[{"data":1,"prerenderedAt":1518},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":24,"navbar-pricing-featured":306,"blog-\u002Fblog\u002Fursa-reimagine-apache-kafka-for-the-cost-conscious-data-streaming":1086,"blog-authors-\u002Fblog\u002Fursa-reimagine-apache-kafka-for-the-cost-conscious-data-streaming":1481,"related-\u002Fblog\u002Fursa-reimagine-apache-kafka-for-the-cost-conscious-data-streaming":1498},{"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,"canonicalUrl":289,"category":290,"createdAt":289,"date":291,"description":292,"extension":8,"featured":7,"image":293,"isDraft":294,"link":289,"meta":295,"navigation":7,"order":296,"path":297,"readingTime":298,"relatedResources":289,"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",null,"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":26,"description":292},"blog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025",[302,303,304],"Announcements","Real-Time","Forrester","5Nr1vAcqlQ7yFQfdL0a3MLsNFerVmEOQJXD9Twz5lx8",{"id":307,"title":308,"authors":309,"body":314,"canonicalUrl":289,"category":1073,"createdAt":289,"date":1074,"description":1075,"extension":8,"featured":7,"image":1076,"isDraft":294,"link":289,"meta":1077,"navigation":7,"order":296,"path":1078,"readingTime":1079,"relatedResources":289,"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","CDUawvFKTs_AD8usvmIcTleU3mbfA0QAoPZM6xfVuo8",{"id":1087,"title":1088,"authors":1089,"body":1091,"canonicalUrl":289,"category":289,"createdAt":289,"date":1473,"description":1088,"extension":8,"featured":294,"image":1474,"isDraft":294,"link":289,"meta":1475,"navigation":7,"order":296,"path":378,"readingTime":289,"relatedResources":289,"seo":1476,"stem":1477,"tags":1478,"__hash__":1480},"blogs\u002Fblog\u002Fursa-reimagine-apache-kafka-for-the-cost-conscious-data-streaming.md","Ursa: Reimagine Apache Kafka for the Cost-Conscious Data Streaming",[1090],"Sijie Guo",{"type":15,"value":1092,"toc":1458},[1093,1096,1099,1103,1106,1120,1123,1128,1132,1135,1137,1142,1150,1165,1169,1172,1175,1183,1186,1189,1192,1195,1199,1202,1205,1217,1220,1223,1226,1230,1233,1235,1240,1243,1254,1258,1261,1264,1272,1275,1278,1282,1285,1288,1291,1293,1298,1301,1304,1307,1310,1313,1316,1319,1322,1326,1329,1331,1342,1348,1352,1355,1358,1360,1365,1368,1371,1374,1377,1381,1384,1387,1412,1414,1419,1422,1430,1433,1437,1440,1456],[48,1094,1095],{},"Today, we are really excited to unveil the next-generation data streaming engine - Ursa, which powers the entire StreamNative Cloud. Ursa is a data streaming engine that speaks the native Kafka protocol and is built directly on top of Lakehouse storage. Developed atop Apache Pulsar, Ursa removes the need for BookKeeper and ZooKeeper, pushing the architectural tenets of Pulsar to new heights, specifically tailored for the cost-conscious economy.",[48,1097,1098],{},"Both Kafka and Pulsar are robust open-source platforms. Our development of the Ursa engine leverages the extensive knowledge and operational insights we've gained from our years working with both Pulsar and Kafka. Throughout this post, I will explore the reasons behind Ursa's creation, highlight its benefits, and provide insight into its underlying mechanisms.",[40,1100,1102],{"id":1101},"understand-the-origin-of-apache-kafka","Understand the origin of Apache Kafka",[48,1104,1105],{},"Before diving into Ursa, it’s important to grasp the significance of Kafka. Originally developed at LinkedIn, Kafka was open-sourced in 2011 and quickly became the go-to framework for building data streaming platforms. It emerged during the On-prem \u002F Hadoop Era (2000 to 2010), a time characterized by on-premises deployments with slow network speeds. Infrastructure software from this period was optimized for rack awareness to compensate for these limitations. Kafka, designed under these conditions, coupled data serving and storage on the same physical units—an approach that matched the technological constraints of