinar-Build%20an%20Event-Driven%20Architecture%20with%20Pulsar.webp",{},"\u002Fwebinars\u002Fbuild-an-event-driven-architecture-with-pulsar-01-13-22",{"title":2232,"description":2237},[1120],"webinars\u002Fbuild-an-event-driven-architecture-with-pulsar-01-13-22",[472,1482],"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=mg7RLwV3x1M&list=PLqRma1oIkcWhfmUuJrMM5YIG8hjju62Ev&index=3?autoplay=1","fDLTraJhC4824a0ptX4UX7E1umHUraik_QttWhG5iiY",{"id":2254,"title":2255,"body":2256,"createdAt":10,"date":2087,"description":2286,"duration":2287,"extension":8,"featured":292,"formTitle":10,"hubspotId":2090,"image":2288,"isDraft":292,"isUpcoming":292,"link":10,"meta":2289,"navigation":7,"order":294,"path":2290,"readingTime":10,"relatedResources":10,"seo":2291,"speakers":2292,"startDate":10,"stem":2294,"submitText":10,"successDescription":10,"successTitle":10,"tags":2295,"time":10,"videoUrl":2296,"__hash__":2297,"resourceType":345},"webinars\u002Fwebinars\u002Fdeveloping-event-driven-microservices-using-apache-pulsar-part-1.md","Developing Event-driven Microservices using Apache Pulsar - Part 1",{"type":14,"value":2257,"toc":2284},[2258,2261,2275,2278,2281],[47,2259,2260],{},"Part 1 of a 3-part series,  this webinar explores how to use Apache Pulsar’s serverless computing framework, known as Pulsar Functions, for microservice development. Using a realistic example of a food delivery service, you will learn how to:",[359,2262,2263,2266,2269,2272],{},[362,2264,2265],{},"Develop microservices using Pulsar Functions",[362,2267,2268],{},"Define and use language-neutral schemas for inter-service communication",[362,2270,2271],{},"Deploy the microservices to Pulsar",[362,2273,2274],{},"Monitor the performance of the microservice and dynamically scale it based on data volumes",[47,2276,2277],{},"Instructors:",[47,2279,2280],{},"David Kjerrumgaard - David is a Developer Advocate for StreamNative with a focus on helping developers solve their streaming data challenges using Apache Pulsar. He has over 15 years of experience working with open source projects in the Big Data, Stream Processing, and Distributed Computing spaces. David is the author of the book Pulsar in Action.",[47,2282,2283],{},"Ioannis Polyzos  Ioannis is a Solutions Engineer at StreamNative with a focus on data intensive systems. He helps companies modernize their data systems by leveraging Big Data technologies. He is passionate about fast & scalable data pipelines, streaming data flows and Machine Learning systems.",{"title":17,"searchDepth":18,"depth":18,"links":2285},[],"Part 1 of a 3-part series, this webinar explores how to use Apache Pulsar’s serverless computing framework, known as Pulsar Functions, for microservice development","01:00:27","\u002Fimgs\u002Fwebinars\u002F63aac2b2e74980ea9421c613_OG_webinar-Developing%20Event-driven%20Microservices%20using%20Apache%20Pulsar.webp",{},"\u002Fwebinars\u002Fdeveloping-event-driven-microservices-using-apache-pulsar-part-1",{"title":2255,"description":2286},[27,2293],"Ioannis Polyzos","webinars\u002Fdeveloping-event-driven-microservices-using-apache-pulsar-part-1",[472,1250,1036,1482],"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=hI1Y_qMilsA&list=PLqRma1oIkcWhfmUuJrMM5YIG8hjju62Ev&index=8?autoplay=1","9G_XSZwj4_9rwUo4U6yKRoKTgmIaIoNq1E1O7lTvYH0",{"id":2299,"title":2300,"body":2301,"createdAt":10,"date":2087,"description":2318,"duration":2319,"extension":8,"featured":292,"formTitle":10,"hubspotId":2090,"image":2320,"isDraft":292,"isUpcoming":292,"link":10,"meta":2321,"navigation":7,"order":294,"path":2322,"readingTime":10,"relatedResources":10,"seo":2323,"speakers":2324,"startDate":10,"stem":2325,"submitText":10,"successDescription":10,"successTitle":10,"tags":2326,"time":10,"videoUrl":2327,"__hash__":2328,"resourceType":345},"webinars\u002Fwebinars\u002Fdeveloping-event-driven-microservices-using-apache-pulsar-part-2.md","Developing Event-driven Microservices using Apache Pulsar - Part 2",{"type":14,"value":2302,"toc":2316},[2303,2306],[47,2304,2305],{},"Part 2 of a 3-part series,  this webinar explores how to use Apache Pulsar’s serverless computing framework, known as Pulsar Functions, for microservice development. Using a realistic example of a food delivery service, you will learn how to:",[359,2307,2308,2310,2312,2314],{},[362,2309,2265],{},[362,2311,2268],{},[362,2313,2271],{},[362,2315,2274],{},{"title":17,"searchDepth":18,"depth":18,"links":2317},[],"Part 2 of a 3-part series, this webinar explores how to use Apache Pulsar’s serverless computing framework, known as Pulsar Functions, for microservice development.","57:46","\u002Fimgs\u002Fwebinars\u002F63aac5b5e749801073242fd3_OG_webinar-Developing%20Event-driven%20Microservices%20using%20Apache%20Pulsar-Part%202.webp",{},"\u002Fwebinars\u002Fdeveloping-event-driven-microservices-using-apache-pulsar-part-2",{"title":2300,"description":2318},[27,2293],"webinars\u002Fdeveloping-event-driven-microservices-using-apache-pulsar-part-2",[472,1250,1036,1482],"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=bgyP3m8QmuI&list=PLqRma1oIkcWhfmUuJrMM5YIG8hjju62Ev&index=7?autoplay=1","xiq476tBS0019zohKMIHX2qcezw6B6qoygov5vXc-pg",{"id":2330,"title":2331,"body":2332,"createdAt":10,"date":2087,"description":2350,"duration":2351,"extension":8,"featured":292,"formTitle":10,"hubspotId":2090,"image":2352,"isDraft":292,"isUpcoming":292,"link":10,"meta":2353,"navigation":7,"order":294,"path":2354,"readingTime":10,"relatedResources":10,"seo":2355,"speakers":2356,"startDate":10,"stem":2358,"submitText":10,"successDescription":10,"successTitle":10,"tags":2359,"time":10,"videoUrl":2360,"__hash__":2361,"resourceType":345},"webinars\u002Fwebinars\u002Fdeveloping-event-driven-microservices-using-apache-pulsar-part-3.md","Developing Event-driven Microservices using Apache Pulsar - Part 3",{"type":14,"value":2333,"toc":2348},[2334,2337],[47,2335,2336],{},"Part of a 3-part series,  this webinar discusses Streaming Analytics Using FlinkSQL and covers the following topics:",[359,2338,2339,2342,2345],{},[362,2340,2341],{},"Why organizations are leveraging SQL as the unifying language for data",[362,2343,2344],{},"Basics of Apache Flink and how it provides an API for handling events and intermediate state and periodic snapshots to ensure consistency",[362,2346,2347],{},"How to run Apache Flink SQL queries against Apache Pulsar topics",{"title":17,"searchDepth":18,"depth":18,"links":2349},[],"Part of a 3-part series, this webinar discusses Streaming Analytics Using FlinkSQL","56:26","\u002Fimgs\u002Fwebinars\u002F63aac6215abd306932f6a9ae_OG_webinar-Developing%20Event-driven%20Microservices%20using%20Apache%20Pulsar-Part%203.webp",{},"\u002Fwebinars\u002Fdeveloping-event-driven-microservices-using-apache-pulsar-part-3",{"title":2331,"description":2350},[2357,1120],"Addison Higham","webinars\u002Fdeveloping-event-driven-microservices-using-apache-pulsar-part-3",[472,1658,1482,301],"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=VPN7bCEezgE&list=PLqRma1oIkcWhfmUuJrMM5YIG8hjju62Ev&index=6?autoplay=1","uei7cvxvlNYdiZotVq3WX6_DSKJ-HTqwZr2QBPCfmBg",{"id":2363,"title":2364,"body":2365,"createdAt":10,"date":2087,"description":2372,"duration":2373,"extension":8,"featured":292,"formTitle":10,"hubspotId":2090,"image":2374,"isDraft":292,"isUpcoming":292,"link":10,"meta":2375,"navigation":7,"order":294,"path":2376,"readingTime":10,"relatedResources":10,"seo":2377,"speakers":2378,"startDate":10,"stem":2380,"submitText":10,"successDescription":10,"successTitle":10,"tags":2381,"time":10,"videoUrl":2382,"__hash__":2383,"resourceType":345},"webinars\u002Fwebinars\u002Fhow-splunk-built-real-time-stream-processing-engine-apache-pulsar-x-flink.md","How Splunk Built a Real-Time Stream Processing Engine with Apache Pulsar x Flink",{"type":14,"value":2366,"toc":2370},[2367],[47,2368,2369],{},"This is a virtual Bay Area Meetup co-hosted by Splunk and StreamNative. Jerry Peng from Splunk will talk about how Splunk uses Apache Pulsar and Flink in the Splunk® Data Stream Processor (DSP), which is widely used across IT, security, IOT, and business analytics use cases.",{"title":17,"searchDepth":18,"depth":18,"links":2371},[],"Virtual Bay Area Meetup co-hosted by Splunk and StreamNative. Jerry Peng from Splunk will talk about how Splunk uses Apache Pulsar and Flink in the Splunk","57:49","\u002Fimgs\u002Fwebinars\u002F63aac10fe74980457c1eff6b_OG_webinar-How%20Splunk%20Built%20a%20Real-Time%20Stream%20Processing%20Engine%20with%20Apache%20Pulsar%20x%20Flink.webp",{},"\u002Fwebinars\u002Fhow-splunk-built-real-time-stream-processing-engine-apache-pulsar-x-flink",{"title":2364,"description":2372},[2379],"Boyang Jerry Peng","webinars\u002Fhow-splunk-built-real-time-stream-processing-engine-apache-pulsar-x-flink",[472,1658,907,301],"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=K2PlapAM0YE&list=PLqRma1oIkcWhfmUuJrMM5YIG8hjju62Ev&index=9?autoplay=1","csQ2hsUaTk7Rr-G34pziYrePnYIq9Lp0x7auWcos0FM",{"id":2385,"title":2386,"body":2387,"createdAt":10,"date":2087,"description":2421,"duration":2422,"extension":8,"featured":292,"formTitle":10,"hubspotId":2090,"image":2423,"isDraft":292,"isUpcoming":292,"link":10,"meta":2424,"navigation":7,"order":294,"path":2425,"readingTime":10,"relatedResources":10,"seo":2426,"speakers":2427,"startDate":10,"stem":2428,"submitText":10,"successDescription":10,"successTitle":10,"tags":2429,"time":10,"videoUrl":2430,"__hash__":2431,"resourceType":345},"webinars\u002Fwebinars\u002Fhow-to-build-an-event-sourcing-app-with-apache-pulsar.md","How to Build an Event Sourcing App with Apache Pulsar",{"type":14,"value":2388,"toc":2419},[2389,2392,2403,2406,2408],[47,2390,2391],{},"Event Sourcing is a powerful pattern that maintains an immutable sequence of events that multiple consumers can access. Today, it is used by some of the largest companies in the world to rapidly scale, iterate, and evolve their systems. The key benefits for software architects include:",[923,2393,2394,2397,2400],{},[362,2395,2396],{},"Auditing: Events are immutable and store the full history of the state of the system, providing a detailed audit trail of all system activity.",[362,2398,2399],{},"Time Travel: By storing events, you have the ability to restore the state of the system to any previous point in time.",[362,2401,2402],{},"Service autonomy: If an event-consuming service goes down it can quickly “catch up” when it recovers by consuming the events that occurred while it was offline.",[47,2404,2405],{},"Common challenges in building an Event Sourcing app to scale are handling schema change, retaining messages indefinitely, and supporting multiple consumers. Join StreamNative’s Developer Advocate, David Kjerrmugaard, to learn how to use Apache Pulsar to build event sourcing applications, common use cases, and how Apache Pulsar’s persistent storage of event streams can simplify this process.",[47,2407,1005],{},[923,2409,2410,2413,2416],{},[362,2411,2412],{},"What event sourcing is and common uses cases",[362,2414,2415],{},"Requirements for event sourcing - Number of topics, tiered storage, and etc.",[362,2417,2418],{},"How to implement an event sourcing pattern using Apache Pulsar",{"title":17,"searchDepth":18,"depth":18,"links":2420},[],"Common challenges in building an Event Sourcing app to scale are handling schema change, retaining messages indefinitely, and supporting multiple consumers.","01:07:51","\u002Fimgs\u002Fwebinars\u002F63aacc8ad28a446dbd639f64_OG_webinar-How%20to%20Build%20an%20Event%20Sourcing%20App%20with%20Apache%20Pulsar.webp",{},"\u002Fwebinars\u002Fhow-to-build-an-event-sourcing-app-with-apache-pulsar",{"title":2386,"description":2421},[27],"webinars\u002Fhow-to-build-an-event-sourcing-app-with-apache-pulsar",[472,1482],"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=wLjEGSqGWJg&list=PLqRma1oIkcWhfmUuJrMM5YIG8hjju62Ev&index=1?autoplay=1","5GHWcz7PJeibbVj-ujfXKJfhDn1gFgyMnZ8r8YjQUD4",{"id":2433,"title":2434,"body":2435,"createdAt":10,"date":2087,"description":2442,"duration":2443,"extension":8,"featured":292,"formTitle":10,"hubspotId":2090,"image":2444,"isDraft":292,"isUpcoming":292,"link":10,"meta":2445,"navigation":7,"order":294,"path":2446,"readingTime":10,"relatedResources":10,"seo":2447,"speakers":2448,"startDate":10,"stem":2450,"submitText":10,"successDescription":10,"successTitle":10,"tags":2451,"time":10,"videoUrl":2452,"__hash__":2453,"resourceType":345},"webinars\u002Fwebinars\u002Fpulsar-and-nifi-for-cloud-data-lakes-03-09-22.md","Pulsar and Nifi for Cloud Data Lakes 03\u002F09\u002F22",{"type":14,"value":2436,"toc":2440},[2437],[47,2438,2439],{},"Timothy Spann, Developer Advocate at StreamNative and John Kuchmek, Principal Solutions Engineer at Cloudera present this webinar about the newly released open source integration between Apache Pulsar and Apache NiFi. The two together create a cloud native, scalable, real-time streaming data platform that can ingest, transform, and analyze massive amounts of data.",{"title":17,"searchDepth":18,"depth":18,"links":2441},[],"Timothy Spann, Developer Advocate at StreamNative and John Kuchmek, Principal Solutions Engineer at Cloudera present this webinar about the newly released open source integration between Apache Pulsar and Apache NiFi","01:29:50","\u002Fimgs\u002Fwebinars\u002F63aacbe903d343a8db13f517_OG_webinar-Pulsar%20and%20Nifi%20for%20Cloud%20Data%20Lakes.webp",{},"\u002Fwebinars\u002Fpulsar-and-nifi-for-cloud-data-lakes-03-09-22",{"title":2434,"description":2442},[1120,2449],"John Kuchmek","webinars\u002Fpulsar-and-nifi-for-cloud-data-lakes-03-09-22",[472,301],"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2BeAQNI8Pu0&list=PLqRma1oIkcWhfmUuJrMM5YIG8hjju62Ev&index=2?autoplay=1","VYtfCKx_GdZqn1UhLURyrUrTl-RepTFSjdeWVkmL28E",{"id":2455,"title":2456,"body":2457,"createdAt":10,"date":2087,"description":2481,"duration":2482,"extension":8,"featured":292,"formTitle":10,"hubspotId":2090,"image":2483,"isDraft":292,"isUpcoming":292,"link":10,"meta":2484,"navigation":7,"order":294,"path":2485,"readingTime":10,"relatedResources":10,"seo":2486,"speakers":2487,"startDate":10,"stem":2488,"submitText":10,"successDescription":10,"successTitle":10,"tags":2489,"time":10,"videoUrl":2491,"__hash__":2492,"resourceType":345},"webinars\u002Fwebinars\u002Fstreamnative-cloud-workshop.md","StreamNative Cloud Workshop - 11\u002F3\u002F21",{"type":14,"value":2458,"toc":2479},[2459,2462,2476],[47,2460,2461],{},"In this workshop, David Kjerrumgaard dives into different use cases to show how businesses are using StreamNative Cloud to accelerate their time-to-value with Pulsar. 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This webinar will help engineers and architects better understand common real-world use cases for Pulsar and learn about helpful patterns that simplify solutions to some of the most common pain points with Pulsar.",[47,2541,2542],{},"In this presentation, we will cover:",[359,2544,2545,2548],{},[362,2546,2547],{},"High-Performance Streaming Enrichment with Apache Ignite. By combining Apache Pulsar Functions with Apache Ignite, we achieve low latency lookup performance for real-time enrichment of data, which is useful for search and other real-time use cases.",[362,2549,2550],{},"Message-Based Distributed Tracing with Apache Flink and Jaeger. We will look at how to solve this common challenge with Pulsar Function architectures by using a message-based approach to tracing. This will allow us to gain insight into the flow of a message from a source all the way to the sink, greatly accelerating development productivity.",[47,2552,2553],{},"By attending this webinar, you can expect to come away with:",[359,2555,2556,2559],{},[362,2557,2558],{},"Best practices and use cases for high-performance streaming enrichment",[362,2560,2561],{},"How to implement distributed tracing to gain visibility into Pulsar function flows",[434,2563,2564],{},[47,2565,2566],{},[54,2567,2570],{"href":2568,"rel":2569},"https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCywxUI5HlIyc0VEKYR4X9Pg",[263],"Subscribe to our Youtube Channel",{"title":17,"searchDepth":18,"depth":18,"links":2572},[],"2022-12-23","01:09:02","\u002Fimgs\u002Fwebinars\u002F63a59ed9b8064600f690d1b6_OG_webinar-Common%20architectural%20patterns%20on%20Apache%20Pulsar.webp",{},"\u002Fwebinars\u002Fcommon-architectural-patterns-on-apache-pulsar",{"title":2534,"description":2534},[2580],"Devin Bost","webinars\u002Fcommon-architectural-patterns-on-apache-pulsar",[472,1250],"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=pmaCG1SHAW8&list=PLqRma1oIkcWhfmUuJrMM5YIG8hjju62Ev&index=18?autoplay=1","96K6lRs3uELTHd24c2-vcsnHhoCIBkLtkGeqXycJ_bM",{"id":2586,"title":2587,"body":2588,"createdAt":10,"date":2573,"description":2600,"duration":2601,"extension":8,"featured":292,"formTitle":10,"hubspotId":2090,"image":2602,"isDraft":292,"isUpcoming":292,"link":10,"meta":2603,"navigation":7,"order":294,"path":2604,"readingTime":10,"relatedResources":10,"seo":2605,"speakers":2606,"startDate":10,"stem":2608,"submitText":10,"successDescription":10,"successTitle":10,"tags":2609,"time":10,"videoUrl":2610,"__hash__":2611,"resourceType":345},"webinars\u002Fwebinars\u002Fhow-to-operate-pulsar-in-production.md","How to Operate Pulsar in Production",{"type":14,"value":2589,"toc":2598},[2590],[434,2591,2592],{},[47,2593,2594],{},[54,2595,2597],{"href":2568,"rel":2596},[263],"Subscribe to our YouTube Channel",{"title":17,"searchDepth":18,"depth":18,"links":2599},[],"StreamNative Webinar - How to Operate Pulsar in Production","01:26:35","\u002Fimgs\u002Fwebinars\u002F63a5a1346ac3009373b5c4c0_OG_webinar-How%20to%20Operate%20Pulsar%20in%20Production.webp",{},"\u002Fwebinars\u002Fhow-to-operate-pulsar-in-production",{"title":2587,"description":2600},[2357,812,2607],"Joe Francis","webinars\u002Fhow-to-operate-pulsar-in-production",[472],"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=mncXc_T6JkU&list=PLqRma1oIkcWhfmUuJrMM5YIG8hjju62Ev&index=16?autoplay=1","Oz2BIqUZnmM9hu827_y6LRCO6u6eDZoxyFCC6OfBgok",{"id":2613,"title":2614,"body":2615,"createdAt":10,"date":2573,"description":2642,"duration":2643,"extension":8,"featured":292,"formTitle":10,"hubspotId":2090,"image":2644,"isDraft":292,"isUpcoming":292,"link":10,"meta":2645,"navigation":7,"order":294,"path":2646,"readingTime":10,"relatedResources":10,"seo":2647,"speakers":2648,"startDate":10,"stem":2650,"submitText":10,"successDescription":10,"successTitle":10,"tags":2651,"time":10,"videoUrl":2652,"__hash__":2653,"resourceType":345},"webinars\u002Fwebinars\u002Fimplementing-opentelemetry-with-apache-pulsar.md","Implementing OpenTelemetry with Apache Pulsar",{"type":14,"value":2616,"toc":2640},[2617,2620,2623,2626,2629],[47,2618,2619],{},"Distributed stream processing applications are a popular way for developers to implement real-time driven solutions and Apache Pulsar is quickly emerging as one of the most popular technologies to do this. 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It will explain, in detail, the architecture of OpenTelemetry deployments and how to instrument Java applications using Apache Pulsar to emit traces compatible with OpenTelemetry specification. 