[{"data":1,"prerenderedAt":1556},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":24,"navbar-pricing-featured":306,"blog-\u002Fblog\u002Fpulsar-vs-kafka-part-2-adoption-use-cases-differentiators-and-community":1086,"blog-authors-\u002Fblog\u002Fpulsar-vs-kafka-part-2-adoption-use-cases-differentiators-and-community":1489,"related-\u002Fblog\u002Fpulsar-vs-kafka-part-2-adoption-use-cases-differentiators-and-community":1537},{"id":4,"title":5,"date":6,"dismissible":7,"extension":8,"link":9,"link2":10,"linkText":11,"linkText2":12,"meta":13,"stem":21,"variant":22,"__hash__":23},"banners\u002Fbanners\u002Flakestream-ufk-launch.md","StreamNative Introduces Lakestream Architecture and Launches Native Kafka Service","2026-04-07",true,"md","\u002Fblog\u002Ffrom-streams-to-lakestreams","https:\u002F\u002Fconsole.streamnative.cloud\u002Fsignup?from=banner_lakestream-launch","Read Announcement","Sign Up Now",{"body":14},{"type":15,"value":16,"toc":17},"minimark",[],{"title":18,"searchDepth":19,"depth":19,"links":20},"",2,[],"banners\u002Flakestream-ufk-launch","default","zRueBGutATZB0ZnFFHwaEV7F0Di4tnZUHhgOiI4cu6k",{"id":25,"title":26,"authors":27,"body":29,"category":289,"createdAt":290,"date":291,"description":292,"extension":8,"featured":7,"image":293,"isDraft":294,"link":290,"meta":295,"navigation":7,"order":296,"path":297,"readingTime":298,"relatedResources":290,"seo":299,"stem":300,"tags":301,"__hash__":305},"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",[28],"David Kjerrumgaard",{"type":15,"value":30,"toc":276},[31,39,47,51,67,73,78,81,87,102,109,115,118,124,127,134,140,143,146,157,163,169,172,175,178,184,191,194,197,204,207,210,224,229,233,237,241,245,249,251,268,270],[32,33,35],"h3",{"id":34},"receives-highest-possible-scores-in-both-the-messaging-and-resource-optimization-criteria",[36,37,38],"em",{},"Receives Highest Possible Scores in BOTH the Messaging and Resource Optimization Criteria",[40,41,43],"h2",{"id":42},"introduction",[44,45,46],"strong",{},"Introduction",[48,49,50],"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.",[48,52,53,54,63,64],{},"Today, we're excited to announce that Forrester Research has named StreamNative as a Contender in its evaluation, ",[55,56,58],"a",{"href":57},"\u002Freports\u002Frecognized-in-the-forrester-wave-tm-streaming-data-platforms-q4-2025",[36,59,60],{},[44,61,62],{},"The Forrester Wave™: Streaming Data Platforms, Q4 2025",". This report evaluated 15 top streaming data platform providers, and we're proud to share that ",[44,65,66],{},"StreamNative received the highest scores possible—5 out of 5—in both the Messaging and Resource Optimization criteria.",[48,68,69,70],{},"***Forrester's Take: ***",[36,71,72],{},"\"StreamNative is a good fit for enterprises that want an Apache Pulsar implementation that is also compatible with Kafka APIs.\"",[48,74,75],{},[36,76,77],{},"— The Forrester Wave™: Streaming Data Platforms, Q4 2025",[48,79,80],{},"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.",[40,82,84],{"id":83},"trusted-by-industry-leaders",[44,85,86],{},"Trusted by Industry Leaders",[48,88,89,90,93,94,97,98,101],{},"Companies across industries are already leveraging StreamNative to drive real-time outcomes. Global enterprises like ",[44,91,92],{},"Cisco"," rely on StreamNative to handle massive IoT telemetry, supporting 245 million+ connected devices. Martech leaders such as ",[44,95,96],{},"Iterable"," process billions of events per day with StreamNative for hyper-personalized customer engagement. And in financial services, ",[44,99,100],{},"FICO"," trusts StreamNative to power its real-time fraud detection and analytics pipelines with a secure, scalable streaming backbone.",[48,103,104,105,108],{},"The Forrester report notes that, “",[36,106,107],{},"Customers appreciate the lower infrastructure costs that result from StreamNative’s cost-efficient, Kafka-compatible architecture. Customers note excellent support responsiveness…","”",[40,110,112],{"id":111},"modern-cloud-native-architecture-built-for-scale",[44,113,114],{},"Modern, Cloud-Native Architecture Built for Scale",[48,116,117],{},"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.",[48,119,120,121,108],{},"Forrester's evaluation described that “",[36,122,123],{},"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.",[48,125,126],{},"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.",[48,128,129,130,133],{},"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 ",[36,131,132],{},"\"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.",[40,135,137],{"id":136},"open-source-foundation-and-pulsar-expertise",[44,138,139],{},"Open Source Foundation and Pulsar Expertise",[48,141,142],{},"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.",[48,144,145],{},"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.",[48,147,148,149,152,153,156],{},"Forrester's assessment noted that StreamNative’s “",[36,150,151],{},"events-driven agents, extensibility, and performance architecture are solid,","” and we're continuing to build on that foundation. ",[44,154,155],{},"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.",[48,158,159,160],{},"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 ",[36,161,162],{},"\"investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.\"",[40,164,166],{"id":165},"powering-real-time-use-cases-across-industries",[44,167,168],{},"Powering Real-Time Use Cases Across Industries",[48,170,171],{},"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.",[48,173,174],{},"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.",[48,176,177],{},"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.",[40,179,181],{"id":180},"continuing-to-innovate-ursa-orca-and-the-road-ahead",[44,182,183],{},"Continuing to Innovate: Ursa, Orca, and the Road Ahead",[48,185,186,187,190],{},"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 ",[44,188,189],{},"provide a unified platform that not only handles today's streaming needs but also anticipates the emerging requirements of tomorrow",".",[48,192,193],{},"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.",[48,195,196],{},"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.",[48,198,199,200,203],{},"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. ",[44,201,202],{},"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.",[48,205,206],{},"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!",[208,209],"hr",{},[32,211,213],{"id":212},"streamnative-in-the-forrester-wave-evaluation-findings",[44,214,215,216,223],{},"StreamNative in ",[44,217,218],{},[55,219,220],{"href":57},[44,221,222],{},"The Forrester Wave™",": Evaluation Findings",[225,226,228],"h5",{"id":227},"recognized-as-a-contender-among-15-streaming-data-platform-providers","• Recognized as a Contender among 15 streaming data platform providers",[225,230,232],{"id":231},"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",[225,234,236],{"id":235},"cited-as-the-primary-platform-for-enterprises-wishing-to-implement-pulsar","• Cited as the primary platform for enterprises wishing to implement Pulsar",[225,238,240],{"id":239},"noted-for-excelling-at-messaging-and-resource-optimization","• Noted for excelling at messaging and resource optimization",[225,242,244],{"id":243},"customers-cited-lower-infrastructure-costs-and-excellent-support-responsiveness","• Customers cited lower infrastructure costs and excellent support responsiveness",[225,246,248],{"id":247},"recognized-for-supporting-event-driven-architectures-with-robust-scalability","• Recognized for supporting event-driven architectures with robust scalability",[208,250],{},[252,253,255,256,259,260,190],"h6",{"id":254},"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: **",[36,257,258],{},"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 *",[55,261,265],{"href":262,"rel":263},"https:\u002F\u002Fwww.forrester.com\u002Fabout-us\u002Fobjectivity\u002F",[264],"nofollow",[36,266,267],{},"here",[208,269],{},[252,271,273],{"id":272},"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",[36,274,275],{},"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":18,"searchDepth":19,"depth":19,"links":277},[278,280,281,282,283,284,285],{"id":34,"depth":279,"text":38},3,{"id":42,"depth":19,"text":46},{"id":83,"depth":19,"text":86},{"id":111,"depth":19,"text":114},{"id":136,"depth":19,"text":139},{"id":165,"depth":19,"text":168},{"id":180,"depth":19,"text":183,"children":286},[287],{"id":212,"depth":279,"text":288},"StreamNative in The Forrester Wave™: Evaluation Findings","Company",null,"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":26,"description":292},"blog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025",[302,303,304],"Announcements","Real-Time","Forrester","sOeeJtEO3O-IIfTPJjY1AFOMawZ_rf8FOH8A98NEKgU",{"id":307,"title":308,"authors":309,"body":314,"category":1073,"createdAt":290,"date":1074,"description":1075,"extension":8,"featured":7,"image":1076,"isDraft":294,"link":290,"meta":1077,"navigation":7,"order":296,"path":1078,"readingTime":1079,"relatedResources":290,"seo":1080,"stem":1081,"tags":1082,"__hash__":1085},"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",[310,311,312,313],"Matteo Meril","Neng Lu","Hang Chen","Penghui Li",{"type":15,"value":315,"toc":1043},[316,319,322,325,328,331,335,338,348,354,357,365,370,374,381,384,387,395,399,402,407,411,414,417,420,423,432,436,439,450,453,457,460,463,474,477,481,485,493,496,500,508,537,541,544,549,553,556,560,563,566,571,580,585,588,591,602,606,609,620,624,627,630,635,638,667,671,673,679,682,687,692,695,699,713,717,728,732,747,756,767,770,773,777,780,783,794,797,800,803,808,813,817,821,838,842,856,861,865,876,879,895,899,910,915,920,928,932,935,939,946,950,953,962,967,976,982,991,1000,1009,1018,1027,1035],[48,317,318],{},"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.",[48,320,321],{},"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.",[48,323,324],{},"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.",[48,326,327],{},"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.",[48,329,330],{},"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.",[40,332,334],{"id":333},"key-benchmark-findings","Key Benchmark Findings",[48,336,337],{},"Ursa delivered 5 GB\u002Fs of sustained throughput at an infrastructure cost of just $54 per hour. For comparison:",[339,340,341,345],"ul",{},[342,343,344],"li",{},"MSK: $303 per hour → 5.6x more expensive compared to Ursa",[342,346,347],{},"Redpanda: $988 per hour → 18x more expensive compared to Ursa",[48,349,350],{},[351,352],"img",{"alt":18,"src":353},"\u002Fimgs\u002Fblogs\u002F679c71b67d9046f26edc7977_AD_4nXfvTqyBNUBu2lObdkKAx-5UNkpNP8UYULLZyOcixE6z99VMZUUEsUqWjzexI7vjyNGRNSAUoM9smYvdTP55ctAhIbrs5lmQgcSVMWdaoigbWouCl95DVSQsxooY-qqfGcYqS4g4zA.png",[48,355,356],{},"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:",[339,358,359,362],{},[342,360,361],{},"50% cheaper than Confluent WarpStream",[342,363,364],{},"85% cheaper than MSK and Redpanda",[48,366,367],{},[351,368],{"alt":18,"src":369},"\u002Fimgs\u002Fblogs\u002F679c602d77e9c706de5343b8_AD_4nXeDv8rrv_C1CTCCiqYo1zpvlGYbdBk1r0VEqovAPu22iFMQZgh54Hfw9PBMLzM7jDFxKwAFDxbdG0np4XVk_tGsWhEKMloLRcmmea7lvueCx-0cFsyaE3Mya4Mxc1Dox95A6JEc.png",[40,371,373],{"id":372},"ursa-highly-cost-efficient-data-streaming-at-scale","Ursa: Highly Cost-Efficient Data Streaming at Scale",[48,375,376,380],{},[55,377,379],{"href":378},"\u002Fblog\u002Fursa-reimagine-apache-kafka-for-the-cost-conscious-data-streaming","Ursa"," 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.",[48,382,383],{},"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.",[48,385,386],{},"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:",[339,388,389,392],{},[342,390,391],{},"Eliminating inter-zone traffic costs via a leaderless architecture.",[342,393,394],{},"Replacing costly inter-zone replication with direct writes to cloud storage using open lakehouse formats.",[40,396,398],{"id":397},"how-ursa-eliminates-inter-zone-traffic","How Ursa Eliminates Inter-Zone Traffic",[48,400,401],{},"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.",[48,403,404],{},[351,405],{"alt":18,"src":406},"\u002Fimgs\u002Fblogs\u002F679c602e21b3571bb7117dca_AD_4nXd7Oahc77NjRLNvA9clLt0tsyU6MrIqVibFYv5pW5giTIcCHPr3EA_yTGzfVEUIVO3VXK56qWK8zmBCp5lY0E_4nmlWIPFrHjtHylA5NhwELjn-UB0fLG2h_kbrxrc7Cs_edvveNA.png",[32,408,410],{"id":409},"leaderless-architecture","Leaderless architecture",[48,412,413],{},"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.",[48,415,416],{},"Pros of Leader-Based Architectures:\n✔ Maintains message ordering via local sequence IDs\n✔ Delivers low latency and high performance through message caching",[48,418,419],{},"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",[48,421,422],{},"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.",[48,424,425,426,431],{},"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 ",[55,427,430],{"href":428,"rel":429},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Foxia",[264],"Oxia",", a scalable metadata and index service created by StreamNative in 2022.",[32,433,435],{"id":434},"oxia-the-metadata-layer-enabling-leaderless-architecture","Oxia: The Metadata Layer Enabling Leaderless Architecture",[48,437,438],{},"Ensuring message ordering in a leaderless architecture is complex, but Ursa solves this with Oxia:",[339,440,441,444,447],{},[342,442,443],{},"Handles millions of metadata\u002Findex operations per second",[342,445,446],{},"Generates sequential IDs to maintain strict message ordering",[342,448,449],{},"Optimized for Kubernetes with horizontal scalability",[48,451,452],{},"Producers and consumers can connect to any broker within their local AZ, eliminating inter-zone traffic costs while maintaining performance through localized caching.",[32,454,456],{"id":455},"zero-interzone-data-replication","Zero interzone data replication",[48,458,459],{},"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.",[48,461,462],{},"Ursa avoids these costs by writing data directly to cloud storage (e.g., AWS S3, Google GCS):",[339,464,465,468,471],{},[342,466,467],{},"Built-In Resilience: Cloud storage inherently offers high availability and fault tolerance without inter-zone traffic fees.",[342,469,470],{},"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).",[342,472,473],{},"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.",[48,475,476],{},"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.",[40,478,480],{"id":479},"how-we-ran-a-5-gbs-test-with-ursa","How We Ran a 5 GB\u002Fs Test with Ursa",[32,482,484],{"id":483},"ursa-cluster-deployment","Ursa Cluster Deployment",[339,486,487,490],{},[342,488,489],{},"9 brokers across 3 availability zones, each on m6i.8xlarge (Fixed 12.5 Gbps bandwidth, 32 vCPU cores, 128 GB memory).",[342,491,492],{},"Oxia cluster (metadata store) with 3 nodes of m6i.8xlarge, distributed across three availability zones (AZs).",[48,494,495],{},"During peak throughput (5 GB\u002Fs), each broker’s network usage was about 10 Gbps.",[32,497,499],{"id":498},"openmessaging-benchmark-workers-configuration","OpenMessaging Benchmark Workers & Configuration",[48,501,502,503,507],{},"The OpenMessaging Benchmark(OMB) Framework is a suite of tools that make it easy to benchmark distributed messaging systems in the cloud. Please check ",[55,504,505],{"href":505,"rel":506},"https:\u002F\u002Fopenmessaging.cloud\u002Fdocs\u002Fbenchmarks\u002F",[264]," for details.",[339,509,510,525,534],{},[342,511,512,513,518,519,524],{},"12 OMB workers: 6 for ",[55,514,517],{"href":515,"rel":516},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002Fd1094122270775e4f1580947f80c5055",[264],"producers",", 6 for ",[55,520,523],{"href":521,"rel":522},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F06bada89381fb77a7862e1b4c1d8963d",[264],"consumers"," across 3 availability zones, on m6i.8xlarge instances. Each worker is configured with 12 CPU cores and 48 GB memory.",[342,526,527,528,533],{},"Sample YAML ",[55,529,532],{"href":530,"rel":531},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F204c1f26c4d44a218ae235bf2de99904",[264],"scripts"," provided for Kafka-compatible configuration and rate limits.",[342,535,536],{},"Achieved consistent 5 GB\u002Fs publish\u002Fsubscribe throughput.",[40,538,540],{"id":539},"ursa-benchmark-tests-results","Ursa Benchmark Tests & Results",[48,542,543],{},"The following diagram demonstrates that Ursa can consistently handle 5 GB\u002Fs of traffic, fully saturating the network across all broker nodes.",[48,545,546],{},[351,547],{"alt":18,"src":548},"\u002Fimgs\u002Fblogs\u002F679c602d7b261bac1113f7d6_AD_4nXdDPsRc3koXICiFF0bqSmGWbJt_RlUy4FE3ruuWOfbCfpcqZ1dejjqGbkaCJv2hQFL1nirRouBVRW2l5uMWBvY9naMqGB_wHcLI14dBM0f85TXhmdm3UxEv1yGX9Y4hf5FttSkZew.png",[40,550,552],{"id":551},"comparing-infrastructure-cost","Comparing Infrastructure Cost",[48,554,555],{},"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.",[32,557,559],{"id":558},"test-setup-key-assumptions","Test Setup & Key Assumptions",[48,561,562],{},"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.",[48,564,565],{},"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:",[339,567,568],{},[342,569,570],{},"9 × m6i.8xlarge instances",[48,572,573,574,579],{},"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",[55,575,578],{"href":576,"rel":577},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fmsk-provision-throughput-management.html#throughput-bottlenecks",[264]," AWS documentation",". Given this constraint, achieving 5 GB\u002Fs throughput with a replication factor of 3 required the following setup:",[339,581,582],{},[342,583,584],{},"15 × kafka.m7g.8xlarge (32 vCPUs, 128 GB memory, 15 Gbps network, 4000 GiB EBS).",[48,586,587],{},"This configuration was necessary to work around MSK's storage bandwidth limitations, ensuring a comparable cost basis to other evaluated streaming engines.",[48,589,590],{},"Additional key assumptions include:",[339,592,593,596,599],{},[342,594,595],{},"Inter-AZ producer traffic: For leader-based engines, two-thirds of producer-to-broker traffic crosses AZs due to leader distribution.",[342,597,598],{},"Consumer optimizations: Follower fetch is enabled across all tests, eliminating inter-AZ consumer traffic.",[342,600,601],{},"Storage cost exclusions: This benchmark only evaluates streaming costs, assuming no long-term data retention.",[32,603,605],{"id":604},"inter-broker-replication-costs","Inter-Broker Replication Costs",[48,607,608],{},"Inter-broker (cross-AZ) replication is a major cost driver for data streaming engines:",[339,610,611,614,617],{},[342,612,613],{},"RedPanda: Inter-broker replication is not free, leading to substantial costs when data must be copied across multiple availability zones.",[342,615,616],{},"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.",[342,618,619],{},"Ursa: No inter-broker replication costs due to its leaderless architecture, eliminating inter-zone replication costs entirely.",[32,621,623],{"id":622},"zone-affinity-reducing-inter-az-costs","Zone Affinity: Reducing Inter-AZ Costs",[48,625,626],{},"We evaluated zone affinity mechanisms to further reduce inter-AZ data transfer costs.",[48,628,629],{},"Consumers:",[339,631,632],{},[342,633,634],{},"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",[48,636,637],{},"Producers:",[339,639,640,649,658],{},[342,641,642,643,648],{},"Kafka protocol lacks an easy way to enforce producer AZ affinity (though ",[55,644,647],{"href":645,"rel":646},"https:\u002F\u002Fcwiki.apache.org\u002Fconfluence\u002Fdisplay\u002FKAFKA\u002FKIP-1123:+Rack-aware+partitioning+for+Kafka+Producer",[264],"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).",[342,650,651,652,657],{},"Redpanda recently introduced ",[55,653,656],{"href":654,"rel":655},"https:\u002F\u002Fdocs.redpanda.com\u002Fredpanda-cloud\u002Fdevelop\u002Fproduce-data\u002Fleader-pinning\u002F",[264],"leader pinning",", but this only benefits setups where producers are confined to a single AZ—not applicable to our multi-AZ benchmark.",[342,659,660,661,666],{},"Ursa is the only system in this test with ",[55,662,665],{"href":663,"rel":664},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fconfig-kafka-client#eliminate-cross-az-networking-traffic",[264],"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.",[32,668,670],{"id":669},"cost-comparison-results","Cost Comparison Results",[48,672,337],{},[339,674,675,677],{},[342,676,344],{},[342,678,347],{},[48,680,681],{},"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.",[48,683,684],{},[351,685],{"alt":18,"src":686},"\u002Fimgs\u002Fblogs\u002F679c72208198ca36a352f228_AD_4nXeeZuM8T-xBlD4Vf3j67K618n08qh8wIDLLtiLJG0ssA1Wj1V26u7wIDTX9sqLrtw8mB2c299dwzarGen62CG0Vh7nWstn5qbPGFcBaKJYEepTsLr5fHWv1U8uqbg8Y0UOK6fJ7.png",[48,688,689],{},[351,690],{"alt":18,"src":691},"\u002Fimgs\u002Fblogs\u002F679c625978031f40229de484_AD_4nXdLkLLJ30KKr-_A_rN1j8akVwBYacAWIPzWHoOReJF421890kfByZoQQxkLczihVSmiw5Q9J51-V9I2SEKITbwsYnANDDTlAVL5nQ_jfaHNTe9VEWhSoa7DZooCnilDYL6l6msmJg.png",[48,693,694],{},"The detailed infrastructure cost calculations for each data streaming engine are listed below:",[32,696,698],{"id":697},"streamnative-ursa","StreamNative - Ursa",[339,700,701,704,707,710],{},[342,702,703],{},"Server EC2 costs: 9 * $1.536\u002Fhr = $14",[342,705,706],{},"Client EC2 costs: 9 * $1.536\u002Fhr =$14",[342,708,709],{},"S3 write requests costs: 1350 r\u002Fs * $0.005\u002F1000r * 3600s = $24",[342,711,712],{},"S3 read requests costs: 1350 r\u002Fs * $0.0004\u002F1000r * 3600s = $2",[32,714,716],{"id":715},"aws-msk","AWS MSK",[339,718,719,722,725],{},[342,720,721],{},"Server EC2 costs: 15 * $3.264\u002Fhr = $49",[342,723,724],{},"Client side EC2 costs: 9 * $1.536\u002Fhr =$14",[342,726,727],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FG(in+out) * 3600 = $240",[32,729,731],{"id":730},"redpanda","RedPanda",[339,733,734,736,738,741,744],{},[342,735,703],{},[342,737,706],{},[342,739,740],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FGB(in+out) * 3600 = $240",[342,742,743],{},"Interzone traffic - replication: 10GB\u002Fs * $0.02\u002FGB(in+out) * 3600 = $720",[342,745,746],{},"Interzone traffic - broker to consumer: $0 (fetch from local zone)",[48,748,749,750,755],{},"Please note that we were unable to test ",[55,751,754],{"href":752,"rel":753},"https:\u002F\u002Fwww.redpanda.com\u002Fblog\u002Fcloud-topics-streaming-data-object-storage",[264],"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.",[339,757,758,764],{},[342,759,760,763],{},[55,761,647],{"href":645,"rel":762},[264]," (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).",[342,765,766],{},"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.",[48,768,769],{},"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.",[48,771,772],{},"We may revisit this comparison as more details become available.",[40,774,776],{"id":775},"comparing-total-cost-of-ownership","Comparing Total Cost of Ownership",[48,778,779],{},"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.",[48,781,782],{},"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:",[339,784,785,788,791],{},[342,786,787],{},"Ursa ($164,353\u002Fmonth) is: 50% cheaper than Confluent WarpStream ($337,068\u002Fmonth)",[342,789,790],{},"85% cheaper than AWS MSK ($1,115,251\u002Fmonth)",[342,792,793],{},"86% cheaper than Redpanda ($1,202,853\u002Fmonth)",[48,795,796],{},"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.",[48,798,799],{},"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.",[48,801,802],{},"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.",[48,804,805],{},[351,806],{"alt":18,"src":807},"\u002Fimgs\u002Fblogs\u002F679c602d194800c9206d9d58_AD_4nXcFlf755xgyz7htxhMhBV5fGrsxy642mQNodt61DTok_z1dwkw5A6lkO5hatXVneCaB0anbZPAyvLI3MlIMuQEYLEACHHvQMOr5UfaB37dfzkdqewDEvcT-20VGd_zzvJsuA00zGA.png",[48,809,810],{},[351,811],{"alt":18,"src":812},"\u002Fimgs\u002Fblogs\u002F679c62594e9c2e629fae73aa_AD_4nXeU6cOgItnjLsEZCOf13TEvMY_SHWWIxYP2OYUj-B1GUPyWO78OG08K_v03hwYSVcg06f9dqDiGmdwy76vynjmiDGL5bluZ5_XF4nSU_r59oOZdfViXndXt6s11vVOY7qwfZN8v.png",[32,814,816],{"id":815},"cost-breakdown","Cost Breakdown",[818,819,820],"h4",{"id":697},"StreamNative – Ursa",[339,822,823,826,829,832,835],{},[342,824,825],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9,953.28",[342,827,828],{},"S3 Write Requests: 1,350 r\u002Fs × $0.005\u002F1,000 r × 3,600 s × 24 hr × 30 days = $17,496",[342,830,831],{},"S3 Read Requests: 1,350 r\u002Fs × $0.0004\u002F1,000 r × 3,600 s × 24 hr × 30 days = $1,400",[342,833,834],{},"S3 Storage Costs: 5 GB\u002Fs × $0.021\u002FGB × 3,600 s × 24 hr × 7 days = $63,504",[342,836,837],{},"Vendor Cost: 200 ETU × $0.50\u002Fhr × 24 hr × 30 days = $72,000",[818,839,841],{"id":840},"warpstream","WarpStream",[339,843,844,847],{},[342,845,846],{},"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.",[342,848,849,850,855],{},"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 “",[55,851,854],{"href":852,"rel":853},"https:\u002F\u002Fbigdata.2minutestreaming.com\u002Fp\u002Fthe-brutal-truth-about-apache-kafka-cost-calculators",[264],"The Brutal Truth About Kafka Cost Calculators","”. To ensure transparency, we have documented the pricing as of January 29, 2025.",[48,857,858],{},[351,859],{"alt":18,"src":860},"\u002Fimgs\u002Fblogs\u002F679c602e42713e0028e9af5e_AD_4nXcu5_VWTLu9jRYs6zX1MBAOtLQEo5gyfNSWPcbpnQHXTa8qNCFAXezRR2E8daygzYTTwd4dhJjaLaLM8C6y_3OGbu2NS7pdvEv3a8-ptNKOg7AeKnYqPQCAYvQ5EuxzuI3JYIvY.png",[818,862,864],{"id":863},"msk","MSK",[339,866,867,870,873],{},[342,868,869],{},"EC2 (Server): 15 * $3.264\u002Fhr × 24 hr × 30 days = $35,251",[342,871,872],{},"Interzone Traffic (Client-Server): 5 GB\u002Fs × ⅔ × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $172,800",[342,874,875],{},"Storage: 5 GB\u002Fs × $0.1\u002FGB-month × 3,600 s × 24 hr × 7 days * 3 replicas = $907,200",[818,877,731],{"id":878},"redpanda-1",[339,880,881,884,886,889,892],{},[342,882,883],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9953",[342,885,872],{},[342,887,888],{},"Interzone Traffic (Replication): 5 GB\u002Fs × 2 × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $518,400",[342,890,891],{},"Storage: 5 GB\u002Fs × $0.045\u002FGB-month(st1) × 3,600 s × 24 hr × 7 days * 3 replicas = $408,240",[342,893,894],{},"Vendor Cost: $93,333 per month (based on limited information. See additional notes below).",[818,896,898],{"id":897},"additional-notes","Additional Notes",[339,900,901],{},[342,902,903,904,909],{},"Redpanda does not publicly disclose its BYOC pricing, making it difficult to accurately assess its total costs. We refer to information from the whitepaper “",[55,905,908],{"href":906,"rel":907},"https:\u002F\u002Fwww.redpanda.com\u002Fresources\u002Fredpanda-vs-confluent-performance-tco-benchmark-report#form",[264],"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.",[48,911,912],{},[351,913],{"alt":18,"src":914},"\u002Fimgs\u002Fblogs\u002F679c602dc8a9859eed89a0ef_AD_4nXdbcO8vsNNPy4GtkNLlmNKf22fjxRvzLzH7CtOna1L08sTbvnZx3HhufeFqc1w4K2gEF7lxO2IR5supotxebAiGnA07Qa8Yr3Rd1pVK2LYKK4WurlJGwgdwwucZIFoF-N_2oBjY.png",[48,916,917],{},[351,918],{"alt":18,"src":919},"\u002Fimgs\u002Fblogs\u002F679c602d6bc1c2287e012540_AD_4nXfcHZnLfjbjIr3ZAgoQXT9dwP3aQCOQPmGZZJUtpNZSwE6qY6M3yehIaBxCwxEIeu5PVdUPY0zhyjnow26YfgjdYgSG4GnV9ibxu0YWTIpwng6z_F6FUGJMpERMKtpsFESzXSN_Sw.png",[339,921,922,925],{},[342,923,924],{},"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.",[342,926,927],{},"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.",[40,929,931],{"id":930},"conclusion","Conclusion",[48,933,934],{},"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.",[32,936,938],{"id":937},"balancing-latency-and-cost","Balancing Latency and Cost",[48,940,941,945],{},[55,942,944],{"href":943},"\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.",[32,947,949],{"id":948},"the-future-of-streaming-infrastructure","The Future of Streaming Infrastructure",[48,951,952],{},"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.",[48,954,955,956,961],{},"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. ",[55,957,960],{"href":958,"rel":959},"https:\u002F\u002Fconsole.streamnative.cloud\u002F",[264],"Get started"," with StreamNative Ursa today!",[963,964,966],"h1",{"id":965},"references","References",[48,968,969,972,973],{},[970,971,430],"span",{}," ",[55,974,975],{"href":975},"\u002Fblog\u002Fintroducing-oxia-scalable-metadata-and-coordination",[48,977,978,972,980],{},[970,979,379],{},[55,981,378],{"href":378},[48,983,984,972,987],{},[970,985,986],{},"StreamNative pricing",[55,988,989],{"href":989,"rel":990},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fbilling-overview",[264],[48,992,993,972,996],{},[970,994,995],{},"WarpStream pricing",[55,997,998],{"href":998,"rel":999},"https:\u002F\u002Fwww.warpstream.com\u002Fpricing#pricingfaqs",[264],[48,1001,1002,972,1005],{},[970,1003,1004],{},"AWS S3 pricing",[55,1006,1007],{"href":1007,"rel":1008},"https:\u002F\u002Faws.amazon.com\u002Fs3\u002Fpricing\u002F",[264],[48,1010,1011,972,1014],{},[970,1012,1013],{},"AWS EBS pricing",[55,1015,1016],{"href":1016,"rel":1017},"https:\u002F\u002Faws.amazon.com\u002Febs\u002Fpricing\u002F",[264],[48,1019,1020,972,1023],{},[970,1021,1022],{},"AWS MSK pricing",[55,1024,1025],{"href":1025,"rel":1026},"https:\u002F\u002Faws.amazon.com\u002Fmsk\u002Fpricing\u002F",[264],[48,1028,1029,972,1032],{},[970,1030,1031],{},"The Brutal Truth about Kafka Cost Calculators",[55,1033,852],{"href":852,"rel":1034},[264],[48,1036,1037,972,1040],{},[970,1038,1039],{},"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group",[55,1041,906],{"href":906,"rel":1042},[264],{"title":18,"searchDepth":19,"depth":19,"links":1044},[1045,1046,1047,1052,1056,1057,1066,1069],{"id":333,"depth":19,"text":334},{"id":372,"depth":19,"text":373},{"id":397,"depth":19,"text":398,"children":1048},[1049,1050,1051],{"id":409,"depth":279,"text":410},{"id":434,"depth":279,"text":435},{"id":455,"depth":279,"text":456},{"id":479,"depth":19,"text":480,"children":1053},[1054,1055],{"id":483,"depth":279,"text":484},{"id":498,"depth":279,"text":499},{"id":539,"depth":19,"text":540},{"id":551,"depth":19,"text":552,"children":1058},[1059,1060,1061,1062,1063,1064,1065],{"id":558,"depth":279,"text":559},{"id":604,"depth":279,"text":605},{"id":622,"depth":279,"text":623},{"id":669,"depth":279,"text":670},{"id":697,"depth":279,"text":698},{"id":715,"depth":279,"text":716},{"id":730,"depth":279,"text":731},{"id":775,"depth":19,"text":776,"children":1067},[1068],{"id":815,"depth":279,"text":816},{"id":930,"depth":19,"text":931,"children":1070},[1071,1072],{"id":937,"depth":279,"text":938},{"id":948,"depth":279,"text":949},"StreamNative Cloud","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":308,"description":1075},"blog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour",[1083,1084,303],"TCO","Apache Kafka","A0o_2xdJiLI6rf6xj4RKsxJNo_A6QN2fYzCp6gaLrFw",{"id":1087,"title":1088,"authors":1089,"body":1093,"category":1478,"createdAt":290,"date":1479,"description":1480,"extension":8,"featured":294,"image":290,"isDraft":294,"link":290,"meta":1481,"navigation":7,"order":296,"path":1482,"readingTime":1483,"relatedResources":290,"seo":1484,"stem":1485,"tags":1486,"__hash__":1488},"blogs\u002Fblog\u002Fpulsar-vs-kafka-part-2-adoption-use-cases-differentiators-and-community.md","Pulsar vs Kafka - Part 2 - Adoption, Use Cases, Differentiators, and