the time.",[48,1107,1108,1109,1114,1115,190],{},"Fast forward to 2015, and the landscape has drastically shifted, particularly with the move towards cloud-native environments. Despite these changes, Kafka's core architecture has remained largely unchanged. Organizations have attempted to transition Kafka to the cloud, but the reality is that Kafka is cumbersome and costly to operate at scale in modern settings. The problem lies not with Kafka's API but with its implementation, which was conceived for on-prem data centers. This tightly coupled architecture is ill-suited for the cloud, leading to significant data rebalancing challenges when adjusting cluster topologies, resulting in high inter-AZ bandwidth costs and ",[55,1110,1113],{"href":1111,"rel":1112},"https:\u002F\u002Fdeveloper.paypal.com\u002Fcommunity\u002Fblog\u002Fscaling-kafka-to-support-paypals-data-growth\u002F",[264],"potential service disruptions",". Managing a Kafka cluster in such environments requires extensive, specialized tooling and ",[55,1116,1119],{"href":1117,"rel":1118},"https:\u002F\u002Fwww.confluent.io\u002Fblog\u002Funderstanding-and-optimizing-your-kafka-costs-part-2-development-and-operations\u002F",[264],"a dedicated support team",[48,1121,1122],{},"‍",[48,1124,1125],{},[351,1126],{"alt":18,"src":1127},"\u002Fimgs\u002Fblogs\u002F66f16f5ec93b13a30217f7ee_66424e2cda8b078d8259f8f9_Sl9T0EpCjEv28bNK-qb98CzS__7jd7usC6WE-bOn27Yp5A6OEzdkg06QdkPJspfk3xohJFXN0vdLgGVTxfWdu9ljRI5vubTAeNRmiMLZfr0vWyfLfsOzWpML3UCMpxeXd5NTYK9SZuIGQTMJsnNHS3c.png",[40,1129,1131],{"id":1130},"pulsar-reimagine-kafka-with-a-rebalance-free-architecture","Pulsar: Reimagine Kafka with a Rebalance-Free Architecture",[48,1133,1134],{},"In contrast to Kafka, Pulsar emerged during the cloud-native era (2010 to 2020), a time marked by the rise of containerized deployments and significantly faster network speeds. As organizations transitioned from on-premises to cloud environments, system designs increasingly prioritized elasticity over cost. This shift led to the widespread adoption of architectures that separate compute from storage, a strategy exemplified by platforms like Snowflake and Databricks.",[48,1136,1122],{},[48,1138,1139],{},[351,1140],{"alt":18,"src":1141},"\u002Fimgs\u002Fblogs\u002F66f16f5dc93b13a30217f7e8_66424e2cb3021b286e4eb919_1hkld2uxKe3jqe-8wocFapU2jkp8pEkFC-mz4hYGztZRY-SPeh4fRekJ0Z0KxGfVn7O2B5kdeRHYNGENUvKZUy0xGGX-lvcVHCMB4D2DCq-AbE6J4bVqHhQmu_YRDgA-9pE6JRDKODagUShx_-nN9O4.png",[48,1143,1144,1145,1149],{},"Pulsar embraced this modern design by decoupling data serving capabilities from the storage layer. With this architecture, it became the pioneer in the market, making it ",[55,1146,1148],{"href":1147},"\u002Fblog\u002Fno-data-rebalance-needed-kafka-and-pulsar","1000x more elastic than Apache Kafka",". This architectural innovation has made Pulsar extremely attractive to those dealing with the challenges of data rebalancing when operating Kafka clusters.",[48,1151,1152,1153,1158,1159,1164],{},"Pulsar's architecture is rebalance-free and supports a unified messaging model that accommodates data streaming and message queuing. Its features, like built-in ",[55,1154,1157],{"href":1155,"rel":1156},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002Fconcepts-multi-tenancy\u002F",[264],"multi-tenancy"," and ",[55,1160,1163],{"href":1161,"rel":1162},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002Fconcepts-replication\u002F",[264],"geo-replication",", are key reasons many prefer Pulsar over other data streaming technologies.",[40,1166,1168],{"id":1167},"can-we-further-reduce-costs-in-this-complex-cost-conscious-economy","Can we further reduce costs in this complex, cost-conscious economy?",[48,1170,1171],{},"Today, there is a heightened focus on operational efficiency and cost reduction, which has slowed the migration to cloud solutions and, in some cases, even reversed it. We navigate a complex landscape that straddles both