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Pulsar",{"type":14,"value":2658,"toc":2687},[2659,2661,2670,2679],[47,2660,2656],{},[434,2662,2663],{},[47,2664,2665],{},[54,2666,2669],{"href":2667,"rel":2668},"https:\u002F\u002Fwww.youtube.com\u002Fredirect?event=video_description&redir_token=QUFFLUhqbHlQREs2cDVyeml3VGlrY3JXM3BybmdkUTJlUXxBQ3Jtc0trZ1hDUkZud3VZdE5DX0d2UzFPRnFqWW9Nd21Fb0JJVnlSLXp5cnFfYmNKWm5JVGc3Q3oycUVBU2VEakwwVi1TR2xhVUUtUjA5XzNqZjNOOUxraVdwS0ZJZGNXYzRSSnVwUk1sUkl5LXlpdi1kdlExUQ&q=https%3A%2F%2Fwww.slideshare.net%2Fstreamnative%2Fintroducing-kafkaonpulsar-bring-native-kafka-protocol-support-to-apache-pulsar&v=gL6hzRtij8M",[263],"Slides",[434,2671,2672],{},[47,2673,2674],{},[54,2675,2678],{"href":2676,"rel":2677},"https:\u002F\u002Fwww.youtube.com\u002Fredirect?event=video_description&redir_token=QUFFLUhqbW5GUmhDcGdJekptbFlNUG9qLW1FaWpZb0s4QXxBQ3Jtc0ttUHQxenN2MmJfWWFPd0FVZGxyZjJMNU9zTF9KNHp5aVFTbEM0U25pT2F6alowZTdCVU40MURzM3RCeXRHV0NCTEFhRFFqanhtTHdySWJSWXBuTnpkWDJKWjNwcG5rTmI2NHVfNDNzUVpLSWNVRTgtRQ&q=https%3A%2F%2Fhackmd.io%2FnLj5M9BEQIacKcZsNrDxmQ&v=gL6hzRtij8M",[263],"Demo notes",[434,2680,2681],{},[47,2682,2683],{},[54,2684,2686],{"href":2568,"rel":2685},[263],"Subscribe",{"title":17,"searchDepth":18,"depth":18,"links":2688},[],"01:11:01","\u002Fimgs\u002Fwebinars\u002F63a59ea597ca89788a7158d8_OG_webinar-Introducing%20Kafka-on-Pulsar.webp",{},"\u002Fwebinars\u002Fintroducing-kafka-on-pulsar-bring-native-kafka-protocol-support-to-apache-pulsar",{"title":2656,"description":2656},"webinars\u002Fintroducing-kafka-on-pulsar-bring-native-kafka-protocol-support-to-apache-pulsar",[685,472],"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=gL6hzRtij8M&list=PLqRma1oIkcWhfmUuJrMM5YIG8hjju62Ev&index=19?autoplay=1","rehbCgqkCZXjcVf6i8rmRuaVxFLwxMkps7nEjgNNtUg",{"id":2699,"title":2700,"body":2701,"createdAt":10,"date":2573,"description":2721,"duration":2722,"extension":8,"featured":292,"formTitle":10,"hubspotId":2090,"image":2723,"isDraft":292,"isUpcoming":292,"link":10,"meta":2724,"navigation":7,"order":294,"path":2725,"readingTime":10,"relatedResources":10,"seo":2726,"speakers":2727,"startDate":10,"stem":2729,"submitText":10,"successDescription":10,"successTitle":10,"tags":2730,"time":10,"videoUrl":2731,"__hash__":2732,"resourceType":345},"webinars\u002Fwebinars\u002Flessons-from-managing-a-pulsar-cluster-by-nutanix.md","Lessons 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Nutanix","01:05:30","\u002Fimgs\u002Fwebinars\u002F63a59f3960075d23d546447c_OG_webinar-Lessons%20From%20Managing%20A%20Pulsar%20Cluster%20by%20Nutanix.webp",{},"\u002Fwebinars\u002Flessons-from-managing-a-pulsar-cluster-by-nutanix",{"title":2700,"description":2721},[2728],"Shivji Jha","webinars\u002Flessons-from-managing-a-pulsar-cluster-by-nutanix",[472],"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=zAHxgG_U67Q&list=PLqRma1oIkcWhfmUuJrMM5YIG8hjju62Ev&index=17?autoplay=1","LmQPsftRMyRzFJOPCwQMw1L3VgKEJYXk8_SC8Da0RcE",{"id":2734,"title":2735,"body":2736,"createdAt":10,"date":2573,"description":2747,"duration":2748,"extension":8,"featured":292,"formTitle":10,"hubspotId":2090,"image":2749,"isDraft":292,"isUpcoming":292,"link":10,"meta":2750,"navigation":7,"order":294,"path":2751,"readingTime":10,"relatedResources":10,"seo":2752,"speakers":2753,"startDate":10,"stem":2755,"submitText":10,"successDescription":10,"successTitle":10,"tags":10,"time":10,"videoUrl":2756,"__hash__":2757,"resourceType":345},"webinars\u002Fwebinars\u002Flow-latency-stream-processing-with-jet.md","Low-Latency Stream Processing with Jet",{"type":14,"value":2737,"toc":2745},[2738],[434,2739,2740],{},[47,2741,2742],{},[54,2743,2597],{"href":2568,"rel":2744},[263],{"title":17,"searchDepth":18,"depth":18,"links":2746},[],"StreamNative Webinar - Low-Latency Stream Processing with Jet","43:50","\u002Fimgs\u002Fwebinars\u002F63a5a3400f968bc2ccf5fc79_OG_webinar-Low-Latency%20Stream%20Processing%20with%20Jet.webp",{},"\u002Fwebinars\u002Flow-latency-stream-processing-with-jet",{"title":2735,"description":2747},[2754],"Can Gencer","webinars\u002Flow-latency-stream-processing-with-jet","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=wIJGusBxB70&list=PLqRma1oIkcWhfmUuJrMM5YIG8hjju62Ev&index=14?autoplay=1","x6ff8b08TBLRzfYfkKKAWI7sTwgwhrnwzh3gZAJRgLM",{"id":2759,"title":2760,"body":2761,"createdAt":10,"date":2573,"description":2781,"duration":2782,"extension":8,"featured":292,"formTitle":10,"hubspotId":2090,"image":2783,"isDraft":292,"isUpcoming":292,"link":10,"meta":2784,"navigation":7,"order":294,"path":2785,"readingTime":10,"relatedResources":10,"seo":2786,"speakers":2787,"startDate":10,"stem":2789,"submitText":10,"successDescription":10,"successTitle":10,"tags":2790,"time":10,"videoUrl":2791,"__hash__":2792,"resourceType":345},"webinars\u002Fwebinars\u002Fmanaging-real-time-data-teams.md","Managing Real-Time Data 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existing message system which was based on Kafka. Rapidly increasing numbers of users, topics, and messages had led to mounting costs associated with storage, processing, labor, and time.",[47,2804,2805],{},"To improve system performance, Tuya required a more flexible message delivery system that could meet their need for persistence. They also needed a solution that would make it easy to classify messages by user.",[47,2807,2808],{},"After evaluating several different options, Tuya settled on Apache Pulsar because it proved to be the most adept at handling the accumulation of messages and repeated consumption. The addition of Pulsar has made Tuya’s message system much more efficient, resulting in lower operational and maintenance costs.",[47,2810,647],{},{"title":17,"searchDepth":18,"depth":18,"links":2812},[],"https:\u002F\u002F6585952.fs1.hubspotusercontent-na1.net\u002Fhubfs\u002F6585952\u002FWhitepapers\u002FSAVE%20%5Bwhitepaper%5D%20apache-pulsar-helps-streamline-message-system-and-reduces-O%26M-costs-at-tuya-smart.pdf","df62ff66-c287-4b5f-a2b6-869471385fdc","\u002Fimgs\u002Fwhitepapers\u002F63aeca68ef34793d08c93534_open-graph-Apache-Pulsar-Helps-Streamline-Message-System.webp",{},"\u002Fwhitepapers\u002Fapache-pulsar-helps-streamline-message-system-and-reduces-o-m-costs-at-tuya-smart",{"title":2795,"description":2795},"whitepapers\u002Fapache-pulsar-helps-streamline-message-system-and-reduces-o-m-costs-at-tuya-smart",[685,472,990],"JMKS4Qbd-zilgUOoFQr4PzsWGK90Jg9CaGxEZT4gKEQ",{"id":2823,"title":2824,"authors":2825,"body":2827,"createdAt":10,"date":2573,"description":2824,"downloadLink":10,"extension":8,"featured":292,"formTitle":10,"hubspotId":2837,"image":2838,"isDraft":292,"link":10,"meta":2839,"navigation":7,"order":294,"path":2840,"readingTime":10,"relatedResources":10,"seo":2841,"stem":2842,"submitText":10,"successDescription":10,"successTitle":10,"tags":2843,"__hash__":2844,"resourceType":776},"whitepapers\u002Fwhitepapers\u002Fapache-pulsar-helps-tencent-process-financial-transactions.md","Apache Pulsar Helps Tencent Process Tens of Billions of Financial Transactions Efficiently with Virtually No Data Loss",[2826],"Dezhi Liu",{"type":14,"value":2828,"toc":2835},[2829,2832],[47,2830,2831],{},"As the largest provider of Internet products and services in China, Tencent serves billions of users and over a million merchants—and these numbers are growing fast! Tencent’s enterprises generate a huge volume of financial transactions, placing a tremendous load on their billing service, which processes hundreds of millions of dollars in revenue each day.",[47,2833,2834],{},"Because Tencent had been unable to scale its current billing service to handle their rapidly growing business, the possibility of data loss had become an escalating concern. To ensure data consistency, the company decided to redesign their system’s transaction processing pipeline. After evaluating the pros and cons of several messaging systems, Tencent chose to implement it using Apache Pulsar. As a result, Tencent can now run their billing service on a very large scale with virtually no data loss.",{"title":17,"searchDepth":18,"depth":18,"links":2836},[],"a17bf1f9-13f5-405a-ae15-9cbe0646f7c5","\u002Fimgs\u002Fwhitepapers\u002F63aecf471d9c96089c87bff2_open-graph-wp-pulsar-tencent-billing.jpg",{},"\u002Fwhitepapers\u002Fapache-pulsar-helps-tencent-process-financial-transactions",{"title":2824,"description":2824},"whitepapers\u002Fapache-pulsar-helps-tencent-process-financial-transactions",[472,2529],"OHdJirTpAIjakFyovnCWRNO925ia3yZHyG2zMK3Azvs",{"id":2846,"title":2847,"authors":2848,"body":2850,"createdAt":10,"date":2573,"description":2847,"downloadLink":10,"extension":8,"featured":292,"formTitle":10,"hubspotId":2863,"image":2864,"isDraft":292,"link":10,"meta":2865,"navigation":7,"order":294,"path":2866,"readingTime":10,"relatedResources":10,"seo":2867,"stem":2868,"submitText":10,"successDescription":10,"successTitle":10,"tags":2869,"__hash__":2870,"resourceType":776},"whitepapers\u002Fwhitepapers\u002Fapplying-apache-pulsar-sql-to-zhaopins-search-log-analysis-significantly-improved-search-and-query-efficiency.md","Applying Apache Pulsar SQL to Zhaopin's Search Log Analysis Significantly Improved Search and Query Efficiency",[2849],"Ran Gao",{"type":14,"value":2851,"toc":2861},[2852,2855,2858],[47,2853,2854],{},"As one of the largest online recruiting and career platforms in China, Zhaopin has 140 million active users and 4 million cooperative companies. Zhaopin provides a high-quality user experience in which job search services play a critical role. Zhaopin’s job search services support a large number of user search requests every day and generate large-scale search logs. These logs are extremely valuable and can be used for troubleshooting or Big Data analysis.",[47,2856,2857],{},"In an effort to improve their job search service and user experience, Zhaopin added Pulsar SQL to their existing Pulsar implementation. Pulsar SQL gives Zhaopin the ability to refine queries by using certain parameters that limit the amount of data that needs to be searched. Pulsar SQL also provides various reports that the company uses to analyze and aggregate data. This article covers some typical usage scenarios at Zhaopin and explains how Pulsar SQL enables the company to query streaming data more efficiently.",[47,2859,2860],{},"Zhaopin is constantly exploring new ways to use their search logs effectively and Pulsar continues to provide inspiration for the company’s efforts.",{"title":17,"searchDepth":18,"depth":18,"links":2862},[],"952b6d63-4875-4134-964a-b768702dc011","\u002Fimgs\u002Fwhitepapers\u002F63aecb8fe18937a9ddf5c624_open-graph-why-zhaopin-chooses-pulsar.webp",{},"\u002Fwhitepapers\u002Fapplying-apache-pulsar-sql-to-zhaopins-search-log-analysis-significantly-improved-search-and-query-efficiency",{"title":2847,"description":2847},"whitepapers\u002Fapplying-apache-pulsar-sql-to-zhaopins-search-log-analysis-significantly-improved-search-and-query-efficiency",[472],"qojhH6RLFFV_M6fJwi0rCOEGhUIqhkc56rW1pJ8V3sw",{"id":2872,"title":2873,"authors":2874,"body":2875,"createdAt":10,"date":2573,"description":2873,"downloadLink":2885,"extension":8,"featured":292,"formTitle":10,"hubspotId":2886,"image":2887,"isDraft":292,"link":10,"meta":2888,"navigation":7,"order":294,"path":2889,"readingTime":10,"relatedResources":10,"seo":2890,"stem":2891,"submitText":10,"successDescription":10,"successTitle":10,"tags":2892,"__hash__":2893,"resourceType":776},"whitepapers\u002Fwhitepapers\u002Fbenchmarking-pulsar-vs-kafka.md","Benchmarking