Community",[1090,1091,1092],"Carolyn King","Addison Higham","Sijie Guo",{"type":15,"value":1094,"toc":1456},[1095,1101,1109,1113,1116,1119,1122,1125,1128,1131,1135,1138,1146,1149,1152,1156,1163,1167,1176,1180,1189,1192,1196,1199,1202,1206,1209,1212,1215,1219,1222,1225,1228,1231,1240,1244,1247,1250,1253,1256,1259,1262,1266,1269,1273,1309,1312,1315,1319,1328,1343,1352,1360,1363,1367,1370,1374,1377,1380,1382,1385,1388,1391,1394,1397,1403,1407,1421,1428,1432,1435,1439],[48,1096,1097],{},[351,1098],{"alt":1099,"src":1100},"pulsar and kafka logo on blue and black background","\u002Fimgs\u002Fblogs\u002F63a377b02be9e607dd3ac516_top.jpeg",[48,1102,1103,1104,1108],{},"This is Part 2 of a two-part series in which we share our perspectives on Pulsar vs. Kafka. In ",[55,1105,1107],{"href":1106},"\u002Fblog\u002Fguide-apache-pulsar-compare-features-architecture-to-apache-kafka","Part 1",", we compared Pulsar and Kafka from an engineering perspective and discussed performance, architecture, and features. In Part 2, we aim to provide a broader business perspective by sharing insights into Pulsar's rapidly growing popularity.",[40,1110,1112],{"id":1111},"intro","INTRO",[48,1114,1115],{},"Data is transforming the business landscape with major industry leaders like Amazon, Uber, and Netflix demonstrating how access to real-time data, data messaging, and processing capabilities can translate to better products and customer experiences, disrupt entire industries, and generate billions in revenue. This need for real-time insights across industries is driving adoption and innovation in the messaging space.",[48,1117,1118],{},"As companies look to adopt real-time streaming solutions for new, innovative applications and to improve their existing systems, business leaders are seeking to better understand the respective advantages and disadvantages associated with the top technologies in the space, namely Pulsar, Kafka, and RabbitMQ.",[48,1120,1121],{},"Today, companies' messaging needs are increasingly complex and many organizations require a more comprehensive solution than RabbitMQ or Kafka can provide on their own. While RabbitMQ is best suited for message queueing and Kafka can manage data pipelines, Pulsar can accomplish both.",[48,1123,1124],{},"Companies that have a need for both types of messaging are increasingly choosing Pulsar for its flexibility, scalability, and ability to simplify operations by delivering multiple messaging functions on the same platform. Pulsar provides unique, sought-after capabilities, such as unified messaging and the ability to build streaming-first applications, which are powering some of today's most advanced companies.",[48,1126,1127],{},"However, because Pulsar is a younger technology, some are less familiar with its capabilities. In this post, we will address some common misconceptions about Pulsar and show Pulsar's growing popularity as evidenced by its rapid growth in adoption, an increase in the number and variety of use cases, and its ever-expanding community. We will also address the risks associated with adopting a new technology and explain why maintaining the status quo presents the risk of being left behind in a quickly changing landscape.",[48,1129,1130],{},"We have chosen to frame our discussion around commonly asked questions.",[40,1132,1134],{"id":1133},"_1-how-mature-is-pulsars-technology-and-has-it-been-tested-in-real-world-applications","#1: How mature is Pulsar's technology and has it been tested in real-world applications?",[48,1136,1137],{},"To provide some insight into Pulsar's maturity and real-world use cases, we'll start with a brief background on its origin and development.",[48,1139,1140,1145],{},[55,1141,1144],{"href":1142,"rel":1143},"https:\u002F\u002Fyahooeng.tumblr.com\u002Fpost\u002F150078336821\u002Fopen-sourcing-pulsar-pub-sub-messaging-at-scale#notes?ref_url=https:\u002F\u002Fyahooeng.tumblr.com\u002Fpost\u002F150078336821\u002Fopen-sourcing-pulsar-pub-sub-messaging-at-scale\u002Fembed#_=_",[264],"Pulsar's development began within Yahoo"," in 2012. It was committed to open source in 2016 and became a top-level Apache project in 2018. It has enterprise support from StreamNative. Pulsar enjoys several advantages as a newer entrant into the messaging space. Specifically, its developers at Yahoo had worked on Kafka and other traditional messaging technologies previously and knew the shortcomings associated with these platforms first-hand. As a result, they designed Pulsar with some distinct advantages that make it easier to operate as well as to provide features - such as unified messaging and tiered storage - which introduce new capabilities that are well-suited for emerging use cases.",[48,1147,1148],{},"By comparison, Kafka originated within LinkedIn. It was committed to open source in 2011 and became a top-level Apache project in 2012. As the first major event-streaming platform on the market, it is widely recognized and widely adopted. Kafka receives enterprise support from a number of companies, including Confluent. Compared to Pulsar, Kafka is a more mature technology that is popular, has a bigger community, and a more advanced ecosystem.",[48,1150,1151],{},"Pulsar has seen tremendous growth, particularly over the past 18 months. It has been adopted by a growing list of global media companies, technology companies, and financial institutions. Below are examples of significant enterprise-level use cases that illustrate Pulsar's ability to handle mission-critical applications.",[32,1153,1155],{"id":1154},"tencent-builds-their-payment-platform-on-pulsar","Tencent Builds Their Payment Platform on Pulsar",[48,1157,1158,1162],{},[55,1159,1161],{"href":1160},"\u002Fblog\u002Ftech\u002F2019-10-22-powering-tencent-billing-platform-with-apache-pulsar\u002F","Tencent's adoption"," of Pulsar for their transactional billing system, Midas, demonstrates Pulsar's ability to handle mission-critical applications and provides compelling evidence that the technology has been rigorously tested and performs well in demanding environments. Midas operates at a massive scale, processing more than 10 billion transactions and 10+ TBs of data daily. The billing system is a critical piece of infrastructure for a company with over $50 billion in annual revenue.",[32,1164,1166],{"id":1165},"five-years-of-success-at-verizon-media","Five Years of Success at Verizon Media",[48,1168,1169,1170,1175],{},"Verizon Media provides another compelling use case, having successfully operated Pulsar in production for over five years. Verizon Media, via its acquisition of Yahoo, is the original developer of Pulsar. In their recent ",[55,1171,1174],{"href":1172,"rel":1173},"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=FXQvsHz_S1A",[264],"Pulsar Summit talk",", Joe Francis and Ludwig Pummer of Verizon Media described Pulsar as a \"battle-tested\" system that is being used throughout the Verizon Media landscape. They shared that Pulsar routinely handles up to 3 million write requests\u002Fsecond on more than 2.8 million distinct topics. Pulsar has satisfied Verizon Media's need for a low-latency, highly available system that can be scaled easily and has the ability to support a business that operates across six global data centers.",[32,1177,1179],{"id":1178},"splunk-adopts-pulsar-for-their-data-stream-processor","Splunk Adopts Pulsar for Their Data Stream Processor",[48,1181,1182,1183,1188],{},"Another key adoption story comes from Splunk, a company that has used Kafka in production environments for years. During a recent Pulsar Summit talk, \"",[55,1184,1187],{"href":1185,"rel":1186},"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_q8s3_0-BRQ",[264],"Why Splunk Chose Pulsar","\", Karthik Ramasamy shared Splunk's reasons for choosing Pulsar to power its next-generation analytics product, Splunk