[{"data":1,"prerenderedAt":1546},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":23,"navbar-pricing-featured":304,"story-\u002Fsuccess-stories\u002Fhuawei-device":1084,"story-authors-\u002Fsuccess-stories\u002Fhuawei-device":1506,"related-\u002Fsuccess-stories\u002Fhuawei-device":1527},{"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",{"id":305,"title":306,"authors":307,"body":312,"category":1071,"createdAt":10,"date":1072,"description":1073,"extension":8,"featured":7,"image":1074,"isDraft":292,"link":10,"meta":1075,"navigation":7,"order":294,"path":1076,"readingTime":1077,"relatedResources":10,"seo":1078,"stem":1079,"tags":1080,"__hash__":1083},"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",[308,309,310,311],"Matteo Meril","Neng Lu","Hang Chen","Penghui Li",{"type":14,"value":313,"toc":1041},[314,317,320,323,326,329,333,336,346,352,355,363,368,372,379,382,385,393,397,400,405,409,412,415,418,421,430,434,437,448,451,455,458,461,472,475,479,483,491,494,498,506,535,539,542,547,551,554,558,561,564,569,578,583,586,589,600,604,607,618,622,625,628,633,636,665,669,671,677,680,685,690,693,697,711,715,726,730,745,754,765,768,771,775,778,781,792,795,798,801,806,811,815,819,836,840,854,859,863,874,877,893,897,908,913,918,926,930,933,937,944,948,951,960,965,974,980,989,998,1007,1016,1025,1033],[47,315,316],{},"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.",[47,318,319],{},"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.",[47,321,322],{},"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.",[47,324,325],{},"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.",[47,327,328],{},"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.",[39,330,332],{"id":331},"key-benchmark-findings","Key Benchmark Findings",[47,334,335],{},"Ursa delivered 5 GB\u002Fs of sustained throughput at an infrastructure cost of just $54 per hour. For comparison:",[337,338,339,343],"ul",{},[340,341,342],"li",{},"MSK: $303 per hour → 5.6x more expensive compared to Ursa",[340,344,345],{},"Redpanda: $988 per hour → 18x more expensive compared to Ursa",[47,347,348],{},[349,350],"img",{"alt":17,"src":351},"\u002Fimgs\u002Fblogs\u002F679c71b67d9046f26edc7977_AD_4nXfvTqyBNUBu2lObdkKAx-5UNkpNP8UYULLZyOcixE6z99VMZUUEsUqWjzexI7vjyNGRNSAUoM9smYvdTP55ctAhIbrs5lmQgcSVMWdaoigbWouCl95DVSQsxooY-qqfGcYqS4g4zA.png",[47,353,354],{},"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:",[337,356,357,360],{},[340,358,359],{},"50% cheaper than Confluent WarpStream",[340,361,362],{},"85% cheaper than MSK and Redpanda",[47,364,365],{},[349,366],{"alt":17,"src":367},"\u002Fimgs\u002Fblogs\u002F679c602d77e9c706de5343b8_AD_4nXeDv8rrv_C1CTCCiqYo1zpvlGYbdBk1r0VEqovAPu22iFMQZgh54Hfw9PBMLzM7jDFxKwAFDxbdG0np4XVk_tGsWhEKMloLRcmmea7lvueCx-0cFsyaE3Mya4Mxc1Dox95A6JEc.png",[39,369,371],{"id":370},"ursa-highly-cost-efficient-data-streaming-at-scale","Ursa: Highly Cost-Efficient Data Streaming at Scale",[47,373,374,378],{},[54,375,377],{"href":376},"\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.",[47,380,381],{},"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.",[47,383,384],{},"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:",[337,386,387,390],{},[340,388,389],{},"Eliminating inter-zone traffic costs via a leaderless architecture.",[340,391,392],{},"Replacing costly inter-zone replication with direct writes to cloud storage using open lakehouse formats.",[39,394,396],{"id":395},"how-ursa-eliminates-inter-zone-traffic","How Ursa Eliminates Inter-Zone Traffic",[47,398,399],{},"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.",[47,401,402],{},[349,403],{"alt":17,"src":404},"\u002Fimgs\u002Fblogs\u002F679c602e21b3571bb7117dca_AD_4nXd7Oahc77NjRLNvA9clLt0tsyU6MrIqVibFYv5pW5giTIcCHPr3EA_yTGzfVEUIVO3VXK56qWK8zmBCp5lY0E_4nmlWIPFrHjtHylA5NhwELjn-UB0fLG2h_kbrxrc7Cs_edvveNA.png",[31,406,408],{"id":407},"leaderless-architecture","Leaderless architecture",[47,410,411],{},"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.",[47,413,414],{},"Pros of Leader-Based Architectures:\n✔ Maintains message ordering via local sequence IDs\n✔ Delivers low latency and high performance through message caching",[47,416,417],{},"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",[47,419,420],{},"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.",[47,422,423,424,429],{},"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 ",[54,425,428],{"href":426,"rel":427},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Foxia",[263],"Oxia",", a scalable metadata and index service created by StreamNative in 2022.",[31,431,433],{"id":432},"oxia-the-metadata-layer-enabling-leaderless-architecture","Oxia: The Metadata Layer Enabling Leaderless Architecture",[47,435,436],{},"Ensuring message ordering in a leaderless architecture is complex, but Ursa solves this with Oxia:",[337,438,439,442,445],{},[340,440,441],{},"Handles millions of metadata\u002Findex operations per second",[340,443,444],{},"Generates sequential IDs to maintain strict message ordering",[340,446,447],{},"Optimized for Kubernetes with horizontal scalability",[47,449,450],{},"Producers and consumers can connect to any broker within their local AZ, eliminating inter-zone traffic costs while maintaining performance through localized caching.",[31,452,454],{"id":453},"zero-interzone-data-replication","Zero interzone data replication",[47,456,457],{},"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.",[47,459,460],{},"Ursa avoids these costs by writing data directly to cloud storage (e.g., AWS S3, Google GCS):",[337,462,463,466,469],{},[340,464,465],{},"Built-In Resilience: Cloud storage inherently offers high availability and fault tolerance without inter-zone traffic fees.",[340,467,468],{},"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).",[340,470,471],{},"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.",[47,473,474],{},"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.",[39,476,478],{"id":477},"how-we-ran-a-5-gbs-test-with-ursa","How