on-premises and cloud environments, where cost and network efficiency are critical considerations. This scenario underscores the need to shift towards more cost-aware and sustainable architectural approaches in data streaming services.",[48,1173,1174],{},"To meet the evolving demands in the current environment and further reduce the costs of running a data streaming platform, we need to revisit and possibly redesign the architecture we have built on with Apache Pulsar. We have identified the following main areas:",[48,1176,1177,1178,1182],{},"Infrastructure Cost: As mentioned in our guide on ",[55,1179,1181],{"href":1180},"\u002Fblog\u002Fa-guide-to-evaluating-the-infrastructure-costs-of-apache-pulsar-and-apache-kafka","evaluating the infrastructure costs of Apache Pulsar and Apache Kafka",", networking often represents the most significant expense for a data streaming platform. Because Pulsar operates as an AP (Availability and Performance) system, cross-AZ traffic incurs substantial costs due to the necessity for cross-AZ replication to ensure high availability and reliability. This availability, unfortunately, comes at a high cost. The cost of inter-AZ data transfer from replication can balloon for high-throughput workloads, accounting for up to 90% of infrastructure costs when self-managing Apache Kafka. Is it possible to completely eliminate cross-AZ traffic from Pulsar?",[48,1184,1185],{},"Operational Cost: Pulsar’s modular design, which includes components like ZooKeeper for metadata management and BookKeeper for ultra-low latency log storage, leverages the elasticity of Kubernetes but can be challenging for beginners. What if we could replace these Keeper services to simplify operations?",[48,1187,1188],{},"Migration Cost: Although Pulsar provides a unified messaging and streaming API, some applications are still written using the Kafka API. Could making Pulsar compatible with the Kafka API eliminate the need for costly application rewrites?",[48,1190,1191],{},"Integration Cost: Pulsar could instantly tap into the vast Kafka ecosystem by supporting the Kafka API, blending robust architectural design with an established user base.",[48,1193,1194],{},"These considerations mark the beginning of our new journey to reimagine Kafka and Pulsar, aiming for a more cost-effective data-streaming architecture that aligns with our industry's evolving needs.",[40,1196,1198],{"id":1197},"object-storage-lakehouse-are-all-you-need","Object Storage & Lakehouse are All You Need",[48,1200,1201],{},"Beyond the cost considerations already discussed, streaming data ultimately finds its home in a data lakehouse, which serves as the foundation for all subsequent analytical processing. However, connecting data streams to these lakehouses typically requires additional integrations to transfer data from the streaming system to the designated lakehouse, incurring significant costs for networking and compute resources.",[48,1203,1204],{},"Given these expenses, we pondered whether developing a Kafka-compatible data streaming system that runs directly atop a data lakehouse would be feasible. This approach could address several of the major challenges we face with current systems:",[1206,1207,1208,1211,1214],"ol",{},[342,1209,1210],{},"Cost Reduction: Operating directly on a lakehouse would significantly cut costs, as no major cloud provider charges for data transfer between VMs and object storage. For example, AWS has dedicated countless engineering resources to ensure the reliability and scalability of S3, thereby reducing the operational burden on users.",[342,1212,1213],{},"Simplified Management: Such a system would be easier to manage without needing local disk storage.",[342,1215,1216],{},"Immediate Data Availability: Data would be instantly available in ready-to-use lakehouse formats, allowing for more efficient and cost-effective real-time ETL processes and bypassing the costs of complex networking and bespoke