Pulsar and Kafka: A More Accurate Perspective on Pulsar's Performance",[847,813],{"type":14,"value":2876,"toc":2883},[2877,2880],[47,2878,2879],{},"Today, many companies are looking at real-time data streaming applications to develop new products and services. Organizations must first understand the advantages and differentiators of the different event streaming systems before they can select the technology best-suited to meet their business needs.",[47,2881,2882],{},"Benchmarks are one method organizations use to compare and measure the performance of different technologies. In order for these benchmarks to be meaningful, they must be done correctly and provide accurate information. Unfortunately, it is all too easy for benchmarks to fail to provide accurate insights due to any number of issues.",{"title":17,"searchDepth":18,"depth":18,"links":2884},[],"https:\u002F\u002F6585952.fs1.hubspotusercontent-na1.net\u002Fhubfs\u002F6585952\u002FBlog%20PDF%20-%20Benchmarking%20Pulsar%20and%20Kafka%20-%20Social%20-%202021.pdf","85a5dd10-940c-4a78-9bad-c1c8f3721943","\u002Fimgs\u002Fwhitepapers\u002F63aecd39801cf457af016505_open-graph-benchmark-pulsar-vs-kafka.webp",{},"\u002Fwhitepapers\u002Fbenchmarking-pulsar-vs-kafka",{"title":2873,"description":2873},"whitepapers\u002Fbenchmarking-pulsar-vs-kafka",[685,472,1749,301],"N09irarY73P4Vq1FxCNgzS4tzdZOfGf65sVBCaJ1V-w",{"id":2895,"title":2896,"authors":2897,"body":2899,"createdAt":10,"date":2573,"description":2896,"downloadLink":10,"extension":8,"featured":292,"formTitle":10,"hubspotId":2939,"image":2940,"isDraft":292,"link":10,"meta":2941,"navigation":7,"order":294,"path":2942,"readingTime":10,"relatedResources":10,"seo":2943,"stem":2944,"submitText":10,"successDescription":10,"successTitle":10,"tags":2945,"__hash__":2946,"resourceType":776},"whitepapers\u002Fwhitepapers\u002Fhow-apache-pulsar-helping-scale-customer-engagement-platform.md","How Apache Pulsar is Helping Iterable Scale its Customer Engagement Platform",[2898],"Greg Methvin",{"type":14,"value":2900,"toc":2937},[2901,2908,2911,2914,2928],[47,2902,2903,2907],{},[54,2904,95],{"href":2905,"rel":2906},"https:\u002F\u002Fiterable.com\u002F",[263]," is a cross-channel marketing platform that powers unified customer experiences and empowers customers to create, optimize and measure every interaction across the entire customer journey.",[47,2909,2910],{},"In this blog, Greg Methvin, Senior Staff Software Engineer at Iterable, discusses how Pulsar's ability to support both streaming and queueing use cases and its ability to scale were key factors driving the adoption of Pulsar. He also shares in detail how they used Pulsar to replace RabbitMQ, and, ultimately, to replace other messaging systems, like Kafka and Amazon SQS.",[47,2912,2913],{},"You will learn more from this blog:",[359,2915,2916,2919,2922,2925],{},[362,2917,2918],{},"Distributed messaging systems support two types of semantics: streaming and queueing. Each is best suited for certain kinds of use cases.",[362,2920,2921],{},"Apache Pulsar is unique in that it supports both streaming and queueing use cases.",[362,2923,2924],{},"Pulsar’s multi-layered architecture allows users to scale the number and size of topics more conveniently than other messaging systems.",[362,2926,2927],{},"Pulsar provided the right balance of scalability, reliability, and features to replace RabbitMQ at Iterable and, ultimately, to replace other messaging systems like Kafka and Amazon SQS.",[47,2929,2930,2931,2936],{},"This article was originally published on ",[54,2932,2935],{"href":2933,"rel":2934},"https:\u002F\u002Fwww.infoq.com\u002Farticles\u002Fpulsar-customer-engagement-platform\u002F",[263],"InfoQ"," on November 30, 2020.",{"title":17,"searchDepth":18,"depth":18,"links":2938},[],"fbd1b920-6fc9-4cbb-bcfe-81e04f0d6b4c","\u002Fimgs\u002Fwhitepapers\u002F63aecfd4628aade9c9604362_open-graph-pulsar-iterable.webp",{},"\u002Fwhitepapers\u002Fhow-apache-pulsar-helping-scale-customer-engagement-platform",{"title":2896,"description":2896},"whitepapers\u002Fhow-apache-pulsar-helping-scale-customer-engagement-platform",[472],"1Lq5hY5_biMSYFOX28xCHYKVAxOX1tc9KOBZTxmhM6E",{"id":2948,"title":2949,"authors":2950,"body":2953,"createdAt":10,"date":2573,"description":2949,"downloadLink":10,"extension":8,"featured":292,"formTitle":10,"hubspotId":2980,"image":2981,"isDraft":292,"link":10,"meta":2982,"navigation":7,"order":294,"path":2983,"readingTime":10,"relatedResources":10,"seo":2984,"stem":2985,"submitText":10,"successDescription":10,"successTitle":10,"tags":2986,"__hash__":2987,"resourceType":776},"whitepapers\u002Fwhitepapers\u002Fkafka-pulsar-bring-native-kafka-protocol-support-apache-pulsar.md","Kafka-on-Pulsar: bring native Kafka protocol support to Apache Pulsar",[2951,2952,847],"Pierre Zemb","Jia Zhai",{"type":14,"value":2954,"toc":2978},[2955,2964],[47,2956,2957,2958,2963],{},"We are excited to announce that StreamNative and OVHcloud are open-sourcing “Kafka on Pulsar\" (KoP). ",[54,2959,2962],{"href":2960,"rel":2961},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Fkop",[263],"KoP"," brings the native Apache Kafka protocol support to Apache Pulsar by introducing a Kafka protocol handler on Pulsar brokers. By adding the KoP protocol handler to your existing Pulsar cluster, you can now migrate your existing Kafka applications and services to Pulsar without modifying the code. 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Angel PowerFL has been widely used in Tencent Financial Cloud, Advertising Joint Modeling, and other businesses. The platform requires a stable and reliable messaging system with guaranteed high performance and data privacy. After investigating different solutions and comparing several messaging queues, Angel PowerFL adopted Apache Pulsar as the data synchronization solution in Federated Learning (FL).",[47,2999,3000],{},"In this blog, the Tencent Angel PowerFL team shares how they built federated communication based on Pulsar, the challenges they encountered with Pulsar, and how they solved those problems and contributed to the Pulsar community. Tencent’s use of Pulsar in production has demonstrated it provides the stability, reliability, and scalability that the machine learning platform requires.",{"title":17,"searchDepth":18,"depth":18,"links":3002},[],"Tencent’s use of Pulsar in production has demonstrated it provides the stability, reliability, and scalability that the machine learning platform requires.","6f1fd266-61d6-4539-8cd2-21125959e6f9","\u002Fimgs\u002Fwhitepapers\u002F63aed287c258eb451d3aea29_open-graph-tencent-angle.jpg",{},"\u002Fwhitepapers\u002Fpowering-federated-learning-at-tencent-with-apache-pulsar",{"title":2990,"description":3003},"whitepapers\u002Fpowering-federated-learning-at-tencent-with-apache-pulsar",[472],"N6FjYrjcY8uivdYV43diQnF87xD6pu_dwpQ-8c_KRNk",{"id":3013,"title":3014,"authors":3015,"body":3017,"createdAt":10,"date":2573,"description":3014,"downloadLink":10,"extension":8,"featured":292,"formTitle":10,"hubspotId":3030,"image":3031,"isDraft":292,"link":10,"meta":3032,"navigation":7,"order":294,"path":3033,"readingTime":10,"relatedResources":10,"seo":3034,"stem":3035,"submitText":10,"successDescription":10,"successTitle":10,"tags":3036,"__hash__":3037,"resourceType":776},"whitepapers\u002Fwhitepapers\u002Fpulsar-vs-kafka-use-cases-community-features-and-performance.md","Pulsar vs. Kafka: A More Accurate Perspective from Use Cases and Community to Features and Performance",[3016,2357,847],"Carolyn King",{"type":14,"value":3018,"toc":3028},[3019,3022,3025],[47,3020,3021],{},"The shift to real-time streaming technologies has bolstered the adoption of Pulsar and there has been a marked increase in both the interest and adoption of Pulsar in 2020. With Pulsar being sought out by companies developing messaging and event-streaming applications — from Fortune 100 companies to forward-thinking start-ups — and so much growth around the Pulsar project, it has garnered a lot of recent press and attention.",[47,3023,3024],{},"Pulsar offers many advantages that make it an attractive choice for companies seeking to adopt a unified messaging and event streaming platform. Compared with Kafka, Pulsar is more resilient and less complex to operate and scale and it covers all the fundamentals necessary for building event streaming applications and incorporates many built-in features with a rich set of tools.",[47,3026,3027],{},"In this blog, we compare Pulsar and Kafka in terms of performance, architecture, and features to help you make an informed decision.",{"title":17,"searchDepth":18,"depth":18,"links":3029},[],"bf5df64a-14c5-4aab-8c66-d36349046422","\u002Fimgs\u002Fwhitepapers\u002F63aed1850987251ce3046ace_open-graph-pulsar-vs-kafka.webp",{},"\u002Fwhitepapers\u002Fpulsar-vs-kafka-use-cases-community-features-and-performance",{"title":3014,"description":3014},"whitepapers\u002Fpulsar-vs-kafka-use-cases-community-features-and-performance",[685,472,301],"FCkipQ208lB19haw7fn-j2z45x69VBLl6Ft0SU2Dw7c",{"id":3039,"title":3040,"authors":3041,"body":3042,"createdAt":10,"date":2573,"description":3040,"downloadLink":10,"extension":8,"featured":292,"formTitle":10,"hubspotId":3055,"image":3056,"isDraft":292,"link":10,"meta":3057,"navigation":7,"order":294,"path":3058,"readingTime":10,"relatedResources":10,"seo":3059,"stem":3060,"submitText":10,"successDescription":10,"successTitle":10,"tags":3061,"__hash__":3062,"resourceType":776},"whitepapers\u002Fwhitepapers\u002Ftaking-a-deep-dive-into-apache-pulsar-architecture-for-performance-tuning.md","Taking a Deep-Dive into Apache Pulsar Architecture for Performance Tuning",[813],{"type":14,"value":3043,"toc":3053},[3044,3047,3050],[47,3045,3046],{},"When we talk about Apache Pulsar’s performance, we are usually referring to the throughput and latency associated with message writes and reads. Pulsar has certain configuration parameters that allow you to control how the system handles message writes or reads. I’d like to start a series to discuss how this type of performance