We Ran a 5 GB\u002Fs Test with Ursa",[31,480,482],{"id":481},"ursa-cluster-deployment","Ursa Cluster Deployment",[337,484,485,488],{},[340,486,487],{},"9 brokers across 3 availability zones, each on m6i.8xlarge (Fixed 12.5 Gbps bandwidth, 32 vCPU cores, 128 GB memory).",[340,489,490],{},"Oxia cluster (metadata store) with 3 nodes of m6i.8xlarge, distributed across three availability zones (AZs).",[47,492,493],{},"During peak throughput (5 GB\u002Fs), each broker’s network usage was about 10 Gbps.",[31,495,497],{"id":496},"openmessaging-benchmark-workers-configuration","OpenMessaging Benchmark Workers & Configuration",[47,499,500,501,505],{},"The OpenMessaging Benchmark(OMB) Framework is a suite of tools that make it easy to benchmark distributed messaging systems in the cloud. Please check ",[54,502,503],{"href":503,"rel":504},"https:\u002F\u002Fopenmessaging.cloud\u002Fdocs\u002Fbenchmarks\u002F",[263]," for details.",[337,507,508,523,532],{},[340,509,510,511,516,517,522],{},"12 OMB workers: 6 for ",[54,512,515],{"href":513,"rel":514},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002Fd1094122270775e4f1580947f80c5055",[263],"producers",", 6 for ",[54,518,521],{"href":519,"rel":520},"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.",[340,524,525,526,531],{},"Sample YAML ",[54,527,530],{"href":528,"rel":529},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F204c1f26c4d44a218ae235bf2de99904",[263],"scripts"," provided for Kafka-compatible configuration and rate limits.",[340,533,534],{},"Achieved consistent 5 GB\u002Fs publish\u002Fsubscribe throughput.",[39,536,538],{"id":537},"ursa-benchmark-tests-results","Ursa Benchmark Tests & Results",[47,540,541],{},"The following diagram demonstrates that Ursa can consistently handle 5 GB\u002Fs of traffic, fully saturating the network across all broker nodes.",[47,543,544],{},[349,545],{"alt":17,"src":546},"\u002Fimgs\u002Fblogs\u002F679c602d7b261bac1113f7d6_AD_4nXdDPsRc3koXICiFF0bqSmGWbJt_RlUy4FE3ruuWOfbCfpcqZ1dejjqGbkaCJv2hQFL1nirRouBVRW2l5uMWBvY9naMqGB_wHcLI14dBM0f85TXhmdm3UxEv1yGX9Y4hf5FttSkZew.png",[39,548,550],{"id":549},"comparing-infrastructure-cost","Comparing Infrastructure Cost",[47,552,553],{},"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.",[31,555,557],{"id":556},"test-setup-key-assumptions","Test Setup & Key Assumptions",[47,559,560],{},"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.",[47,562,563],{},"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:",[337,565,566],{},[340,567,568],{},"9 × m6i.8xlarge instances",[47,570,571,572,577],{},"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",[54,573,576],{"href":574,"rel":575},"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:",[337,579,580],{},[340,581,582],{},"15 × kafka.m7g.8xlarge (32 vCPUs, 128 GB memory, 15 Gbps network, 4000 GiB EBS).",[47,584,585],{},"This configuration was necessary to work around MSK's storage bandwidth limitations, ensuring a comparable cost basis to other evaluated streaming engines.",[47,587,588],{},"Additional key assumptions include:",[337,590,591,594,597],{},[340,592,593],{},"Inter-AZ producer traffic: For leader-based engines, two-thirds of producer-to-broker traffic crosses AZs due to leader distribution.",[340,595,596],{},"Consumer optimizations: Follower fetch is enabled across all tests, eliminating inter-AZ consumer traffic.",[340,598,599],{},"Storage cost exclusions: This benchmark only evaluates streaming costs, assuming no long-term data retention.",[31,601,603],{"id":602},"inter-broker-replication-costs","Inter-Broker Replication Costs",[47,605,606],{},"Inter-broker (cross-AZ) replication is a major cost driver for data streaming engines:",[337,608,609,612,615],{},[340,610,611],{},"RedPanda: Inter-broker replication is not free, leading to substantial costs when data must be copied across multiple availability zones.",[340,613,614],{},"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.",[340,616,617],{},"Ursa: No inter-broker replication costs due to its leaderless architecture, eliminating inter-zone replication costs entirely.",[31,619,621],{"id":620},"zone-affinity-reducing-inter-az-costs","Zone Affinity: Reducing Inter-AZ Costs",[47,623,624],{},"We evaluated zone affinity mechanisms to further reduce inter-AZ data transfer costs.",[47,626,627],{},"Consumers:",[337,629,630],{},[340,631,632],{},"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",[47,634,635],{},"Producers:",[337,637,638,647,656],{},[340,639,640,641,646],{},"Kafka protocol lacks an easy way to enforce producer AZ affinity (though ",[54,642,645],{"href":643,"rel":644},"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).",[340,648,649,650,655],{},"Redpanda recently introduced ",[54,651,654],{"href":652,"rel":653},"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.",[340,657,658,659,664],{},"Ursa is the only system in this test with ",[54,660,663],{"href":661,"rel":662},"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.",[31,666,668],{"id":667},"cost-comparison-results","Cost Comparison Results",[47,670,335],{},[337,672,673,675],{},[340,674,342],{},[340,676,345],{},[47,678,679],{},"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.",[47,681,682],{},[349,683],{"alt":17,"src":684},"\u002Fimgs\u002Fblogs\u002F679c72208198ca36a352f228_AD_4nXeeZuM8T-xBlD4Vf3j67K618n08qh8wIDLLtiLJG0ssA1Wj1V26u7wIDTX9sqLrtw8mB2c299dwzarGen62CG0Vh7nWstn5qbPGFcBaKJYEepTsLr5fHWv1U8uqbg8Y0UOK6fJ7.png",[47,686,687],{},[349,688],{"alt":17,"src":689},"\u002Fimgs\u002Fblogs\u002F679c625978031f40229de484_AD_4nXdLkLLJ30KKr-_A_rN1j8akVwBYacAWIPzWHoOReJF421890kfByZoQQxkLczihVSmiw5Q9J51-V9I2SEKITbwsYnANDDTlAVL5nQ_jfaHNTe9VEWhSoa7DZooCnilDYL6l6msmJg.png",[47,691,692],{},"The detailed infrastructure cost calculations for each data streaming engine are listed below:",[31,694,696],{"id":695},"streamnative-ursa","StreamNative - Ursa",[337,698,699,702,705,708],{},[340,700,701],{},"Server EC2 costs: 9 * $1.536\u002Fhr = $14",[340,703,704],{},"Client EC2 costs: 9 * $1.536\u002Fhr =$14",[340,706,707],{},"S3 write requests costs: 1350 r\u002Fs * $0.005\u002F1000r * 3600s = $24",[340,709,710],{},"S3 read requests costs: 1350 r\u002Fs * $0.0004\u002F1000r * 3600s = $2",[31,712,714],{"id":713},"aws-msk","AWS MSK",[337,716,717,720,723],{},[340,718,719],{},"Server EC2 costs: 15 * $3.264\u002Fhr = $49",[340,721,722],{},"Client side EC2 costs: 9 * $1.536\u002Fhr =$14",[340,724,725],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FG(in+out) * 3600 = $240",[31,727,729],{"id":728},"redpanda","RedPanda",[337,731,732,734,736,739,742],{},[340,733,701],{},[340,735,704],{},[340,737,738],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FGB(in+out) * 3600 = $240",[340,740,741],{},"Interzone traffic - replication: 10GB\u002Fs * $0.02\u002FGB(in+out) * 3600 = $720",[340,743,744],{},"Interzone traffic - broker to consumer: $0 (fetch from local zone)",[47,746,747,748,753],{},"Please note that we were unable to test ",[54,749,752],{"href":750,"rel":751},"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.",[337,755,756,762],{},[340,757,758,761],{},[54,759,645],{"href":643,"rel":760},[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).",[340,763,764],{},"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.",[47,766,767],{},"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.",[47,769,770],{},"We may revisit this comparison as more details become available.",[39,772,774],{"id":773},"comparing-total-cost-of-ownership","Comparing Total Cost of Ownership",[47,776,777],{},"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.",[47,779,780],{},"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:",[337,782,783,786,789],{},[340,784,785],{},"Ursa ($164,353\u002Fmonth) is: 50% cheaper than Confluent WarpStream ($337,068\u002Fmonth)",[340,787,788],{},"85% cheaper than AWS MSK ($1,115,251\u002Fmonth)",[340,790,791],{},"86% cheaper than Redpanda ($1,202,853\u002Fmonth)",[47,793,794],{},"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.",[47,796,797],{},"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.",[47,799,800],{},"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.",[47,802,803],{},[349,804],{"alt":17,"src":805},"\u002Fimgs\u002Fblogs\u002F679c602d194800c9206d9d58_AD_4nXcFlf755xgyz7htxhMhBV5fGrsxy642mQNodt61DTok_z1dwkw5A6lkO5hatXVneCaB0anbZPAyvLI3MlIMuQEYLEACHHvQMOr5UfaB37dfzkdqewDEvcT-20VGd_zzvJsuA00zGA.png",[47,807,808],{},[349,809],{"alt":17,"src":810},"\u002Fimgs\u002Fblogs\u002F679c62594e9c2e629fae73aa_AD_4nXeU6cOgItnjLsEZCOf13TEvMY_SHWWIxYP2OYUj-B1GUPyWO78OG08K_v03hwYSVcg06f9dqDiGmdwy76vynjmiDGL5bluZ5_XF4nSU_r59oOZdfViXndXt6s11vVOY7qwfZN8v.png",[31,812,814],{"id":813},"cost-breakdown","Cost Breakdown",[816,817,818],"h4",{"id":695},"StreamNative – Ursa",[337,820,821,824,827,830,833],{},[340,822,823],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9,953.28",[340,825,826],{},"S3 Write Requests: 1,350 r\u002Fs × $0.005\u002F1,000 r × 3,600 s × 24 hr × 30 days = $17,496",[340,828,829],{},"S3 Read Requests: 1,350 r\u002Fs × $0.0004\u002F1,000 r × 3,600 s × 24 hr × 30 days = $1,400",[340,831,832],{},"S3 Storage Costs: 5 GB\u002Fs × $0.021\u002FGB × 3,600 s × 24 hr × 7 days = $63,504",[340,834,835],{},"Vendor Cost: 200 ETU × $0.50\u002Fhr × 24 hr × 30 days = $72,000",[816,837,839],{"id":838},"warpstream","WarpStream",[337,841,842,845],{},[340,843,844],{},"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.",[340,846,847,848,853],{},"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 “",[54,849,852],{"href":850,"rel":851},"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.",[47,855,856],{},[349,857],{"alt":17,"src":858},"\u002Fimgs\u002Fblogs\u002F679c602e42713e0028e9af5e_AD_4nXcu5_VWTLu9jRYs6zX1MBAOtLQEo5gyfNSWPcbpnQHXTa8qNCFAXezRR2E8daygzYTTwd4dhJjaLaLM8C6y_3OGbu2NS7pdvEv3a8-ptNKOg7AeKnYqPQCAYvQ5EuxzuI3JYIvY.png",[816,860,862],{"id":861},"msk","MSK",[337,864,865,868,871],{},[340,866,867],{},"EC2 (Server): 15 * $3.264\u002Fhr × 24 hr × 30 days = $35,251",[340,869,870],{},"Interzone Traffic (Client-Server): 5 GB\u002Fs × ⅔ × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $172,800",[340,872,873],{},"Storage: 5 GB\u002Fs × $0.1\u002FGB-month × 3,600 s × 24 hr × 7 days * 3 replicas = $907,200",[816,875,729],{"id":876},"redpanda-1",[337,878,879,882,884,887,890],{},[340,880,881],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9953",[340,883,870],{},[340,885,886],{},"Interzone Traffic (Replication): 5 GB\u002Fs × 2 × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $518,400",[340,888,889],{},"Storage: 5 GB\u002Fs × $0.045\u002FGB-month(st1) × 3,600 s × 24 hr × 7 days * 3 replicas = $408,240",[340,891,892],{},"Vendor Cost: $93,333 per month (based on limited information. See additional notes below).",[816,894,896],{"id":895},"additional-notes","Additional Notes",[337,898,899],{},[340,900,901,902,907],{},"Redpanda does not publicly disclose its BYOC pricing, making it difficult to accurately assess its total costs. We refer to information from the whitepaper “",[54,903,906],{"href":904,"rel":905},"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.",[47,909,910],{},[349,911],{"alt":17,"src":912},"\u002Fimgs\u002Fblogs\u002F679c602dc8a9859eed89a0ef_AD_4nXdbcO8vsNNPy4GtkNLlmNKf22fjxRvzLzH7CtOna1L08sTbvnZx3HhufeFqc1w4K2gEF7lxO2IR5supotxebAiGnA07Qa8Yr3Rd1pVK2LYKK4WurlJGwgdwwucZIFoF-N_2oBjY.png",[47,914,915],{},[349,916],{"alt":17,"src":917},"\u002Fimgs\u002Fblogs\u002F679c602d6bc1c2287e012540_AD_4nXfcHZnLfjbjIr3ZAgoQXT9dwP3aQCOQPmGZZJUtpNZSwE6qY6M3yehIaBxCwxEIeu5PVdUPY0zhyjnow26YfgjdYgSG4GnV9ibxu0YWTIpwng6z_F6FUGJMpERMKtpsFESzXSN_Sw.png",[337,919,920,923],{},[340,921,922],{},"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.",[340,924,925],{},"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.",[39,927,929],{"id":928},"conclusion","Conclusion",[47,931,932],{},"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.",[31,934,936],{"id":935},"balancing-latency-and-cost","Balancing