integrations.",[48,1218,1219],{},"Implementing this concept is no small feat. Building a low-latency streaming infrastructure on top of the inherently high-latency lakehouse storage while maintaining full compatibility with the Kafka protocol and adhering to strict data agreements between streaming and lakehouse platforms poses a significant challenge.",[48,1221,1222],{},"So we asked ourselves: “What would Kafka or Pulsar look like if it was redesigned from the ground up today to run in the modern cloud data stack, directly on top of a lakehouse (which is the destination for most of the data streams) over commodity object storage, with no ZooKeeper and BookKeeper to manage, but still had to support the existing Kafka and Pulsar protocols?”",[48,1224,1225],{},"Ursa is our answer to that question.",[40,1227,1229],{"id":1228},"introducing-ursa","Introducing Ursa",[48,1231,1232],{},"Ursa is an Apache Kafka API-compatible data streaming engine that runs directly on top of commodity object stores like AWS S3, GCP GCS, and Azure Blob Storage and stores streams in lakehouse table formats (such as Hudi, Iceberg, Delta Lake). This setup makes data immediately available in the lakehouse and simplifies management by eliminating the need for ZooKeeper and, soon, BookKeeper—thereby reducing inter-AZ bandwidth costs.",[48,1234,1122],{},[48,1236,1237],{},[351,1238],{"alt":18,"src":1239},"\u002Fimgs\u002Fblogs\u002F66f16f5ec93b13a30217f7fe_66424e2c8592d4b0ddd495ea_XYmrGwDv7ngDdZZ5yWt78p1Tmpo-4aXKWZujklw6GkGAW1XoJnPu3Ng-A1RJZPuRwJDmJmJwsYgCYY7-OlzDklqmo_d6QZHdhHclurjvYT_2fl5Vt-v1uqgeCqZ0DRjd_rhkO-dKq-T8_uh9aXcksXs.png",[48,1241,1242],{},"That’s a lot to digest, so let’s unpack it by highlighting three major features within Ursa.",[1206,1244,1245,1248,1251],{},[342,1246,1247],{},"Kafka API Compatibility",[342,1249,1250],{},"Native Lakehouse Storage",[342,1252,1253],{},"No Keepers",[40,1255,1257],{"id":1256},"kafka-api-compatibility-embracing-the-best-of-pulsar-kafka","Kafka API Compatibility - Embracing the Best of Pulsar & Kafka",[48,1259,1260],{},"The development of the Ursa engine began with a project called KoP (Kafka-on-Pulsar). The original idea of KoP was to develop a Kafka API-compatible layer using the distributed log infrastructure of Apache Pulsar and its pluggable protocol handler framework. The project gained significant traction in the Apache Pulsar community and has been adopted by large-scale tech companies (such as WeChat, Didi, etc) to migrate their Kafka workloads to Apache Pulsar.",[48,1262,1263],{},"However, we quickly realized that more than KoP was needed to fulfill the mission of building a data streaming engine directly on a lakehouse. We needed to revolutionize the Kafka and Pulsar protocol implementations to fit the broad vision we had laid out with Ursa.",[48,1265,1266,1267,1271],{},"Hence, we took the experience gained in building KoP and evolved it into KSN (",[55,1268,1270],{"href":1269},"\u002Fblog\u002Fkafka-on-streamnative-bringing-enterprise-grade-kafka-support-to-streamnative-pulsar-clusters","Kafka-on-StreamNative","), which became the core foundation of the Ursa Engine.",[48,1273,1274],{},"The Ursa Engine is compatible with Apache Kafka versions from 0.9 to 3.4. Modern Kafka clients will automatically negotiate protocol versions or utilize an earlier one that Ursa accepts. In addition to the basic produce and consume protocols, Ursa also supports Kafka-compatible transaction semantics and APIs and has built-in support for a schema registry.",[48,1276,1277],{},"With the Ursa Engine, your Kafka applications can directly work and run on StreamNative Cloud without rewriting your code. This eliminates the costs of rewriting and migrating your existing Kafka applications to the Apache Pulsar protocol. Ursa incorporates interoperability between Kafka and Pulsar protocols, enabling you to either begin developing new streaming applications with Pulsar's unified protocol, continue