DSP, which handles billions of events per day. Ramasamy explained that Pulsar was able to meet 18 key requirements and cited its ease of scalability, lower operating costs, better performance, and strong open-source community as major factors in their decision to adopt Pulsar.",[48,1190,1191],{},"The above use cases clearly demonstrate that Pulsar is a powerful solution that many industry leaders are choosing to power critical business infrastructure. Although Kafka is more mature and more widely used, Pulsar's rapid rate of adoption is evidence of its strong capabilities and readiness for mission-critical use cases.",[40,1193,1195],{"id":1194},"_2-what-are-the-key-differences-between-the-competing-technologies-and-what-business-advantages-are-associated-with-each","#2: What are the key differences between the competing technologies, and what business advantages are associated with each?",[48,1197,1198],{},"While major technology and media companies, such as Uber and Netflix, have been able to successfully build unified batch and stream processing and streaming-first applications to power their real-time data needs, most companies lack the vast engineering and financial resources these applications typically require. However, Pulsar offers advanced messaging capabilities that enable companies to overcome many of these challenges.",[48,1200,1201],{},"Below, we highlight three unique capabilities - some current and others still in development - that distinctly set Pulsar apart from its competitors.",[32,1203,1205],{"id":1204},"unified-messaging-model","Unified Messaging Model",[48,1207,1208],{},"Two of the most common types of messaging used today are application messaging (traditional queuing systems) and data pipelines. Application messaging is used to enable asynchronous communications (often developed on platforms such as RabbitMQ, AMQP, JMS, among others), while data pipelines are used to move high volumes of data between different systems (such as Apache Kafka or AWS Kinesis). Because these two types of messaging are performed on different systems and serve different functions, companies often need to operate both. Developing and managing separate systems is not only expensive and complex, but can also make it difficult to integrate systems and centralize data.",[48,1210,1211],{},"Pulsar's core technology gives users the ability both to deploy it as a traditional queuing system and use it in data pipelines, uniquely positioning Pulsar as the ideal platform to provide unified messaging capabilities. Unified messaging makes it easier for organizations to capture and distribute their data, which facilitates the use of real-time data to drive business innovation.",[48,1213,1214],{},"Pulsar also recently added tools - Kafka-on-Pulsar (KoP) and AMQP-on-Pulsar (AoP) - that make it even easier for companies to leverage these unified messaging capabilities. (We discuss KoP and AoP in more detail below.)",[32,1216,1218],{"id":1217},"batch-and-event-stream-storage","Batch and Event-Stream Storage",[48,1220,1221],{},"Because companies today need to be able to make timely decisions and react to change quickly, the need for real-time, meaningful data has never been more critical. At the same time, it is crucial to be able to integrate and understand large amounts of historical data in order to gain a complete picture of a business.",[48,1223,1224],{},"Traditional Big Data systems (such as Hadoop) facilitate decision-making by allowing organizations to analyze massive historical data sets. However, as these systems can take minutes, hours, or even days to process data, they struggle to integrate real-time data and the results they produce are often of limited value.",[48,1226,1227],{},"Stream processors, such as Kafka Streams, are adept at processing streaming data and computing answers closer to real-time, but are not a good fit for processing large historical datasets. Many organizations need to run both batch and streaming data processors in order to gain the insights they need for their business. However, maintaining multiple systems is expensive and each system has its own respective challenges.",[48,1229,1230],{},"More recently, systems have been developed which can do both batch and stream processing. Apache Flink is one example. Currently, Flink is used for stream processing with both Kafka and Pulsar. However, Flink's batch capabilities are not particularly compatible with Kafka as Kafka is only able to deliver data in streams, making it too slow for most batch workloads.",[48,1232,1233,1234,1239],{},"By contrast, ",[55,1235,1238],{"href":1236,"rel":1237},"http:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002Fen\u002Fconcepts-tiered-storage\u002F",[264],"Pulsar's tiered storage model"," provides the batch storage capabilities needed to support batch processing in Flink. In the near future, Flink's batch processing capabilities will be integrated with Pulsar, enabling companies to query both historical and real-time data quickly and more easily, unlocking a unique competitive advantage.",[32,1241,1243],{"id":1242},"streaming-first-applications","\"Streaming-First\" Applications",[48,1245,1246],{},"Web application development is in the midst of a major transformation as companies look to develop more sophisticated software. The traditional application model that pairs a single monolithic application with a large SQL database is giving way to applications composed of many, smaller components, or \"microservices.\"",[48,1248,1249],{},"Many organizations are now adopting microservices because they offer greater flexibility to meet changing business needs and help facilitate development across growing engineering teams. However, microservices introduce new challenges, such as the need to enable communication among various components and keep them synchronized.",[48,1251,1252],{},"With a newer microservices technique called \"event sourcing,\" applications produce and broadcast streams of events into a shared messaging system which captures the event history in a centralized log. This improves the flow of data and helps keep applications in sync.",[48,1254,1255],{},"But event sourcing can be difficult to implement as it requires both traditional messaging capabilities and the ability to store event history for long periods of time. While Kafka is capable of storing streams of events for days or weeks, event sourcing typically requires longer retention times. This added challenge often requires users to build multiple tiers of Kafka clusters to manage the growth of event data, plus additional systems to manage and track data collectively.",[48,1257,1258],{},"By contrast, Pulsar's unified messaging model is a natural fit, as it can easily distribute events to other components and effectively store event streams for indefinite periods of time. This unique design feature makes Pulsar especially attractive to companies looking to acquire dynamic, streaming-first capabilities.",[48,1260,1261],{},"While unified messaging, combined batch and event-streaming storage, and a \"streaming-first\" approach might be feasible to achieve with other systems, these features would be complex to implement and would require a great deal of effort and investment. In contrast, Pulsar's design includes all of these features, enabling users to adapt to the changing technology landscape easily and with far less complexity.",[40,1263,1265],{"id":1264},"_3-does-pulsar-have-the-community-and-enterprise-support-it-needs-to-continue-to-develop-and-garner-further-adoption","#3: Does Pulsar have the community and enterprise support it needs to continue to develop and garner further adoption?",[48,1267,1268],{},"A snapshot comparison of the Pulsar and Kafka communities today reflects that Kafka's is larger overall, with more Slack users and more stack overflow