Latency and Cost",[47,938,939,943],{},[54,940,942],{"href":941},"\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.",[31,945,947],{"id":946},"the-future-of-streaming-infrastructure","The Future of Streaming Infrastructure",[47,949,950],{},"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.",[47,952,953,954,959],{},"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. ",[54,955,958],{"href":956,"rel":957},"https:\u002F\u002Fconsole.streamnative.cloud\u002F",[263],"Get started"," with StreamNative Ursa today!",[961,962,964],"h1",{"id":963},"references","References",[47,966,967,970,971],{},[968,969,428],"span",{}," ",[54,972,973],{"href":973},"\u002Fblog\u002Fintroducing-oxia-scalable-metadata-and-coordination",[47,975,976,970,978],{},[968,977,377],{},[54,979,376],{"href":376},[47,981,982,970,985],{},[968,983,984],{},"StreamNative pricing",[54,986,987],{"href":987,"rel":988},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fbilling-overview",[263],[47,990,991,970,994],{},[968,992,993],{},"WarpStream pricing",[54,995,996],{"href":996,"rel":997},"https:\u002F\u002Fwww.warpstream.com\u002Fpricing#pricingfaqs",[263],[47,999,1000,970,1003],{},[968,1001,1002],{},"AWS S3 pricing",[54,1004,1005],{"href":1005,"rel":1006},"https:\u002F\u002Faws.amazon.com\u002Fs3\u002Fpricing\u002F",[263],[47,1008,1009,970,1012],{},[968,1010,1011],{},"AWS EBS pricing",[54,1013,1014],{"href":1014,"rel":1015},"https:\u002F\u002Faws.amazon.com\u002Febs\u002Fpricing\u002F",[263],[47,1017,1018,970,1021],{},[968,1019,1020],{},"AWS MSK pricing",[54,1022,1023],{"href":1023,"rel":1024},"https:\u002F\u002Faws.amazon.com\u002Fmsk\u002Fpricing\u002F",[263],[47,1026,1027,970,1030],{},[968,1028,1029],{},"The Brutal Truth about Kafka Cost Calculators",[54,1031,850],{"href":850,"rel":1032},[263],[47,1034,1035,970,1038],{},[968,1036,1037],{},"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group",[54,1039,904],{"href":904,"rel":1040},[263],{"title":17,"searchDepth":18,"depth":18,"links":1042},[1043,1044,1045,1050,1054,1055,1064,1067],{"id":331,"depth":18,"text":332},{"id":370,"depth":18,"text":371},{"id":395,"depth":18,"text":396,"children":1046},[1047,1048,1049],{"id":407,"depth":278,"text":408},{"id":432,"depth":278,"text":433},{"id":453,"depth":278,"text":454},{"id":477,"depth":18,"text":478,"children":1051},[1052,1053],{"id":481,"depth":278,"text":482},{"id":496,"depth":278,"text":497},{"id":537,"depth":18,"text":538},{"id":549,"depth":18,"text":550,"children":1056},[1057,1058,1059,1060,1061,1062,1063],{"id":556,"depth":278,"text":557},{"id":602,"depth":278,"text":603},{"id":620,"depth":278,"text":621},{"id":667,"depth":278,"text":668},{"id":695,"depth":278,"text":696},{"id":713,"depth":278,"text":714},{"id":728,"depth":278,"text":729},{"id":773,"depth":18,"text":774,"children":1065},[1066],{"id":813,"depth":278,"text":814},{"id":928,"depth":18,"text":929,"children":1068},[1069,1070],{"id":935,"depth":278,"text":936},{"id":946,"depth":278,"text":947},"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":306,"description":1073},"blog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour",[1081,1082,301],"TCO","Apache Kafka","A0o_2xdJiLI6rf6xj4RKsxJNo_A6QN2fYzCp6gaLrFw",{"id":1085,"title":1086,"authors":1087,"body":1090,"company":1491,"createdAt":10,"customerQuote":10,"date":1492,"description":1493,"extension":8,"featured":292,"image":1494,"industry":1495,"isDraft":292,"link":10,"logo":10,"meta":1496,"navigation":7,"order":294,"path":1497,"products":10,"readingTime":1498,"relatedResources":10,"seo":1499,"size":1500,"stem":1501,"tags":1502,"technologies":1504,"useCases":10,"__hash__":1505},"successStories\u002Fsuccess-stories\u002Fhuawei-device.md","From Kafka to Pulsar: Creating A Comprehensive Middleware Platform to Power HUAWEI Mobile Services",[1088,1089],"Lin Lin","Xiaotong Wang",{"type":14,"value":1091,"toc":1469},[1092,1096,1110,1114,1150,1154,1157,1161,1164,1167,1178,1182,1185,1188,1200,1204,1207,1215,1218,1222,1225,1228,1231,1234,1238,1241,1244,1248,1251,1257,1260,1264,1267,1270,1276,1280,1283,1289,1292,1296,1299,1305,1308,1311,1314,1317,1320,1338,1341,1344,1348,1351,1355,1358,1361,1372,1378,1381,1385,1388,1391,1397,1400,1406,1409,1419,1422,1425,1439,1445,1448,1452,1455,1461,1464,1466],[39,1093,1095],{"id":1094},"key-takeaways","Key takeaways",[337,1097,1098,1101,1104,1107],{},[340,1099,1100],{},"Huawei Device sought a message queue solution capable of meeting the diverse service requirements of HMS.",[340,1102,1103],{},"Leveraging Apache Pulsar, Huawei Device developed a middleware platform that includes a unified SDK interface, a dynamic topic discovery mechanism, and consolidated authentication and authorization.",[340,1105,1106],{},"After evaluating various disaster recovery models using Kafka and Pulsar, HMS concluded that Pulsar’s model better satisfied its need for high reliability, low latency, and cost-efficiency.",[340,1108,1109],{},"To improve resource utilization, HMS implemented a shared storage pool to support Pulsar deployments on Kubernetes.",[39,1111,1113],{"id":1112},"about-huawei-mobile-services","About HUAWEI Mobile Services",[47,1115,1116,1121,1122,1127,1128,1133,1134,1133,1139,1144,1145,189],{},[54,1117,1120],{"href":1118,"rel":1119},"https:\u002F\u002Fwww.huawei.com\u002Fen\u002F",[263],"Huawei"," is a leading global provider of information and communications technology (ICT) infrastructure and smart devices. As a part of Huawei, Huawei Device provides key products for Huawei users, like smartphones, personal computers, wearable devices, and ",[54,1123,1126],{"href":1124,"rel":1125},"https:\u002F\u002Fconsumer.huawei.com\u002Fen\u002Fmobileservices\u002F",[263],"HUAWEI Mobile Services"," (HMS). Huawei Device aims to leverage HMS to enrich users’ lives with advanced content and services, catering to a wide