tuning works in Apache Pulsar. This is the first post in that series.",[47,3048,3049],{},"In this post, I’ll try to give you a deep explanation of Apache Pulsar’s architecture and also describe its storage layer, Apache BookKeeper™. Knowledge of BookKeeper is essential to the Apache Pulsar tuning process.",[47,3051,3052],{},"Before we begin, you will need to become familiar with some basic concepts. At a minimum, you need to know how messages are sent and received (that is, produced and consumed) in Apache Pulsar.",{"title":17,"searchDepth":18,"depth":18,"links":3054},[],"8a6e0e70-1bee-4ee9-ac8e-65e48b6dffb8","\u002Fimgs\u002Fwhitepapers\u002F63aed220d640de4e2ddb73d7_open-graph-wp-pulsar-performance-deep-dive.webp",{},"\u002Fwhitepapers\u002Ftaking-a-deep-dive-into-apache-pulsar-architecture-for-performance-tuning",{"title":3040,"description":3040},"whitepapers\u002Ftaking-a-deep-dive-into-apache-pulsar-architecture-for-performance-tuning",[472],"SanciPYbNdhfNNwoPMHugTARYD5JGVQkBSvBYYHGlDM",{"id":3064,"title":3065,"authors":3066,"body":3067,"createdAt":10,"date":2573,"description":3065,"downloadLink":3080,"extension":8,"featured":292,"formTitle":10,"hubspotId":3081,"image":3082,"isDraft":292,"link":10,"meta":3083,"navigation":7,"order":294,"path":3084,"readingTime":10,"redirectDelay":10,"relatedResources":10,"seo":3085,"stem":3086,"submitText":10,"successDescription":10,"successTitle":10,"tags":3087,"__hash__":3088,"resourceType":474},"reports\u002Freports\u002F2020-apache-pulsar-user-survey-report.md","The 2020 Apache Pulsar User Survey Report",[3016,847],{"type":14,"value":3068,"toc":3078},[3069,3072,3075],[47,3070,3071],{},"For the first time ever, the Apache Pulsar PMC team is publishing a user survey report. The 2020 ApachePulsar User Survey Report reveals Pulsar’s accelerating rate of global adoption, details how organizations are leveraging Pulsar to build real-time streaming applications, and highlights key features on Pulsar’s product roadmap.",[47,3073,3074],{},"Pulsar adoption has largely been driven by the market’s increased demand for real-time, data-enabled technologies. While companies have tried to leverage monolithic messaging systems to build-out real-time offerings, they’ve hit major roadblocks. Ultimately, these technologies are not equipped to provide the scale or reliability that mission-critical applications require.",[47,3076,3077],{},"As a result, companies have sought-out Apache Pulsar for its cloud-native, distributed messaging and streaming platform capabilities. From asynchronous applications to core business applications to ETL, companies are increasingly leveraging Pulsar to develop real-time applications. Pulsar has received global adoption from major technology companies such as Verizon Media, Narvar, Overstock, Nutanix, Yahoo! JAPAN, Tencent, OVHCloud, and Clever Cloud, who rely on its ability to deliver on performance, scalability, and resiliency. As the Pulsar project and community garner increasing attention, we’re excited to share the 2020 Apache Pulsar User Survey Report.",{"title":17,"searchDepth":18,"depth":18,"links":3079},[],"https:\u002F\u002F6585952.fs1.hubspotusercontent-na1.net\u002Fhubfs\u002F6585952\u002FReports\u002FApache%20Pulsar%20User%20Report%20-%202021.pdf","f4b5280e-93a5-4847-82cd-e5353f01d9ad","\u002Fimgs\u002Fwhitepapers\u002F63aed305c258eb5ca73b3f02_open-graph-wp-apache-pulsar-report-2020.jpg",{},"\u002Freports\u002F2020-apache-pulsar-user-survey-report",{"title":3065,"description":3065},"reports\u002F2020-apache-pulsar-user-survey-report",[472,301],"ojILmtZdF6vs7UrOiX0t-Wx2QrQ0KBUZ1r-jJqakIWE",{"id":3090,"title":3091,"authors":3092,"body":3093,"createdAt":10,"date":2573,"description":3126,"downloadLink":3127,"extension":8,"featured":7,"formTitle":10,"hubspotId":3128,"image":3129,"isDraft":292,"link":10,"meta":3130,"navigation":7,"order":294,"path":3131,"readingTime":10,"redirectDelay":10,"relatedResources":10,"seo":3132,"stem":3133,"submitText":10,"successDescription":10,"successTitle":10,"tags":3134,"__hash__":3135,"resourceType":474},"reports\u002Freports\u002Fapache-pulsar-vs-apache-kafka-2022-benchmark.md","Apache Pulsar vs. Apache Kafka 2022 Benchmark",[812,813],{"type":14,"value":3094,"toc":3124},[3095,3098,3101,3114,3117],[47,3096,3097],{},"**Get your copy of the Apache Pulsar vs. Apache Kafka 2022 Benchmark today! **",[47,3099,3100],{},"Organizations often make comparisons between Apache Pulsar and Apache Kafka based on features, capabilities, size of the community, and a number of other metrics of varying importance. This report focuses purely on comparing the technical performance based on benchmark tests.",[47,3102,3103,3104,3108,3109,3113],{},"The last widely published ",[54,3105,3107],{"href":3106},"\u002Fblog\u002Fperspective-on-pulsars-performance-compared-to-kafka","Pulsar versus Kafka benchmark"," was performed in 2020, and a lot has happened since then. In 2021, Pulsar ranked as a Top 5 Apache Software Foundation project and ",[54,3110,3112],{"href":3111},"\u002Fblog\u002Fpulsar-hits-400th-contributor-passes-kafka-monthly-active-contributors","surpassed Apache Kafka"," in monthly active contributors. Pulsar also averaged more monthly active contributors than Kafka for most of the past 18 months.",[47,3115,3116],{},"These contributions led to major performance improvements for Pulsar. To measure the impact of the improvements, the engineering team at StreamNative, led by Matteo Merli, Apache Pulsar PMC Chairperson and StreamNative CTO, performed a benchmark study using the Linux Foundation Open Messaging benchmark.",[47,3118,3119,3120,3123],{},"Read the Apache Pulsar vs. Apache Kafka 2022 Benchmark ",[54,3121,266],{"href":3122},"\u002Fblog\u002Fapache-pulsar-vs-apache-kafka-2022-benchmark"," or download the PDF today for a side-by-side comparison of Pulsar and Kafka performance, including tests on throughput, latency, and more.",{"title":17,"searchDepth":18,"depth":18,"links":3125},[],"Apache Pulsar vs. Apache Kafka 2022 Benchmark Report","https:\u002F\u002F6585952.fs1.hubspotusercontent-na1.net\u002Fhubfs\u002F6585952\u002FApache%20Pulsar%20vs.%20Apache%20Kafka%202022%20Performance%20Benchmark.pdf","4a31507e-c155-4977-9e1d-8e6c13a7b319","\u002Fimgs\u002Fwhitepapers\u002F63bd307e8c4076bac9f1b090_pulsar-vs-kafka.png",{},"\u002Freports\u002Fapache-pulsar-vs-apache-kafka-2022-benchmark",{"title":3091,"description":3126},"reports\u002Fapache-pulsar-vs-apache-kafka-2022-benchmark",[685,472,1749,2490],"njAXqSZPCeG-sin7mZqWzgRfSoY54mhAbB5OdO1YED4",{"id":3137,"title":3138,"authors":3139,"body":3143,"category":1299,"createdAt":10,"date":3896,"description":3897,"extension":8,"featured":7,"image":3898,"isDraft":292,"link":10,"meta":3899,"navigation":7,"order":294,"path":3900,"readingTime":3901,"relatedResources":10,"seo":3902,"stem":3903,"tags":3904,"__hash__":3905},"blogs\u002Fblog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour.md","How We Run a 5 GB\u002Fs Kafka Workload for Just $50 per Hour",[3140,3141,3142,813],"Matteo Meril","Neng Lu","Hang Chen",{"type":14,"value":3144,"toc":3866},[3145,3148,3151,3154,3157,3160,3164,3167,3175,3181,3184,3192,3197,3201,3207,3210,3213,3221,3225,3228,3233,3237,3240,3243,3246,3249,3258,3262,3265,3276,3279,3283,3286,3289,3300,3303,3307,3311,3319,3322,3326,3334,3363,3367,3370,3375,3379,3382,3386,3389,3392,3397,3406,3411,3414,3417,3428,3432,3435,3446,3450,3453,3456,3461,3464,3493,3497,3499,3505,3508,3513,3518,3521,3525,3539,3543,3554,3558,3573,3582,3593,3596,3599,3603,3606,3609,3620,3623,3626,3629,3634,3639,3643,3646,3663,3667,3681,3686,3690,3701,3704,3720,3724,3735,3740,3745,3753,3757,3760,3764,3771,3775,3778,3787,3792,3799,3805,3814,3823,3832,3841,3850,3858],[47,3146,3147],{},"The rise of DeepSeek has shaken the AI infrastructure market, forcing companies to confront the escalating costs of training and deploying AI models. But the real pressure point isn’t just compute—it’s data acquisition and ingestion costs.",[47,3149,3150],{},"As businesses rethink their AI cost-containment strategies, real-time data streaming is emerging as a critical enabler. The growing adoption of Kafka as a standard protocol has expanded cost-efficient options, allowing companies to optimize streaming analytics while keeping expenses in check.",[47,3152,3153],{},"Ursa, the data streaming engine powering StreamNative’s managed Kafka service, is built for this new reality. With its leaderless architecture and native lakehouse storage integration, Ursa eliminates costly inter-zone network traffic for data replication and client-to-broker communication while ensuring high availability at minimal operational cost.",[47,3155,3156],{},"In this blog post, we benchmarked the infrastructure cost and total cost of ownership (TCO) for running a 5GB\u002Fs Kafka workload across different Kafka vendors, including Redpanda, Confluent WarpStream, and AWS MSK. Our benchmark results show that Ursa can sustain 5GB\u002Fs Kafka workloads at just 5% of the cost of traditional streaming engines like Redpanda—making it the ideal solution for high-performance, cost-efficient ingestion and data streaming for data lakehouses and AI workloads.",[47,3158,3159],{},"Note: We also evaluated vanilla Kafka in our benchmark; however, for simplicity, we have focused our cost comparison on vendor solutions rather than self-managed deployments. That said, it is important to highlight that both Redpanda and vanilla Kafka use a leader-based data replication approach. In a data-intensive, network-bound workload like 5GB\u002Fs streaming, with the same machine type and replication factor, Redpanda and vanilla Kafka produced nearly identical cost profiles.",[39,3161,3163],{"id":3162},"key-benchmark-findings","Key Benchmark Findings",[47,3165,3166],{},"Ursa delivered 5 GB\u002Fs of sustained throughput at an infrastructure cost of just $54 per hour. For comparison:",[359,3168,3169,3172],{},[362,3170,3171],{},"MSK: $303 per hour → 5.6x more expensive compared to Ursa",[362,3173,3174],{},"Redpanda: $988 per hour → 18x more expensive compared to