using the Kafka protocol if you already have Kafka developers, or start migrating some of your existing Kafka applications immediately. This also allows you to immediately enjoy the benefits of Apache Pulsar (such as multi-tenancy, geo-replication, etc.) along with a robust Kafka ecosystem. You get the best of both worlds.",[40,1279,1281],{"id":1280},"built-on-top-of-lakehouse-unify-data-streaming-and-data-lakehouse","Built on top of Lakehouse - Unify Data Streaming and Data Lakehouse",[48,1283,1284],{},"Ursa is built on top of lakehouse, enabling StreamNative users to store their Pulsar & Kafka topics and associated schemas directly into lakehouse tables. Our goal with Ursa is to simplify the process of feeding streaming data into your lakehouse.",[48,1286,1287],{},"Ursa utilizes the innovations we have developed to evolve the Pulsar tiered storage. Pulsar was the first data streaming technology to introduce tiered storage, which offloads sealed log segments into commodity object storage like S3, GCS, and Azure Blob Store. While trying to enhance the offloading performance and streamline the process, we realized that tiered storage could evolve for data destined for lakehouses. We asked ourselves: Why can't we store the data directly in lakehouses?",[48,1289,1290],{},"Taking a step back, Pulsar stores the data first in a giant, aggregated write-ahead log (WAL) backed by Apache BookKeeper (as illustrated in Figure 4 below), consolidating data entries from different topics with a smart distributed index for fast lookups.",[48,1292,1122],{},[48,1294,1295],{},[351,1296],{"alt":18,"src":1297},"\u002Fimgs\u002Fblogs\u002F66f16f5ec93b13a30217f7f8_66424e2ce7e2434485798b67_kgy1v3W0w5XE5mW7VrxLq2Kv20U5aZOvo85S5gamUuCwXu_5jebRuT4nUGHSgh8rJ32ipMmevwxuNfBGvqsEtmumWkTk6AVMqyPsiT4W87_lTtGHFrwDRSgFS0eIEzn8mWQQbPU3hraFhr2cimSe8sg.png",[48,1299,1300],{},"After the data is persisted to the WAL, it will be compacted and stored as continuous data objects in commodity object storage. Thus, in Pulsar’s design, there is no actual data tiering. Writing or moving data to object storage is effectively a “compaction” operation that reorganizes the data stored in WAL into continuous data objects grouped by topics for faster scans and lookups.",[48,1302,1303],{},"Given this capability, if the system is intelligent enough when compacting data, we can leverage schema information to store the data in columnar formats directly in standard lakehouse formats. This approach would make the data immediately available in the lakehouse, eliminating the need for bespoke integration between a data streaming platform and a data lakehouse.",[48,1305,1306],{},"These insights have led to the development of Lakehouse Storage, which now serves as Ursa's primary storage. We now refer to it simply as \"Lakehouse Storage,\" eliminating traditional data \"tiering.\" The data can be made immediately accessible in the lakehouse.",[48,1308,1309],{},"Hence, in the Ursa engine, instead of compacting the data into Pulsar’s proprietary storage format, the Ursa engine can now compact the data into other open standard formats, like lakehouse formats such as Apache Hudi, Apache Iceberg, and Delta Lake. Ursa taps into the schema registry during this compaction process to generate lakehouse metadata while managing schema mapping, evolution, and type conversions. This system eliminates the need for manual mappings, which often break when the upstream application updates. Data schemas are enforced upstream as part of the data stream contract—ensuring that incompatible data is detected early and not processed.",[48,1311,1312],{},"In addition to managing schemas, Ursa continuously compacts small parquet files generated by the streaming data into larger files to maintain good read performance. We are collaborating with lakehouse vendors such as Databricks, OneHouse, and others to offload some of these complexities, enabling users to optimize their use of these