array of needs in various fields such as smart homes, health and fitness, mobile offices, apps, smart travel, and entertainment. Notable services running on HMS include ",[54,1129,1132],{"href":1130,"rel":1131},"https:\u002F\u002Fconsumer.huawei.com\u002Fen\u002Fmobileservices\u002Fhealth\u002F",[263],"HUAWEI Health",", ",[54,1135,1138],{"href":1136,"rel":1137},"https:\u002F\u002Fconsumer.huawei.com\u002Fen\u002Fmobileservices\u002Fappgallery\u002F",[263],"AppGallery",[54,1140,1143],{"href":1141,"rel":1142},"https:\u002F\u002Fconsumer.huawei.com\u002Fen\u002Fmobileservices\u002Fvideo\u002F",[263],"HUAWEI Video",", and ",[54,1146,1149],{"href":1147,"rel":1148},"https:\u002F\u002Fconsumer.huawei.com\u002Fen\u002Fmobileservices\u002Fmobilecloud\u002F",[263],"HUAWEI Mobile Cloud",[39,1151,1153],{"id":1152},"challenges","Challenges",[47,1155,1156],{},"Huawei Device faced several challenges when selecting a messaging and streaming system for HMS.",[31,1158,1160],{"id":1159},"maintenance-complexity","Maintenance complexity",[47,1162,1163],{},"“HMS provides consumers with a diversified portfolio of services such as Smart Office, Fitness and Health, Easy Travel, and Entertainment,” said Lin Lin, SDE Expert at Huawei Device. “To support these services, we need to maintain different types of clusters for various scenarios. However, there is hardly an all-in-one message queue solution that can cover all the use cases and meet our requirements.”",[47,1165,1166],{},"The ideal messaging solution for Huawei Device should possess advanced capabilities as follows.",[337,1168,1169,1172,1175],{},[340,1170,1171],{},"Delay queues: Messages can be delivered at any time.",[340,1173,1174],{},"Dead-letter queues: Messages can be automatically sent to dead-letter topics after a certain number of failed delivery attempts, preventing congestion in the current queue.",[340,1176,1177],{},"Massive partitions: A single cluster should support a large number of partitions without affecting performance. This is particularly important in IoT scenarios where the cluster needs to serve numerous clients.",[31,1179,1181],{"id":1180},"cloud-native-compatibility","Cloud-native compatibility",[47,1183,1184],{},"“In a cloud-native era, organizations aim for high efficiency and low costs,” Lin explained. “Against this backdrop, our messaging system should feature excellent scalability so that it is compatible with cloud-native environments.”",[47,1186,1187],{},"The ideal solution should guarantee the following for HMS:",[1189,1190,1191,1194,1197],"ol",{},[340,1192,1193],{},"Scaling should be complete within seconds.",[340,1195,1196],{},"Scaling should not impact any ongoing business.",[340,1198,1199],{},"Data replication during scaling doesn’t occur, or if it does, it doesn’t affect cluster availability.",[31,1201,1203],{"id":1202},"disaster-recovery","Disaster recovery",[47,1205,1206],{},"Most disaster recovery solutions fall into two types:",[337,1208,1209,1212],{},[340,1210,1211],{},"A single cluster across multiple availability zones, which can result in higher network latency and reduced overall performance when synchronizing data.",[340,1213,1214],{},"Multiple clusters in separate availability zones, where users deploy identical active and backup clusters in separate availability zones. In case of a disaster, the standby cluster can take over. However, this deployment method for better redundancy has higher overhead.",[47,1216,1217],{},"“Redundancy is very important for disaster recovery scenarios. Unfortunately, it is not always easy to control your costs,” Lin added.",[31,1219,1221],{"id":1220},"resource-utilization","Resource utilization",[47,1223,1224],{},"A single cluster cannot support an unlimited number of topics or partitions. When traffic surges, clusters are unable to handle requests from multiple services. “To support different services, we choose to deploy a dedicated message queue cluster for each service,” Lin said. “As they are physically isolated, we can use more partitions and topics for a single cluster and avoid resource contention.”",[47,1226,1227],{},"Although using dedicated clusters solves the problem in some cases, it necessitates reserving resources, such as CPU and memory, potentially leading to low resource utilization. “Traffic peaks only occur on certain dates and you can utilize your resources to the maximum during that time. On the flip side, it also means a waste of resources when you have a low traffic volume,” Lin noted.",[47,1229,1230],{},"As an alternative, Huawei Device considered adopting a multi-tenant messaging system capable of supporting numerous topics and partitions within a single cluster. “We hope the new system allows us to isolate resources and is easy for operations and monitoring,” Lin said.",[47,1232,1233],{},"To overcome these challenges, Huawei Device ultimately opted for Apache Pulsar and created respective solutions with the messaging system.",[39,1235,1237],{"id":1236},"a-unified-middle-platform-based-on-apache-pulsar","A unified middle platform based on Apache Pulsar",[47,1239,1240],{},"To support HMS, message queues are now widely applied to various internal services and production systems, addressing common business scenarios such as asynchronous service decoupling, MQTT devices with high resource requirements, big data log streaming and analysis, and data exposure to external parties.",[47,1242,1243],{},"“To address the diverse requirements of different business scenarios, we looked to build a single architecture that can cover most use cases and reduce our development and operations workloads,” said Xiaotong Wang, Senior Software Engineer at Huawei Device. “Therefore, we created a unified middle platform based on Pulsar, which supports multiple client connections with a single cluster. For example, we are using Kafka-on-Pulsar (KoP) to support Kafka client connections.”",[31,1245,1247],{"id":1246},"a-unified-sdk-interface","A