Ursa",[47,3176,3177],{},[3178,3179],"img",{"alt":17,"src":3180},"\u002Fimgs\u002Fblogs\u002F679c71b67d9046f26edc7977_AD_4nXfvTqyBNUBu2lObdkKAx-5UNkpNP8UYULLZyOcixE6z99VMZUUEsUqWjzexI7vjyNGRNSAUoM9smYvdTP55ctAhIbrs5lmQgcSVMWdaoigbWouCl95DVSQsxooY-qqfGcYqS4g4zA.png",[47,3182,3183],{},"Beyond infrastructure costs, when factoring in both storage pricing, vendor pricing and operational expenses, Ursa’s total cost of ownership (TCO) for a 5GB\u002Fs workload with a 7-day retention period is:",[359,3185,3186,3189],{},[362,3187,3188],{},"50% cheaper than Confluent WarpStream",[362,3190,3191],{},"85% cheaper than MSK and Redpanda",[47,3193,3194],{},[3178,3195],{"alt":17,"src":3196},"\u002Fimgs\u002Fblogs\u002F679c602d77e9c706de5343b8_AD_4nXeDv8rrv_C1CTCCiqYo1zpvlGYbdBk1r0VEqovAPu22iFMQZgh54Hfw9PBMLzM7jDFxKwAFDxbdG0np4XVk_tGsWhEKMloLRcmmea7lvueCx-0cFsyaE3Mya4Mxc1Dox95A6JEc.png",[39,3198,3200],{"id":3199},"ursa-highly-cost-efficient-data-streaming-at-scale","Ursa: Highly Cost-Efficient Data Streaming at Scale",[47,3202,3203,3206],{},[54,3204,686],{"href":3205},"\u002Fblog\u002Fursa-reimagine-apache-kafka-for-the-cost-conscious-data-streaming"," is a next-generation data streaming engine designed to deliver high performance at a fraction of the cost of traditional disk-based solutions. It is fully compatible with Apache Kafka and Apache Pulsar APIs, while leveraging a leaderless, lakehouse-native architecture to maximize scalability, efficiency, and cost savings.",[47,3208,3209],{},"Ursa’s key innovation is separating storage from compute and decoupling metadata\u002Findex operations from data operations by utilizing cloud object storage (e.g., AWS S3) instead of costly inter-zone disk-based replication. It also employs open lakehouse formats (Iceberg and Delta Lake), enabling columnar compression to significantly reduce storage costs while maintaining durability and availability.",[47,3211,3212],{},"In contrast, traditional streaming systems—like Kafka and Redpanda—depend on leader-based architectures, which drive up inter-zone traffic costs due to replication and client communication. Ursa mitigates these costs by:",[359,3214,3215,3218],{},[362,3216,3217],{},"Eliminating inter-zone traffic costs via a leaderless architecture.",[362,3219,3220],{},"Replacing costly inter-zone replication with direct writes to cloud storage using open lakehouse formats.",[39,3222,3224],{"id":3223},"how-ursa-eliminates-inter-zone-traffic","How Ursa Eliminates Inter-Zone Traffic",[47,3226,3227],{},"Ursa minimizes inter-zone traffic by leveraging a leaderless architecture, which eliminates inter-zone communication between clients and brokers, and lakehouse-native storage, which removes the need for inter-zone data replication. This approach ensures high availability and scalability while avoiding unnecessary cross-zone data movement.",[47,3229,3230],{},[3178,3231],{"alt":17,"src":3232},"\u002Fimgs\u002Fblogs\u002F679c602e21b3571bb7117dca_AD_4nXd7Oahc77NjRLNvA9clLt0tsyU6MrIqVibFYv5pW5giTIcCHPr3EA_yTGzfVEUIVO3VXK56qWK8zmBCp5lY0E_4nmlWIPFrHjtHylA5NhwELjn-UB0fLG2h_kbrxrc7Cs_edvveNA.png",[31,3234,3236],{"id":3235},"leaderless-architecture","Leaderless architecture",[47,3238,3239],{},"Traditional streaming engines such as Kafka, Pulsar, or RedPanda rely on a leader-based model, where each partition is assigned to a single leader broker that handles all writes and reads.",[47,3241,3242],{},"Pros of Leader-Based Architectures:\n✔ Maintains message ordering via local sequence IDs\n✔ Delivers low latency and high performance through message caching",[47,3244,3245],{},"Cons of Leader-Based Architectures:\n✖ Throughput bottlenecked by a single broker per partition\n✖ Inter-zone traffic required for high availability in multi-AZ deployments",[47,3247,3248],{},"While Kafka and Pulsar offer partial solutions (e.g., reading from followers, shadow topics) to reduce read-related inter-zone traffic, producers still send data to a single leader.",[47,3250,3251,3252,3257],{},"Ursa removes the concept of topic ownership, allowing any broker in the cluster to handle reads or writes for any partition. The primary challenge—ensuring message ordering—is solved with ",[54,3253,3256],{"href":3254,"rel":3255},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Foxia",[263],"Oxia",", a scalable metadata and index service created by StreamNative in 2022.",[31,3259,3261],{"id":3260},"oxia-the-metadata-layer-enabling-leaderless-architecture","Oxia: The Metadata Layer Enabling Leaderless Architecture",[47,3263,3264],{},"Ensuring message ordering in a leaderless architecture is complex, but Ursa solves this with Oxia:",[359,3266,3267,3270,3273],{},[362,3268,3269],{},"Handles millions of metadata\u002Findex operations per second",[362,3271,3272],{},"Generates sequential IDs to maintain strict message ordering",[362,3274,3275],{},"Optimized for Kubernetes with horizontal scalability",[47,3277,3278],{},"Producers and consumers can connect to any broker within their local AZ, eliminating inter-zone traffic costs while maintaining performance through localized caching.",[31,3280,3282],{"id":3281},"zero-interzone-data-replication","Zero interzone data replication",[47,3284,3285],{},"In most distributed systems, data replication from a leader (primary) to followers (replicas) is crucial for fault tolerance and availability. However, replication across zones can inflate infrastructure expenses substantially.",[47,3287,3288],{},"Ursa avoids these costs by writing data directly to cloud storage (e.g., AWS S3, Google GCS):",[359,3290,3291,3294,3297],{},[362,3292,3293],{},"Built-In Resilience: Cloud storage inherently offers high availability and fault tolerance without inter-zone traffic fees.",[362,3295,3296],{},"Tradeoff: Slightly higher latency (sub-second, with p99 at 500 milliseconds) compared to local disk\u002FEBS (single-digit to sub-100 milliseconds), in exchange for significantly lower costs (up to 10x lower).",[362,3298,3299],{},"Flexible Modes: Ursa is an addition to the classic BookKeeper-based engine, providing users with the flexibility to optimize for either cost or low latency based on their workload requirements.",[47,3301,3302],{},"By foregoing conventional replication, Ursa slashes inter-zone traffic costs and associated complexities—making it a compelling option for organizations seeking to balance high-performance data streaming with strict budget constraints.",[39,3304,3306],{"id":3305},"how-we-ran-a-5-gbs-test-with-ursa","How We Ran a 5 GB\u002Fs Test with Ursa",[31,3308,3310],{"id":3309},"ursa-cluster-deployment","Ursa Cluster Deployment",[359,3312,3313,3316],{},[362,3314,3315],{},"9 brokers across 3 availability zones, each on m6i.8xlarge (Fixed 12.5 Gbps bandwidth, 32 vCPU cores, 128 GB memory).",[362,3317,3318],{},"Oxia cluster (metadata store) with 3 nodes of m6i.8xlarge, distributed across three availability zones (AZs).",[47,3320,3321],{},"During peak throughput (5 GB\u002Fs), each broker’s network usage was about 10 Gbps.",[31,3323,3325],{"id":3324},"openmessaging-benchmark-workers-configuration","OpenMessaging Benchmark Workers & Configuration",[47,3327,3328,3329,3333],{},"The OpenMessaging Benchmark(OMB) Framework is a suite of tools that make it easy to benchmark distributed messaging systems in the cloud. Please check ",[54,3330,3331],{"href":3331,"rel":3332},"https:\u002F\u002Fopenmessaging.cloud\u002Fdocs\u002Fbenchmarks\u002F",[263]," for details.",[359,3335,3336,3351,3360],{},[362,3337,3338,3339,3344,3345,3350],{},"12 OMB workers: 6 for ",[54,3340,3343],{"href":3341,"rel":3342},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002Fd1094122270775e4f1580947f80c5055",[263],"producers",", 6 for ",[54,3346,3349],{"href":3347,"rel":3348},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F06bada89381fb77a7862e1b4c1d8963d",[263],"consumers"," across 3 availability zones, on m6i.8xlarge instances. Each worker is configured with 12 CPU cores and 48 GB memory.",[362,3352,3353,3354,3359],{},"Sample YAML ",[54,3355,3358],{"href":3356,"rel":3357},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F204c1f26c4d44a218ae235bf2de99904",[263],"scripts"," provided for Kafka-compatible configuration and rate limits.",[362,3361,3362],{},"Achieved consistent 5 GB\u002Fs publish\u002Fsubscribe throughput.",[39,3364,3366],{"id":3365},"ursa-benchmark-tests-results","Ursa Benchmark Tests & Results",[47,3368,3369],{},"The following diagram demonstrates that Ursa can consistently handle 5 GB\u002Fs of traffic, fully saturating the network across all broker nodes.",[47,3371,3372],{},[3178,3373],{"alt":17,"src":3374},"\u002Fimgs\u002Fblogs\u002F679c602d7b261bac1113f7d6_AD_4nXdDPsRc3koXICiFF0bqSmGWbJt_RlUy4FE3ruuWOfbCfpcqZ1dejjqGbkaCJv2hQFL1nirRouBVRW2l5uMWBvY9naMqGB_wHcLI14dBM0f85TXhmdm3UxEv1yGX9Y4hf5FttSkZew.png",[39,3376,3378],{"id":3377},"comparing-infrastructure-cost","Comparing Infrastructure Cost",[47,3380,3381],{},"This benchmark first evaluates infrastructure costs of running a 5 GB\u002Fs streaming workload (1:1 producer-to-consumer ratio) across different data streaming engines, including Ursa, Redpanda, and AWS MSK, with a focus on multi-AZ deployments to ensure a fair comparison.",[31,3383,3385],{"id":3384},"test-setup-key-assumptions","Test Setup & Key Assumptions",[47,3387,3388],{},"All tests use multi-AZ configurations, with clusters and clients distributed across three AWS availability zones (AZs). Cluster size scales proportionally to the number of AZs, and rack-awareness is enabled for all engines to evenly distribute topic partitions and leaders.",[47,3390,3391],{},"To ensure a fair comparison, we selected the same machine type capable of fully utilizing both network and storage bandwidth for Ursa and Redpanda in this 5GB\u002Fs test:",[359,3393,3394],{},[362,3395,3396],{},"9 × m6i.8xlarge instances",[47,3398,3399,3400,3405],{},"However, MSK's storage bandwidth limits vary depending on the selected instance type, with the highest allowed limit capped at 1000 MiB\u002Fs per broker, according to",[54,3401,3404],{"href":3402,"rel":3403},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fmsk-provision-throughput-management.html#throughput-bottlenecks",[263]," AWS documentation",". Given this constraint, achieving 5 GB\u002Fs throughput with a replication factor of 