questions. While Pulsar's community is currently smaller, it is highly engaged and rapidly growing. Below are some highlights of its recent momentum.",[32,1270,1272],{"id":1271},"pulsars-first-global-summit","Pulsar's First Global Summit",[48,1274,1275,1276,1281,1282,1287,1288,1293,1294,1293,1299,1303,1304,190],{},"In June, ",[55,1277,1280],{"href":1278,"rel":1279},"https:\u002F\u002Ffinance.yahoo.com\u002Fnews\u002Frise-apache-pulsar-first-ever-162100598.html",[264],"Pulsar held its first global event"," - the ",[55,1283,1286],{"href":1284,"rel":1285},"https:\u002F\u002Fpulsar-summit.org\u002Fschedule\u002Ffirst-day",[264],"Pulsar Summit Virtual Conference 2020",". The event featured more than 30 speaker sessions from Pulsar's top contributors, thought leaders, and developers. We heard real-world Pulsar adoption stories and received insights from companies such as ",[55,1289,1292],{"href":1290,"rel":1291},"https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fverizon-media\u002F",[264],"Verizon Media",", ",[55,1295,1298],{"href":1296,"rel":1297},"https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fsplunk\u002F",[264],"Splunk",[55,1300,96],{"href":1301,"rel":1302},"https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fiterable\u002F",[264],", and ",[55,1305,1308],{"href":1306,"rel":1307},"https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fovhgroup\u002F",[264],"OVHcloud",[48,1310,1311],{},"With more than 600 sign-ups - including attendees from top internet, technology, and financial institutions such as Google, Microsoft, AMEX, Salesforce, Disney, and Paypal - the event revealed a highly engaged and global Pulsar community and demonstrated that interest in Pulsar is burgeoning.",[48,1313,1314],{},"In fact, the global Pulsar community subsequently asked us to host dedicated regional events in Asia and Europe soon. To meet this growing demand, we have scheduled Pulsar Summit Asia 2020 in October and are currently planning Pulsar Summit Europe.",[32,1316,1318],{"id":1317},"community-support-training-and-events","Community Support - Training and Events",[48,1320,1321,1322,1327],{},"In addition to facilitating large, widely attended summits, the Pulsar community is focusing on interactive training and online events. For example, earlier this year, the community, led by StreamNative, launched a weekly ",[55,1323,1326],{"href":1324,"rel":1325},"https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqRma1oIkcWhWAhKgImEeRiQi5vMlqTc-",[264],"live-streaming, interactive tutorial"," called TGIP (Thank Goodness It's Pulsar) that provides technology updates and hands-on tutorials highlighting various operational aspects. TGIP sessions are available on YouTube and StreamNative.io and are helping to augment Pulsar's growing knowledge base.",[48,1329,1330,1331,1336,1337,1342],{},"In 2020, the Pulsar community also launched ",[55,1332,1335],{"href":1333,"rel":1334},"https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqRma1oIkcWhfmUuJrMM5YIG8hjju62Ev",[264],"monthly webinars"," to share best practices, new use cases, and technology updates. Recent webinars have been hosted by strategic commercial and open-source partners such as OVHCloud, Overstock, and Nutanix. On July 28th, StreamNative will be hosting ",[55,1338,1341],{"href":1339,"rel":1340},"https:\u002F\u002Fus02web.zoom.us\u002Fwebinar\u002Fregister\u002FWN_xMt6QBJ9TWiyeVdifqKITg",[264],"Operating Pulsar in Production"," as a panel discussion with additional participants from Verizon Media and Splunk.",[48,1344,1345,1346,1351],{},"Pulsar's ecosystem has further evolved with the expansion of professional training, which is available through StreamNative and other partners. In fact, Pulsar and Kafka expert Jesse Anderson recently led an in-depth training session on ",[55,1347,1350],{"href":1348,"rel":1349},"https:\u002F\u002Fgumroad.com\u002Fl\u002FsuukG",[264],"Developing Pulsar Applications",". Professional training sessions help to enlarge the pool of Pulsar-trained engineers and allow Pulsar users to accelerate their messaging and streaming platform development initiatives.",[48,1353,1354,1355,1359],{},"In addition, an increase in the ",[55,1356,1358],{"href":1357},"\u002Fresource","publication of whitepapers"," is helping to expand Pulsar's knowledge base.",[48,1361,1362],{},"Committed community partners have also contributed to key project advancements. Below, we look at two recent product launches.",[818,1364,1366],{"id":1365},"ovhcloud-helps-companies-move-from-kafka-to-pulsar","OVHCloud Helps Companies Move from Kafka to Pulsar",[48,1368,1369],{},"In March 2020, OVHCloud and StreamNative launched Kafka-on-Pulsar (KoP), the result of the two companies working closely in partnership. KoP enables Kafka users to migrate their existing Kafka applications and services to Pulsar without modifying the code. Although only recently released, KoP has already been adopted by several organizations and is being used in production environments. Moreover, KoP's availability is helping to expand Pulsar's adoption.",[818,1371,1373],{"id":1372},"china-mobile-helps-companies-move-from-rabbitmq-to-pulsar","China Mobile Helps Companies Move from RabbitMQ to Pulsar",[48,1375,1376],{},"In June 2020, China Mobile and StreamNative announced the launch of another major platform upgrade, AMQP on Pulsar (AoP). Similar to KoP, AoP allows organizations currently using RabbitMQ (or other AMQP message brokers) to migrate existing applications and services to Pulsar without code modification. Again, this is a key initiative that will help drive the adoption and usage of Pulsar.",[48,1378,1379],{},"The events and initiatives described above illustrate the Pulsar community's firm commitment to education and ecosystem development. More importantly, they demonstrate the momentum and growth we can expect in the future.",[40,1381,931],{"id":930},[48,1383,1384],{},"In today's ever-changing business landscape, access to data can unlock innovative business opportunities, define new categories, and propel companies ahead of the competition. As a result, organizations are increasingly seeking to leverage their data and the insights that can be gained from it to develop competitive advantages, and they are seeking new technologies to help them achieve these goals.",[48,1386,1387],{},"In this post, we set out to address some common business concerns organizations face when evaluating a new technology. These include the technology's proven capabilities, its ability to enable in-demand business use cases, and, in the case of open-source technologies, the size and level of engagement within the project's community.",[48,1389,1390],{},"The Tencent, Verizon Media, and Splunk use cases described earlier demonstrate Pulsar's ability to deliver mission-critical applications in the real world. Beyond its proven capabilities, Pulsar's ability to deliver unified messaging and streaming-first applications provides a marked advantage by enabling organizations to build disruptive, competitive technologies without requiring extensive resources. Pulsar's integration with Flink, which is currently in development, will provide yet another competitive advantage: the ability to perform both batch and stream processing on the same platform.",[48,1392,1393],{},"While the Pulsar community and a few other key areas, such as documentation, are still small, their growth has increased considerably in the past 18 months. Pulsar's highly engaged and quickly growing community and ecosystem are committed to contributing to the ongoing expansion of Pulsar's knowledge base and training materials, while also accelerating the development of key capabilities.",[48,1395,1396],{},"Disruption can happen quickly and organizations evaluating any technology need to consider not only the strengths and weaknesses it has today, but also