unified SDK interface",[47,1249,1250],{},"For some internal clients, HMS provides a unified SDK that encapsulates standard messaging queue pub-sub interfaces. This way, the clients don’t need to know the actual protocol that the server is using.",[47,1252,1253],{},[349,1254],{"alt":1255,"src":1256},"Unified SKID Interface","\u002Fimgs\u002Fsuccess-stories\u002F66a898429cb73a56291d9022_6440e346ad2c5e83b90f322a_image4.webp",[47,1258,1259],{},"“One of the primary benefits is that if we change the protocols or authentication and authorization modes on the server side, we don’t need to make any changes to our clients,” Wang explained. “They only need to focus on the data that they send and the topics that they access.”",[31,1261,1263],{"id":1262},"dynamic-topic-discovery","Dynamic topic discovery",[47,1265,1266],{},"When a service needs to access multiple topics across different clusters, the conventional approach may require configuring multiple cluster addresses for the topics on local files, which can be redundant and difficult to manage. “It is unacceptable to make changes to clients when something like cluster migration, scaling, or node failure happens on the server side,” Wang emphasized.",[47,1268,1269],{},"To address this issue, Huawei Device uses a Cloud Map-based topic registration and discovery mechanism. All topic-related operations such as creation, partition scaling, and migration, are managed through a centralized administration panel. “We register the basic information of topics to a configuration center in Cloud Map and it is dynamically updated,” Wang said. “This way, clients can dynamically connect to the appropriate IP address without impacting themselves.”",[47,1271,1272],{},[349,1273],{"alt":1274,"src":1275},"Dynamic Topic Discovery based on Cloud Map","\u002Fimgs\u002Fsuccess-stories\u002F66a898429cb73a56291d900d_6440e378e8d1570d06ec8dd1_image1.webp",[31,1277,1279],{"id":1278},"unified-authentication-and-authorization","Unified authentication and authorization",[47,1281,1282],{},"The message queues powering HMS have varied implementations for authentication and authorization for a wide range of scenarios, such as SSO Kerberos for big data services. The Pulsar-based platform supports common authentication and authorization models for different data sources. To ensure consistent authentication across the platform, Huawei Device extended Pulsar’s support for SCRAM-SHA-256 authentication when integrating it.",[47,1284,1285],{},[349,1286],{"alt":1287,"src":1288},"Huawei Device Uses Scram","\u002Fimgs\u002Fsuccess-stories\u002F66a898429cb73a56291d9025_6440e3945dfc34b1c0140f9a_image3.webp",[47,1290,1291],{},"“As Pulsar doesn’t support SCRAM-SHA-256 authentication by default, we added the support internally for both brokers and bookies,” Wang said. For interactions between brokers\u002Fbookies and ZooKeeper, Huawei Device adopts SASL authentication with TLS encryption to safeguard against credential exposure.",[39,1293,1295],{"id":1294},"choosing-pulsar-over-kafka-scalability-and-load-balancing","Choosing Pulsar over Kafka: Scalability and load balancing",[47,1297,1298],{},"Previously, Huawei Device used Kafka to power its message queues. By default, Kafka does not automatically migrate traffic during horizontal scaling. “Newly-added nodes in Kafka can’t handle requests immediately and require operators to manually rebalance partitions,” Wang explained. “For data migration, we need to set a rate limit. The challenging part is determining the appropriate threshold. If it is too large, our clients may experience performance issues for reads and writes; if it is too small, the entire migration process may be too long and it requires human intervention.”",[47,1300,1301],{},[349,1302],{"alt":1303,"src":1304},"Data Migration in Kafka","\u002Fimgs\u002Fsuccess-stories\u002F66a898429cb73a56291d9001_6440e3adb614d2080b4faaf1_image9.webp",[47,1306,1307],{},"Due to these scaling difficulties, Wang’s team opted to scale up Kafka clusters by adding more CPU or disk resources, rather than scaling out. If this wasn’t feasible, Wang admitted that they had to create a new cluster. “Creating another cluster for data migration leads to extra operational costs. It also means that we may need to maintain multiple clusters for a single business module as it grows,” Wang added.",[47,1309,1310],{},"In contrast to Kafka, Pulsar separates computing from storage, which is powered by another open-source project, Apache BookKeeper. When a new node (bookie) joins the cluster, it is immediately available to serve writes. Messages are evenly stored across bookies and historical data is smoothly removed according to the configured retention and expiry policy.",[47,1312,1313],{},"“Scaling out a Kafka cluster might require a few hours. Even if we decide to create a new cluster and migrate topics to it, the process can still take tens of minutes,” Wang said. “By contrast, BookKeeper only takes a couple of minutes to finish scaling.”",[47,1315,1316],{},"Pulsar also distinguishes itself from Kafka in terms of load balancing. In Kafka, topic partitions are essentially bound to brokers, meaning human intervention may be necessary for load balancing. In the event of node failures, operators must manually migrate impacted partitions to another node. When traffic is not evenly distributed across Kafka brokers, operators may need to rebalance partitions themselves.",[47,1318,1319],{},"“To solve the load balancing pain points when using Kafka, our operators attempted to place topics for different services on separate machines, thereby physically isolating them,” Wang added. “However, this approach could result in additional maintenance overhead.”",[47,1321,1322,1323,1133,1328,1144,1333,189],{},"Pulsar achieves load balancing with an automatic load-shedding mechanism. Users can set a threshold and an overloaded broker will unload the excess traffic to other