products for superior performance.",[48,1314,1315],{},"Lakehouse storage is currently available as a Public Preview feature in StreamNative Hosted and BYOC (Bring Your Own Cloud) deployments. StreamNative users can now access their data streams as Delta Lake tables, with the development of Iceberg & Hudi tables coming soon.",[40,1317,1253],{"id":1318},"no-keepers",[48,1320,1321],{},"Pulsar is designed as a modular system with distinct components for different functionalities, such as ZooKeeper for metadata management and BookKeeper for ultra-low latency log storage. While this design capitalizes on the elasticity of Kubernetes, it also introduces overhead that can challenge beginners. As a result, this inherent barrier has prompted initiatives within the Pulsar community and StreamNative to replace these 'Keeper' services, including ZooKeeper and BookKeeper.",[32,1323,1325],{"id":1324},"no-zookeeper","No ZooKeeper",[48,1327,1328],{},"Traditionally, Apache Pulsar has relied on Apache ZooKeeper for all coordination and metadata. Although ZooKeeper is a robust and consistent metadata service, it is difficult to manage and tune. Instead of simply replacing ZooKeeper, we adopted a more sophisticated approach by introducing a pluggable metadata interface, enabling Pulsar to support additional backends such as Etcd. However, there remains a need to design a system that can effectively overcome the limitations of existing solutions like ZooKeeper and Etcd:",[48,1330,1122],{},[339,1332,1333,1336,1339],{},[342,1334,1335],{},"Fundamental Limitation: These systems are not horizontally scalable. An operator cannot add more nodes and expand the cluster capacity since each node must store the entire dataset for the cluster.",[342,1337,1338],{},"Ineffective Vertical Scaling: Since the maximum dataset and throughput are capped, the next best alternative is to scale vertically by increasing CPU and IO resources on the same nodes. However, this stop-gap solution doesn’t fully resolve the issue.",[342,1340,1341],{},"Inefficient Storage: Storing more than 1 GB of data in these systems is highly inefficient due to their periodic snapshots. This snapshot process repeatedly writes the same data, consuming all the IO resources and slowing down write operations.",[48,1343,1344,1345,190],{},"Oxia represents a step toward overcoming these limitations and scaling Pulsar’s ability to support from 1 million topics to hundreds of millions, with efficient hardware and storage. Oxia is currently available for public preview on StreamNative Cloud. For more details about Oxia, you can check out our ",[55,1346,1347],{"href":975},"blog post",[32,1349,1351],{"id":1350},"no-bookkeeper","No BookKeeper",[48,1353,1354],{},"BookKeeper is a high-performance, scalable log storage system that is the secret behind Pulsar’s ability to achieve a rebalance-free architecture and deliver vastly greater elasticity than Apache Kafka. However, BookKeeper’s design depends on replicating data across multiple storage nodes in different availability zones to ensure high availability. It is ideally suited for latency-optimized workloads. Deploying BookKeeper for high-volume data streaming workloads involves significant inter-AZ traffic, making operating in a multi-AZ deployment expensive. The cost of inter-AZ data transfer from replication can balloon for high-throughput workloads, accounting for up to 90% of infrastructure costs when self-managing Apache Kafka. Although deploying Pulsar in a single-zone environment could reduce cross-AZ network traffic, it would trade off availability for cost and performance.",[48,1356,1357],{},"Since Ursa already utilizes object storage, what if we eliminate the need for BookKeeper as a WAL storage solution and instead directly leverage commodity object storage (S3, GCS, or ABS) as a write-ahead log for data storage and replication? This approach would eliminate the need for inter-AZ data replication and its associated