how the technology will continue to grow and evolve to meet business needs in the future. The combination of Pulsar's enhanced messaging offering and unique capabilities make it a strong alternative that should be considered by any company looking to develop real-time data streaming capabilities.",[48,1398,1399,1400,190],{},"For a deeper dive into Pulsar vs. Kafka — A More Accurate Perspective on Performance, Architecture, and Features, please read Part 1 of this series ",[55,1401,267],{"href":1402},"\u002Fblog\u002Ftech\u002Fpulsar-vs-kafka-part-1",[32,1404,1406],{"id":1405},"learn-more-about-pulsar","Learn More About Pulsar",[48,1408,1409,1410,1415,1416,190],{},"We encourage you to sign up for the ",[55,1411,1414],{"href":1412,"rel":1413},"https:\u002F\u002Fshare.hsforms.com\u002F1IS56E-RvSVuMXU-ghlkoFA3x5r4",[264],"Pulsar Newsletter"," to stay up-to-date on upcoming events and technology updates. If you would like to chat with current Pulsar users, you can join the ",[55,1417,1420],{"href":1418,"rel":1419},"https:\u002F\u002Fapache-pulsar.herokuapp.com\u002F",[264],"Pulsar Slack Channel",[48,1422,1423,1424,1427],{},"And don't forget to join our webinar, ",[55,1425,1341],{"href":1339,"rel":1426},[264],", on Tuesday, July 28th at 10 am. This will be a highly interactive roundtable discussion with additional participants from Verizon Media, Splunk, and StreamNative.",[32,1429,1431],{"id":1430},"special-thanks","Special Thanks",[48,1433,1434],{},"We would like to thank the many members of the Pulsar community who contributed to this article - especially, Jerry Peng, Jesse Anderson, Joe Francis, Matteo Merli, Sanjeev Kulkarni, and Addison Higham.",[32,1436,1438],{"id":1437},"links-resources","Links & Resources",[339,1440,1441,1448],{},[342,1442,1443,1444,190],{},"For more on Pulsar documentation and training, visit ",[55,1445,1447],{"href":1446},"\u002Fresource#pulsar","StreamNative's Resources page",[342,1449,1450,1451,1455],{},"You can also access ",[55,1452,1454],{"href":1453},"\u002Fblog","recent whitepapers"," from Tuya, OVHCloud, Tencent, Yahoo!Japan, and more.",{"title":18,"searchDepth":19,"depth":19,"links":1457},[1458,1459,1464,1469,1473],{"id":1111,"depth":19,"text":1112},{"id":1133,"depth":19,"text":1134,"children":1460},[1461,1462,1463],{"id":1154,"depth":279,"text":1155},{"id":1165,"depth":279,"text":1166},{"id":1178,"depth":279,"text":1179},{"id":1194,"depth":19,"text":1195,"children":1465},[1466,1467,1468],{"id":1204,"depth":279,"text":1205},{"id":1217,"depth":279,"text":1218},{"id":1242,"depth":279,"text":1243},{"id":1264,"depth":19,"text":1265,"children":1470},[1471,1472],{"id":1271,"depth":279,"text":1272},{"id":1317,"depth":279,"text":1318},{"id":930,"depth":19,"text":931,"children":1474},[1475,1476,1477],{"id":1405,"depth":279,"text":1406},{"id":1430,"depth":279,"text":1431},{"id":1437,"depth":279,"text":1438},"Apache Pulsar","2020-07-22","Emerging adoption stories, new messaging trends, technology differentiators, and community growth to better understand the key advantages and disadvantages of the top technologies in the messaging and event streaming space",{},"\u002Fblog\u002Fpulsar-vs-kafka-part-2-adoption-use-cases-differentiators-and-community","9 min read",{"title":1088,"description":1480},"blog\u002Fpulsar-vs-kafka-part-2-adoption-use-cases-differentiators-and-community",[1084,1487],"Success Stories","xnq9Tdmmhp0Z0NN3l_1_m535tr-hx_8LAcvws1QXtE8",[1490,1506,1521],{"id":1491,"title":1090,"bioSummary":1492,"email":290,"extension":8,"image":1493,"linkedinUrl":1494,"meta":1495,"position":1502,"stem":1503,"twitterUrl":1504,"__hash__":1505},"authors\u002Fauthors\u002Fcarolyn-king.md","Carolyn has dedicated the past 15 years to helping companies develop growth strategies to drive customer acquisition and revenue. At StreamNative, she leads all things growth, including global Marketing and Community, global Training and Documentation, Developer Relations and Sales for the US and EMEA. She holds an MBA from UCLA Anderson and a BA in Business-Economics from UCLA. Carolyn lives in Santa Monica, California.","\u002Fimgs\u002Fauthors\u002Fcarolyn-king.webp","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fcarolynnicoleking\u002F",{"body":1496},{"type":15,"value":1497,"toc":1500},[1498],[48,1499,1492],{},{"title":18,"searchDepth":19,"depth":19,"links":1501},[],"Former VP of Growth, StreamNative","authors\u002Fcarolyn-king","https:\u002F\u002Ftwitter.com\u002Fcarolynking22","yTyJgeMMyV9lLQiJEQq9_me9Vb3o5cMqh8lVfZBceDY",{"id":1507,"title":1091,"bioSummary":1508,"email":290,"extension":8,"image":1509,"linkedinUrl":290,"meta":1510,"position":1517,"stem":1518,"twitterUrl":1519,"__hash__":1520},"authors\u002Fauthors\u002Faddison-higham.md","Addison Higham has deep experience with streaming technologies such as Flink and Spark. Seeking a new stream storage technology for his previous company, Instructure, Addison discovered Pulsar and quickly became a Pulsar champion and drove the company’s adoption of the technology. Addison then joined StreamNative, where he leads development of StreamNative Cloud and helps customers to successfully adopt Pulsar. Addison lives in Salt Lake City, Utah.","\u002Fimgs\u002Fauthors\u002Faddison-higham.webp",{"body":1511},{"type":15,"value":1512,"toc":1515},[1513],[48,1514,1508],{},{"title":18,"searchDepth":19,"depth":19,"links":1516},[],"Chief Architect, StreamNative","authors\u002Faddison-higham","https:\u002F\u002Ftwitter.com\u002Faddisonjh?lang=en","jzIyP69DmPgDfuOwbZvvSU4LsXYSvJn9n31qhQCFqBg",{"id":1522,"title":1092,"bioSummary":1523,"email":290,"extension":8,"image":1524,"linkedinUrl":1525,"meta":1526,"position":1533,"stem":1534,"twitterUrl":1535,"__hash__":1536},"authors\u002Fauthors\u002Fsijie-guo.md","Sijie’s journey with Apache Pulsar began at Yahoo! where he was part of the team working to develop a global messaging platform for the company. He then went to Twitter, where he led the messaging infrastructure group and co-created DistributedLog and Twitter EventBus. In 2017, he co-founded Streamlio, which was acquired by Splunk, and in 2019 he founded StreamNative. He is one of the original creators of Apache Pulsar and Apache BookKeeper, and remains VP of Apache BookKeeper and PMC Member of Apache Pulsar. Sijie lives in the San Francisco Bay Area of California.","\u002Fimgs\u002Fauthors\u002Fsijie-guo.webp","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fsijieg\u002F",{"body":1527},{"type":15,"value":1528,"toc":1531},[1529],[48,1530,1523],{},{"title":18,"searchDepth":19,"depth":19,"links":1532},[],"CEO and Co-Founder, StreamNative, Apache Pulsar PMC Member","authors\u002Fsijie-guo","https:\u002F\u002Ftwitter.com\u002Fsijieg","krzMgsbADqGZT1TnpWTVzT4HJ9U7oZB9hzOMiDT5Wd0",[1538,1545,1550],{"path":1539,"title":1540,"date":1541,"image":-1,"link":-1,"collection":1542,"resourceType":1543,"score":1544,"id":1539},"\u002Fblog\u002Fmoved-from-apache-kafka-to-apache-pulsar","Why we moved from Apache Kafka to Apache Pulsar","2020-04-21","blogs","Blog",1,{"path":1546,"title":1547,"date":1479,"image":1548,"link":-1,"collection":1542,"resourceType":1543,"score":1549,"id":1546},"\u002Fblog\u002Fapache-pulsar-adoption-why-companies-use-streaming-messaging-platform","Apache Pulsar Adoption: Why Companies Use the Streaming and Messaging Platform","\u002Fimgs\u002Fblogs\u002F63c7c024fe34bd0cf4bdc094_63a377b02be9e607dd3ac516_top-1.jpeg",0.667,{"path":1551,"title":1552,"date":1553,"image":1554,"link":-1,"collection":1542,"resourceType":1543,"score":1555,"id":1551},"\u002Fblog\u002Fapache-pulsar-kafka-protocol-tiered-storage-and-beyond-heres-what-happened-at-pulsar-meetup-beijing-2023","Apache Pulsar, Kafka Protocol, Tiered storage and Beyond! Here’s What Happened at Pulsar Meetup Beijing 2023","2023-10-16","\u002Fimgs\u002Fblogs\u002F652eac15b2c14020a359e9fb_IMG_9095.JPG",0.5,1775615052167]