nodes. Pulsar supports three types of strategies - ",[54,1324,1327],{"href":1325,"rel":1326},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002F2.11.x\u002Fadministration-load-balance\u002F#thresholdshedder",[263],"ThresholdShedder",[54,1329,1332],{"href":1330,"rel":1331},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002F2.11.x\u002Fadministration-load-balance\u002F#overloadshedder",[263],"OverloadShedder",[54,1334,1337],{"href":1335,"rel":1336},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002F2.11.x\u002Fadministration-load-balance\u002F#uniformloadshedder",[263],"UniformLoadShedder",[47,1339,1340],{},"“In Pulsar, if a broker goes down, the partition previously owned by the broker will be automatically transferred to another broker,” Wang said. “To balance traffic, we can choose different strategies based on various algorithms or factors,  such as resource usage averages.”",[47,1342,1343],{},"When using Pulsar’s load-balancing strategies, Wang found it difficult to set an appropriate traffic threshold. For instance, using a low threshold value might lead to frequent traffic rebalancing. To address this issue, Wang’s team redesigned Pulsar’s rebalancing algorithms, which reduced the load difference between brokers to less than 10% and increased the overall resource utilization by approximately 30%. Currently, they are working on a proposal and look to submit it to the Pulsar community.",[39,1345,1347],{"id":1346},"building-a-disaster-recovery-strategy-kafka-vs-pulsar","Building a disaster recovery strategy: Kafka vs. Pulsar",[47,1349,1350],{},"To address challenges in disaster recovery, Wang’s team looked to design a strategy featuring high reliability, low latency, and low costs. The messaging system must remain available during a single AZ failure with three replicas.",[31,1352,1354],{"id":1353},"high-reliability","High reliability",[47,1356,1357],{},"“When using Kafka with acks=all, the ISR (in-sync replica) algorithm requires all broker nodes in the ISR list to complete synchronization before responding to the client,” Wang said. “Consequently, network fluctuations across AZs can impact Kafka’s write performance and latency. However, this approach offers high reliability, tolerating single AZ failures without making the message queue unavailable.”",[47,1359,1360],{},"For Pulsar, Wang’s team created a policy with similar reliability while it was less affected by network instability. In this setting (Figure 5), bookies are assigned AZ labels and they are selected as follows:",[1189,1362,1363,1366,1369],{},[340,1364,1365],{},"Randomly select a bookie from the available nodes, such as Bookie1 from AZ1.",[340,1367,1368],{},"For the second replica (exclude Bookie1), randomly select one from AZ2 and AZ3, such as Bookie3.",[340,1370,1371],{},"For the third replica (exclude all bookies in AZ1 and AZ2), select Bookie5 from AZ3.",[47,1373,1374],{},[349,1375],{"alt":1376,"src":1377},"Disaster Recovery Scnario","\u002Fimgs\u002Fsuccess-stories\u002F66a898429cb73a56291d9009_6440e3cb7c2a323fe6bbd4cb_image5.webp",[47,1379,1380],{},"For an ack quorum of 2, this setup ensures that the client receives a successful message write response once any two AZs have completed writing. “One advantage Pulsar offers over Kafka is that it requires only two AZs to succeed in writing data before responding as successful, whereas Kafka’s ISR mechanism necessitates all three AZs in the list to complete the write. Thus, writes to Pulsar are less affected by inter-AZ network fluctuations compared to Kafka,” Wang added.",[31,1382,1384],{"id":1383},"low-latency-and-costs","Low latency and costs",[47,1386,1387],{},"Some HMS services may require low latency and can tolerate slight data inconsistencies during a single AZ failure while message queue availability must be guaranteed. For this use case, Wang’s team designed different settings for both Kafka and Pulsar.",[47,1389,1390],{},"For Kafka, inter-AZ network fluctuations can lead to unstable writing latency for a multi-AZ cluster. Therefore, Huawei Device deployed independent Kafka clusters within each AZ, without any data synchronization between them. One of them served as a backup, such as Cluster2 in AZ2 in Figure 6, ensuring service availability during AZ1 failures. However, this solution results in resource waste and difficulty in maintaining data consistency during cluster switches.",[47,1392,1393],{},[349,1394],{"alt":1395,"src":1396},"Two Kafka Clusters in Two AZS","\u002Fimgs\u002Fsuccess-stories\u002F66a898429cb73a56291d901b_6440e3e92956cb34cddea417_image7.webp",[47,1398,1399],{},"For Pulsar, Wang’s team created a two-AZ deployment configuration that meets low-latency requirements while allowing for slight data inconsistencies during a single AZ failure (see Figure 7 for details). “We optimized Pulsar by supporting AZ labels for brokers and adjusting the bookie selection process,” Wang explained.",[47,1401,1402],{},[349,1403],{"alt":1404,"src":1405},"Single Pulsar Cluster across two AZS","\u002Fimgs\u002Fsuccess-stories\u002F66a898429cb73a56291d9029_6440e409b1d0339b80ce5435_image6.webp",[47,1407,1408],{},"With the updated logic, bookies are selected in the following way.",[1189,1410,1411,1413,1416],{},[340,1412,1365],{},[340,1414,1415],{},"For the second replica (exclude Bookie1), randomly select one from AZ2, such as Bookie3.",[340,1417,1418],{},"For the third replica, although there is no bookie to select from other AZs, the updated logic allows Pulsar to check the label of brokers (for example, AZ1 of Broker1) and write the last copy to another available bookie in AZ1, like Bookie2.",[47,1420,1421],{},"With an ack quorum of 2, writes are still considered successful even if data is replicated within the same AZ (Bookie1 and Bookie2), providing low write latency with minor data inconsistencies. “The benefit of using Pulsar with this setting is that the cluster remains available when an AZ fails