3 required the following setup:",[359,3407,3408],{},[362,3409,3410],{},"15 × kafka.m7g.8xlarge (32 vCPUs, 128 GB memory, 15 Gbps network, 4000 GiB EBS).",[47,3412,3413],{},"This configuration was necessary to work around MSK's storage bandwidth limitations, ensuring a comparable cost basis to other evaluated streaming engines.",[47,3415,3416],{},"Additional key assumptions include:",[359,3418,3419,3422,3425],{},[362,3420,3421],{},"Inter-AZ producer traffic: For leader-based engines, two-thirds of producer-to-broker traffic crosses AZs due to leader distribution.",[362,3423,3424],{},"Consumer optimizations: Follower fetch is enabled across all tests, eliminating inter-AZ consumer traffic.",[362,3426,3427],{},"Storage cost exclusions: This benchmark only evaluates streaming costs, assuming no long-term data retention.",[31,3429,3431],{"id":3430},"inter-broker-replication-costs","Inter-Broker Replication Costs",[47,3433,3434],{},"Inter-broker (cross-AZ) replication is a major cost driver for data streaming engines:",[359,3436,3437,3440,3443],{},[362,3438,3439],{},"RedPanda: Inter-broker replication is not free, leading to substantial costs when data must be copied across multiple availability zones.",[362,3441,3442],{},"AWS MSK: Inter-broker replication is free, but MSK instance pricing is significantly higher (e.g., $3.264 per hour for kafka.m7g.8xlarge vs $1.306 per hour for an on-demand m7g.8xlarge). The storage price of MSK is $0.10 per GB-month which is significantly higher than st1, which costs $0.045 per GB-month. Even though replication is free, client-to-broker traffic still incurs inter-AZ charges.",[362,3444,3445],{},"Ursa: No inter-broker replication costs due to its leaderless architecture, eliminating inter-zone replication costs entirely.",[31,3447,3449],{"id":3448},"zone-affinity-reducing-inter-az-costs","Zone Affinity: Reducing Inter-AZ Costs",[47,3451,3452],{},"We evaluated zone affinity mechanisms to further reduce inter-AZ data transfer costs.",[47,3454,3455],{},"Consumers:",[359,3457,3458],{},[362,3459,3460],{},"Follower fetch is enabled across all tests, ensuring consumers fetch data from replicas in their local AZ—eliminating inter-zone consumer traffic except for metadata lookups",[47,3462,3463],{},"Producers:",[359,3465,3466,3475,3484],{},[362,3467,3468,3469,3474],{},"Kafka protocol lacks an easy way to enforce producer AZ affinity (though ",[54,3470,3473],{"href":3471,"rel":3472},"https:\u002F\u002Fcwiki.apache.org\u002Fconfluence\u002Fdisplay\u002FKAFKA\u002FKIP-1123:+Rack-aware+partitioning+for+Kafka+Producer",[263],"KIP-1123"," aims to address this). And it only works with the default partitioner (i.e., when no record partition or record key is specified).",[362,3476,3477,3478,3483],{},"Redpanda recently introduced ",[54,3479,3482],{"href":3480,"rel":3481},"https:\u002F\u002Fdocs.redpanda.com\u002Fredpanda-cloud\u002Fdevelop\u002Fproduce-data\u002Fleader-pinning\u002F",[263],"leader pinning",", but this only benefits setups where producers are confined to a single AZ—not applicable to our multi-AZ benchmark.",[362,3485,3486,3487,3492],{},"Ursa is the only system in this test with ",[54,3488,3491],{"href":3489,"rel":3490},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fconfig-kafka-client#eliminate-cross-az-networking-traffic",[263],"built-in zone affinity for both producers and consumers",". It achieves this by embedding producer AZ information in client.id, allowing metadata lookups to route clients to local-AZ brokers, eliminating inter-AZ producer traffic.",[31,3494,3496],{"id":3495},"cost-comparison-results","Cost Comparison Results",[47,3498,3166],{},[359,3500,3501,3503],{},[362,3502,3171],{},[362,3504,3174],{},[47,3506,3507],{},"Ursa’s leaderless architecture, zone affinity, and native cloud storage integration deliver unparalleled cost efficiency, making it the most cost-effective choice for high-throughput data streaming workloads.",[47,3509,3510],{},[3178,3511],{"alt":17,"src":3512},"\u002Fimgs\u002Fblogs\u002F679c72208198ca36a352f228_AD_4nXeeZuM8T-xBlD4Vf3j67K618n08qh8wIDLLtiLJG0ssA1Wj1V26u7wIDTX9sqLrtw8mB2c299dwzarGen62CG0Vh7nWstn5qbPGFcBaKJYEepTsLr5fHWv1U8uqbg8Y0UOK6fJ7.png",[47,3514,3515],{},[3178,3516],{"alt":17,"src":3517},"\u002Fimgs\u002Fblogs\u002F679c625978031f40229de484_AD_4nXdLkLLJ30KKr-_A_rN1j8akVwBYacAWIPzWHoOReJF421890kfByZoQQxkLczihVSmiw5Q9J51-V9I2SEKITbwsYnANDDTlAVL5nQ_jfaHNTe9VEWhSoa7DZooCnilDYL6l6msmJg.png",[47,3519,3520],{},"The detailed infrastructure cost calculations for each data streaming engine are listed below:",[31,3522,3524],{"id":3523},"streamnative-ursa","StreamNative - Ursa",[359,3526,3527,3530,3533,3536],{},[362,3528,3529],{},"Server EC2 costs: 9 * $1.536\u002Fhr = $14",[362,3531,3532],{},"Client EC2 costs: 9 * $1.536\u002Fhr =$14",[362,3534,3535],{},"S3 write requests costs: 1350 r\u002Fs * $0.005\u002F1000r * 3600s = $24",[362,3537,3538],{},"S3 read requests costs: 1350 r\u002Fs * $0.0004\u002F1000r * 3600s = $2",[31,3540,3542],{"id":3541},"aws-msk","AWS MSK",[359,3544,3545,3548,3551],{},[362,3546,3547],{},"Server EC2 costs: 15 * $3.264\u002Fhr = $49",[362,3549,3550],{},"Client side EC2 costs: 9 * $1.536\u002Fhr =$14",[362,3552,3553],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FG(in+out) * 3600 = $240",[31,3555,3557],{"id":3556},"redpanda","RedPanda",[359,3559,3560,3562,3564,3567,3570],{},[362,3561,3529],{},[362,3563,3532],{},[362,3565,3566],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FGB(in+out) * 3600 = $240",[362,3568,3569],{},"Interzone traffic - replication: 10GB\u002Fs * $0.02\u002FGB(in+out) * 3600 = $720",[362,3571,3572],{},"Interzone traffic - broker to consumer: $0 (fetch from local zone)",[47,3574,3575,3576,3581],{},"Please note that we were unable to test ",[54,3577,3580],{"href":3578,"rel":3579},"https:\u002F\u002Fwww.redpanda.com\u002Fblog\u002Fcloud-topics-streaming-data-object-storage",[263],"Redpanda with Cloud Topics",", as it remains an announced but unreleased feature and is not yet available for evaluation. Based on the limited information available, while Cloud Topics may help optimize inter-zone data replication costs, producers still need to traverse inter-availability zones to connect to the topic partition owners and incur inter-zone traffic costs of up to $240 per hour.",[359,3583,3584,3590],{},[362,3585,3586,3589],{},[54,3587,3473],{"href":3471,"rel":3588},[263]," (when implemented) will help mitigate producer-to-broker inter-zone traffic, but it is not yet available. And it only works with the default partitioner (no record partition or key is specified).",[362,3591,3592],{},"Redpanda’s leader pinning helps only when all producers for the pinned topic are confined to a single AZ. In multi-AZ environments (like our benchmark), inter-zone producer traffic remains unavoidable.",[47,3594,3595],{},"Additionally, Redpanda’s Cloud Topics architecture is not documented publicly. Their blog mentions \"leader placement rules to optimize produce latency and ingress cost,\" but it is unclear whether this represents a shift away from a leader-based architecture or if it uses techniques similar to Ursa’s zone-aware approach.",[47,3597,3598],{},"We may revisit this comparison as more details become available.",[39,3600,3602],{"id":3601},"comparing-total-cost-of-ownership","Comparing Total Cost of Ownership",[47,3604,3605],{},"As highlighted earlier, with a BYOC Ursa setup, you can achieve 5 GB\u002Fs throughput at just 5% of the infrastructure cost of a traditional leader-based data streaming engine, such as Kafka or RedPanda, while managing the infrastructure yourself. This significant cost reduction is enabled by Ursa’s leaderless architecture and lakehouse-native storage design, which eliminate overhead costs such as inter-zone traffic and leader-based data replication. By leveraging a lakehouse-native, leaderless architecture, Ursa reduces resource requirements, enabling you to handle high data throughput efficiently and at a fraction of the cost of RedPanda.",[47,3607,3608],{},"Now, let’s examine the total cost comparison, evaluating Ursa alongside other vendors, including those that have adopted a leaderless architecture (e.g., Confluent WarpStream). This comparison is based on a 5GB\u002Fs workload with a 7-day retention period, factoring in both storage cost and vendor costs Here are the key findings:",[359,3610,3611,3614,3617],{},[362,3612,3613],{},"Ursa ($164,353\u002Fmonth) is: 50% cheaper than Confluent WarpStream ($337,068\u002Fmonth)",[362,3615,3616],{},"85% cheaper than AWS MSK ($1,115,251\u002Fmonth)",[362,3618,3619],{},"86% cheaper than Redpanda ($1,202,853\u002Fmonth)",[47,3621,3622],{},"In addition to Ursa’s architectural advantages—eliminating most inter-AZ traffic and leveraging lakehouse storage for cost-effective data retention—it also adopts a more fair and cost-efficient pricing model: Elastic Throughput-based pricing. This approach aligns costs with actual usage, avoiding unnecessary overhead.",[47,3624,3625],{},"Unlike WarpStream, which charges for both storage and throughput, Ursa ensures that customers only pay for the throughput they actively use. Ursa’s pricing is based on compressed data sent by clients, meaning the more data compressed on the client side, the lower the cost. In contrast, WarpStream prices are based on uncompressed data, unfairly inflating expenses and failing to incentivize customers to optimize their client applications.",[47,3627,3628],{},"This distinction is crucial, as compressed data reduces both storage and network costs, making Ursa’s pricing model not only more cost-effective but also more transparent and predictable.",[47,3630,3631],{},[3178,3632],{"alt":17,"src":3633},"\u002Fimgs\u002Fblogs\u002F679c602d194800c9206d9d58_AD_4nXcFlf755xgyz7htxhMhBV5fGrsxy642mQNodt61DTok_z1dwkw5A6lkO5hatXVneCaB0anbZPAyvLI3MlIMuQEYLEACHHvQMOr5UfaB37dfzkdqewDEvcT-20VGd_zzvJsuA00zGA.png",[47,3635,3636],{},[3178,3637],{"alt":17,"src":3638},"\u002Fimgs\u002Fblogs\u002F679c62594e9c2e629fae73aa_AD_4nXeU6cOgItnjLsEZCOf13TEvMY_SHWWIxYP2OYUj-B1GUPyWO78OG08K_v03hwYSVcg06f9dqDiGmdwy76vynjmiDGL5bluZ5_XF4nSU_r59oOZdfViXndXt6s11vVOY7qwfZN8v.png",[31,3640,3642],{"id":3641},"cost-breakdown","Cost Breakdown",[999,3644,3645],{"id":3523},"StreamNative – Ursa",[359,3647,3648,3651,3654,3657,3660],{},[362,3649,3650],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9,953.28",[362,3652,3653],{},"S3 Write Requests: 1,350 r\u002Fs × $0.005\u002F1,000 r × 3,600 s × 24 hr × 30 days = $17,496",[362,3655,3656],{},"S3 Read Requests: 1,350 r\u002Fs × $0.0004\u002F1,000 r × 3,600 s × 24 hr × 30 days = $1,400",[362,3658,3659],{},"S3 Storage Costs: 5 GB\u002Fs × $0.021\u002FGB × 3,600 s × 24 hr × 7 days = $63,504",[362,3661,3662],{},"Vendor Cost: 200 ETU × $0.50\u002Fhr × 24 hr × 30 days = $72,000",[999,3664,3666],{"id":3665},"warpstream","WarpStream",[359,3668,3669,3672],{},[362,3670,3671],{},"Based on WarpStream’s pricing calculator (as of January 29, 2025), we assume a 4:1 client data compression ratio, meaning 20 GB\u002Fs of uncompressed data translates to 5 GB\u002Fs of compressed data.",[362,3673,3674,3675,3680],{},"It's important to note that WarpStream’s pricing structure has fluctuated frequently throughout January. We observed the cost reported by their calculator changing from $409,644 per month to $337,068 per month. This variability has been previously highlighted in the blog post “",[54,3676,3679],{"href":3677,"rel":3678},"https:\u002F\u002Fbigdata.2minutestreaming.com\u002Fp\u002Fthe-brutal-truth-about-apache-kafka-cost-calculators",[263],"The Brutal Truth About Kafka Cost Calculators","”. To ensure transparency, we have documented the pricing as of January 29, 2025.",[47,3682,3683],{},[3178,3684],{"alt":17,"src":3685},"\u002Fimgs\u002Fblogs\u002F679c602e42713e0028e9af5e_AD_4nXcu5_VWTLu9jRYs6zX1MBAOtLQEo5gyfNSWPcbpnQHXTa8qNCFAXezRR2E8daygzYTTwd4dhJjaLaLM8C6y_3OGbu2NS7pdvEv3a8-ptNKOg7AeKnYqPQCAYvQ5EuxzuI3JYIvY.png",[999,3687,3689],{"id":3688},"msk","MSK",[359,3691,3692,3695,3698],{},[362,3693,3694],{},"EC2 (Server): 15 * $3.264\u002Fhr × 24 hr × 30 days = $35,251",[362,3696,3697],{},"Interzone Traffic (Client-Server): 5 GB\u002Fs × ⅔ × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $172,800",[362,3699,3700],{},"Storage: 5 GB\u002Fs × $0.1\u002FGB-month × 3,600 s × 24 hr × 7 days * 3 replicas = $907,200",[999,3702,3557],{"id":3703},"redpanda-1",[359,3705,3706,3709,3711,3714,3717],{},[362,3707,3708],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9953",[362,3710,3697],{},[362,3712,3713],{},"Interzone Traffic (Replication): 5 GB\u002Fs × 2 × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $518,400",[362,3715,3716],{},"Storage: 5 GB\u002Fs × $0.045\u002FGB-month(st1) × 3,600 s × 24 hr × 7 days * 3 replicas = $408,240",[362,3718,3719],{},"Vendor Cost: $93,333 per month (based on limited information. See additional notes below).",[999,3721,3723],{"id":3722},"additional-notes","Additional Notes",[359,3725,3726],{},[362,3727,3728,3729,3734],{},"Redpanda does not publicly disclose its BYOC pricing, making it difficult to accurately assess its total costs. We refer to information from the whitepaper “",[54,3730,3733],{"href":3731,"rel":3732},"https:\u002F\u002Fwww.redpanda.com\u002Fresources\u002Fredpanda-vs-confluent-performance-tco-benchmark-report#form",[263],"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group.","” for estimation purposes. Based on the Tier-8 pricing model in the whitepaper,  the estimated cost to support a 5GB\u002Fs workload would be $1.12 million per year ($93,333 per month). However, since this calculation is based on an estimation, we will revisit and refine the cost assessment once Redpanda publishes its BYOC pricing.",[47,3736,3737],{},[3178,3738],{"alt":17,"src":3739},"\u002Fimgs\u002Fblogs\u002F679c602dc8a9859eed89a0ef_AD_4nXdbcO8vsNNPy4GtkNLlmNKf22fjxRvzLzH7CtOna1L08sTbvnZx3HhufeFqc1w4K2gEF7lxO2IR5supotxebAiGnA07Qa8Yr3Rd1pVK2LYKK4WurlJGwgdwwucZIFoF-N_2oBjY.png",[47,3741,3742],{},[3178,3743],{"alt":17,"src":3744},"\u002Fimgs\u002Fblogs\u002F679c602d6bc1c2287e012540_AD_4nXfcHZnLfjbjIr3ZAgoQXT9dwP3aQCOQPmGZZJUtpNZSwE6qY6M3yehIaBxCwxEIeu5PVdUPY0zhyjnow26YfgjdYgSG4GnV9ibxu0YWTIpwng6z_F6FUGJMpERMKtpsFESzXSN_Sw.png",[359,3746,3747,3750],{},[362,3748,3749],{},"When estimating the storage costs for Kafka and Redpanda, we assume the use of HDD storage at $0.045\u002FGB, based on the premise that both systems can fully utilize disk bandwidth without incurring the higher costs associated with GP2 or GP3 volumes. However, in practice, many users opt for GP2 or GP3, significantly increasing the total storage cost for Kafka and Redpanda.",[362,3751,3752],{},"Unlike disk-based solutions, S3 storage does not require capacity preallocation—Ursa only incurs costs for the actual data stored. This contrasts with Kafka and Redpanda, where preallocating storage can drive up expenses. As a result, the real-world storage costs for Kafka and Redpanda are often 50% higher than the estimates above.",[39,3754,3756],{"id":3755},"conclusion","Conclusion",[47,3758,3759],{},"Ursa represents a transformative shift in streaming data infrastructure, offering cost efficiency, scalability, and flexibility without compromising durability or reliability. By leveraging a leaderless architecture and eliminating inter-zone data replication, Ursa reduces total cost of ownership by over 90% compared to traditional leader-based streaming engines like Kafka and Redpanda. Its direct integration with cloud storage and scalable metadata & index management via Oxia ensure high availability and simplified infrastructure management.",[31,3761,3763],{"id":3762},"balancing-latency-and-cost","Balancing Latency and Cost",[47,3765,3766,3770],{},[54,3767,3769],{"href":3768},"\u002Fblog\u002Fcap-theorem-for-data-streaming","Ursa trades off slightly higher latency for ultra low cost",", making it an ideal choice for the majority of streaming workloads, especially those that prioritize throughput and cost savings over ultra-low latency. Meanwhile, StreamNative’s BookKeeper-based engine remains the preferred solution for real-time, latency-sensitive applications. By combining these two approaches, StreamNative empowers customers with the flexibility to choose the right engine for their specific needs—whether it's maximizing cost savings or achieving ultra low-latency real-time performance.",[31,3772,3774],{"id":3773},"the-future-of-streaming-infrastructure","The Future of Streaming Infrastructure",[47,3776,3777],{},"In an era where data fuels AI, analytics, and real-time decision-making, managing infrastructure costs is critical to sustaining innovation. Ursa is not just a cost-cutting alternative—it is a forward-thinking, lakehouse-native platform that redefines how modern data streaming infrastructure should be built and operated.",[47,3779,3780,3781,3786],{},"Whether your priority is reducing costs, improving flexibility, or ingesting massive data into lakehouses, Ursa delivers a future-proof solution for the evolving demands of real-time data streaming. ",[54,3782,3785],{"href":3783,"rel":3784},"https:\u002F\u002Fconsole.streamnative.cloud\u002F",[263],"Get started"," with StreamNative Ursa today!",[3788,3789,3791],"h1",{"id":3790},"references","References",[47,3793,3794,2166,3796],{},[1627,3795,3256],{},[54,3797,3798],{"href":3798},"\u002Fblog\u002Fintroducing-oxia-scalable-metadata-and-coordination",[47,3800,3801,2166,3803],{},[1627,3802,686],{},[54,3804,3205],{"href":3205},[47,3806,3807,2166,3810],{},[1627,3808,3809],{},"StreamNative pricing",[54,3811,3812],{"href":3812,"rel":3813},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fbilling-overview",[263],[47,3815,3816,2166,3819],{},[1627,3817,3818],{},"WarpStream pricing",[54,3820,3821],{"href":3821,"rel":3822},"https:\u002F\u002Fwww.warpstream.com\u002Fpricing#pricingfaqs",[263],[47,3824,3825,2166,3828],{},[1627,3826,3827],{},"AWS S3 pricing",[54,3829,3830],{"href":3830,"rel":3831},"https:\u002F\u002Faws.amazon.com\u002Fs3\u002Fpricing\u002F",[263],[47,3833,3834,2166,3837],{},[1627,3835,3836],{},"AWS EBS pricing",[54,3838,3839],{"href":3839,"rel":3840},"https:\u002F\u002Faws.amazon.com\u002Febs\u002Fpricing\u002F",[263],[47,3842,3843,2166,3846],{},[1627,3844,3845],{},"AWS MSK pricing",[54,3847,3848],{"href":3848,"rel":3849},"https:\u002F\u002Faws.amazon.com\u002Fmsk\u002Fpricing\u002F",[263],[47,3851,3852,2166,3855],{},[1627,3853,3854],{},"The Brutal Truth about Kafka Cost Calculators",[54,3856,3677],{"href":3677,"rel":3857},[263],[47,3859,3860,2166,3863],{},[1627,3861,3862],{},"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group",[54,3864,3731],{"href":3731,"rel":3865},[263],{"title":17,"searchDepth":18,"depth":18,"links":3867},[3868,3869,3870,3875,3879,3880,3889,3892],{"id":3162,"depth":18,"text":3163},{"id":3199,"depth":18,"text":3200},{"id":3223,"depth":18,"text":3224,"children":3871},[3872,3873,3874],{"id":3235,"depth":278,"text":3236},{"id":3260,"depth":278,"text":3261},{"id":3281,"depth":278,"text":3282},{"id":3305,"depth":18,"text":3306,"children":3876},[3877,3878],{"id":3309,"depth":278,"text":3310},{"id":3324,"depth":278,"text":3325},{"id":3365,"depth":18,"text":3366},{"id":3377,"depth":18,"text":3378,"children":3881},[3882,3883,3884,3885,3886,3887,3888],{"id":3384,"depth":278,"text":3385},{"id":3430,"depth":278,"text":3431},{"id":3448,"depth":278,"text":3449},{"id":3495,"depth":278,"text":3496},{"id":3523,"depth":278,"text":3524},{"id":3541,"depth":278,"text":3542},{"id":3556,"depth":278,"text":3557},{"id":3601,"depth":18,"text":3602,"children":3890},[3891],{"id":3641,"depth":278,"text":3642},{"id":3755,"depth":18,"text":3756,"children":3893},[3894,3895],{"id":3762,"depth":278,"text":3763},{"id":3773,"depth":278,"text":3774},"2025-01-31","Discover how Ursa achieves 5GB\u002Fs Kafka workloads at just 5% of the cost of traditional streaming engines like Redpanda and AWS MSK. See our benchmark results comparing infrastructure costs, total cost of ownership (TCO), and performance across leading Kafka vendors.","\u002Fimgs\u002Fblogs\u002F679c6593d25099b1cdcec4ca_image-31.png",{},"\u002Fblog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour","30 min",{"title":3138,"description":3897},"blog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour",[990,685,301],"A0o_2xdJiLI6rf6xj4RKsxJNo_A6QN2fYzCp6gaLrFw",1775235748366]