and you don’t need to create a backup cluster,” Wang said. “Unlike Kafka, Pulsar allows better use of cluster resources.”",[47,1423,1424],{},"However, using a single Pulsar cluster across multiple AZs does not guarantee zero downtime. In a few cases, a shared backup cluster is still needed, but it is only used in specific scenarios as follows:",[337,1426,1427,1430,1433,1436],{},[340,1428,1429],{},"Unexpected bugs",[340,1431,1432],{},"Slow cluster automatic recovery",[340,1434,1435],{},"Simultaneous failures in multiple AZs",[340,1437,1438],{},"Untimely bookie scaling",[47,1440,1441],{},[349,1442],{"alt":1443,"src":1444},"Shared Pulsar Backup Cluster","\u002Fimgs\u002Fsuccess-stories\u002F66a898429cb73a56291d9018_6440e4335dfc349e8814c421_image2.webp",[47,1446,1447],{},"“Using the Pulsar-based disaster recovery strategy, we have increased CPU utilization from 20% to 40%,” Wang noted. “Unlike Kafka, the Pulsar backup cluster is shared so that we can significantly reduce resource waste.”",[39,1449,1451],{"id":1450},"containerization-for-better-resource-utilization","Containerization for better resource utilization",[47,1453,1454],{},"In order to optimize resource utilization, Huawei Device decided to containerize the messaging system for HMS. Pulsar’s stateless brokers make it much easier to migrate workloads onto Kubernetes. “Thanks to its decoupled architecture, Pulsar enables us to dynamically scale the number of brokers and adjust CPU or memory resources as required, thereby maximizing resource utilization,” Wang explained.",[47,1456,1457],{},[349,1458],{"alt":1459,"src":1460},"Shared Storage Resources","\u002Fimgs\u002Fsuccess-stories\u002F66a898429cb73a56291d9012_6440e44b5dfc34b4c214d450_image8.webp",[47,1462,1463],{},"Furthermore, Huawei Device has created a unified bookie node pool to support multiple broker clusters. “With a shared bookie pool, we can effectively utilize cluster storage resources and enhance our capacity to handle unexpected traffic spikes,” Wang noted.",[39,1465,929],{"id":928},[47,1467,1468],{},"Huawei Device has successfully adopted Pulsar as the unified middle platform powering HMS, leveraging its unique capabilities to address its challenges and requirements. With Pulsar, Huawei Device has improved operation efficiency, achieved better scalability, established an effective disaster recovery strategy, and optimized resource utilization.",{"title":17,"searchDepth":18,"depth":18,"links":1470},[1471,1472,1473,1479,1484,1485,1489,1490],{"id":1094,"depth":18,"text":1095},{"id":1112,"depth":18,"text":1113},{"id":1152,"depth":18,"text":1153,"children":1474},[1475,1476,1477,1478],{"id":1159,"depth":278,"text":1160},{"id":1180,"depth":278,"text":1181},{"id":1202,"depth":278,"text":1203},{"id":1220,"depth":278,"text":1221},{"id":1236,"depth":18,"text":1237,"children":1480},[1481,1482,1483],{"id":1246,"depth":278,"text":1247},{"id":1262,"depth":278,"text":1263},{"id":1278,"depth":278,"text":1279},{"id":1294,"depth":18,"text":1295},{"id":1346,"depth":18,"text":1347,"children":1486},[1487,1488],{"id":1353,"depth":278,"text":1354},{"id":1383,"depth":278,"text":1384},{"id":1450,"depth":18,"text":1451},{"id":928,"depth":18,"text":929},"Huawei Device","2023-04-20","Learn how Huawei Device used Apache Pulsar to build a unified platform for HUAWEI Mobile Services and why it migrated from Kafka to Pulsar.","\u002Fimgs\u002Fsuccess-stories\u002F679569be695cfbd6d2fbadc6_SN-SuccessStories-huawei.webp","Telecommunications",{},"\u002Fsuccess-stories\u002Fhuawei-device","10 min read",{"title":1086,"description":1493},"10000+ employees","success-stories\u002Fhuawei-device",[1503,1082],"Apache Pulsar",[1503,1082],"kVAR5pZ-GrlHXcpM9BZ9W0wfjWvSPcuTneK3cDqb-8I",[1507,1517],{"id":1508,"title":1088,"bioSummary":10,"email":10,"extension":8,"image":10,"linkedinUrl":10,"meta":1509,"position":1514,"stem":1515,"twitterUrl":10,"__hash__":1516},"authors\u002Fauthors\u002Flin-lin.md",{"body":1510},{"type":14,"value":1511,"toc":1512},[],{"title":17,"searchDepth":18,"depth":18,"links":1513},[],"SDE Expert, Huawei Device","authors\u002Flin-lin","wwde51iFMvI2CNQgvaLN86KrjL4j3J_T_TDDanbHBTE",{"id":1518,"title":1089,"bioSummary":10,"email":10,"extension":8,"image":10,"linkedinUrl":10,"meta":1519,"position":1524,"stem":1525,"twitterUrl":10,"__hash__":1526},"authors\u002Fauthors\u002Fxiaotong-wang.md",{"body":1520},{"type":14,"value":1521,"toc":1522},[],{"title":17,"searchDepth":18,"depth":18,"links":1523},[],"Senior Software Engineer, Huawei Device","authors\u002Fxiaotong-wang","E1Cw1Qqdb0_AoNswX7RaX0VC77rWDdbnoBF9P-Pb5_M",[1528,1536,1541],{"path":1529,"title":1530,"date":1531,"image":1532,"link":-1,"collection":1533,"resourceType":1534,"score":1535,"id":1529},"\u002Fblog\u002Frevolutionizing-data-connectivity-introducing-streamnatives-universal-connectivity-uniconn-for-seamless-real-time-data-access","Revolutionizing Data Connectivity: Introducing StreamNative's Universal Connectivity (UniConn) for Seamless Real-Time Data Access","2024-09-10","\u002Fimgs\u002Fblogs\u002F66df5a0f1b3a6adb917fd577_Universal-Connectivity_BlogPost.png","blogs","Blog",1.1,{"path":1537,"title":1538,"date":1539,"image":1540,"link":-1,"collection":1533,"resourceType":1534,"score":1535,"id":1537},"\u002Fblog\u002Fintroducing-streamnative-serverless-instant-start-seamless-scaling-and-effortless-data-streaming","Introducing StreamNative Serverless: Instant Start, Seamless Scaling, and Effortless Data Streaming","2024-09-09","\u002Fimgs\u002Fblogs\u002F66df5a1f1e964b7302f7acbd_Serverless_BlogPost.png",{"path":1542,"title":1543,"date":1544,"image":1545,"link":-1,"collection":1533,"resourceType":1534,"score":1535,"id":1542},"\u002Fblog\u002Fno-data-rebalance-needed-kafka-and-pulsar","No Data Rebalance Needed! That's Why We Reimagined Kafka with Apache Pulsar to Make it 1000x More Elastic ","2024-04-29","\u002Fimgs\u002Fblogs\u002F662fd25d01990449cd5ffd58_broker-scaling.png",1775235725333]