costs. This is the essence of introducing a cost-optimized WAL based on object storage, which is at the heart of the Ursa engine (as illustrated in the diagram below).",[48,1359,1122],{},[48,1361,1362],{},[351,1363],{"alt":18,"src":1364},"\u002Fimgs\u002Fblogs\u002F66f16f5ec93b13a30217f7f5_66424e2d89a600a903ee8a19_Q_-JUi2fdM54P9J9Vl6q302K60L2fK8vE4R_zQIrGG1Hm5Yy1hNM_GVWb_YP86oFfNEUNwRQnWgF7NqTCvVArVhCehAfNz8icuPpsEdJl8lnM7TlPYQubjKmzmLHEvNP-o0c57t28_vH-UGQB4jUaLI.png",[48,1366,1367],{},"With this cost-optimized WAL, we can eliminate almost all cross-AZ traffic, significantly reducing the total infrastructure costs of running high-throughput, latency-relaxed workloads.",[48,1369,1370],{},"Due to its unbeatable durability and cost, Ursa was designed to use cloud object storage as a major storage layer. However, as most workloads need lower latency than what object stores typically provide, we didn't choose one implementation over the other; instead, we incorporated multi-tenancy features that allow users to select the most optimized storage profiles based on their needs for throughput, latency, and cost.",[48,1372,1373],{},"Therefore, you can optimize your tenants and topics based on latency versus cost. Workloads optimized for latency can continue using a latency-optimized WAL without converting data to lakehouse formatted tables. High-throughput and latency-relaxed workloads can choose a cost-optimized WAL to avoid costly cross-AZ data transfers. Data can be stored in lakehouse table formatted streams for longer-term storage and analytical purposes.",[48,1375,1376],{},"With this multi-model and modular Ursa storage engine, we are developing a unified data streaming platform that supports all types of workloads not only with the Kafka API for data streaming and the Pulsar API for messaging queues but also with lakehouse formats as the emerging standard for feeding your analytics systems.",[40,1378,1380],{"id":1379},"our-ambition-unify-data-streaming-and-data-lakes","Our Ambition: Unify Data Streaming and Data Lakes",[48,1382,1383],{},"The Ursa Engine, available on StreamNative Cloud, represents the culmination of years of development and operational experience with Pulsar and Kafka. It's designed to meet the evolving needs of StreamNative customers with a new data streaming engine to support a modern, cost-conscious data streaming cloud. Key developments include full support for the Kafka protocol, a transition from tiered storage to lakehouse storage, the introduction of the more robust metadata management with Oxia, and a shift to make BookKeeper optional by utilizing a WAL system based on commodity object storage.",[48,1385,1386],{},"The rollout of Ursa is structured into distinct phases:",[339,1388,1389,1398,1406,1409],{},[342,1390,1391,1392,1397],{},"Phase 1: Kafka API Compatibility. Achieved ",[55,1393,1396],{"href":1394,"rel":1395},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fkafka-on-cloud",[264],"general availability on StreamNative Cloud"," in January 2024.",[342,1399,1400,1401,1405],{},"Phase 2: Lakehouse Storage. Set for a ",[55,1402,1404],{"href":1403},"\u002Fblog\u002Funlocking-lakehouse-storage-potential-seamless-data-ingestion-from-streamnative-to-databricks","private preview"," on StreamNative Cloud in May 2024.",[342,1407,1408],{},"Phase 3: No Keepers. Plans to remove ZooKeeper with Oxia entering public preview by Q2 2024 and to remove BookKeeper later in the year.",[342,1410,1411],{},"Phase 4: Stream \u003C-> Table Duality. Ursa currently enables the writing of data streams as data tables for storage, with future ambitions to allow users to consume Lakehouse Tables as Streams.",[48,1413,1122],{},[48,1415,1416],{},[351,1417],{"alt":18,"src":1418},"\u002Fimgs\u002Fblogs\u002F66f16f5ec93b13a30217f7f1_66424e2c55fc05c861673bed_eXG5P9aYmYKZSUnW79a-vXvDlE2XccwC6DP0Z2fwK5Szmw4QEVZFp51yQDPQS3gDJytuxh-aJCbPHooGveTNFGE3psnULVxyAXNLOMcO6Jj5tDbCeqzXr_Vb8gVRYwk2YV-2cgi0MN-GsxIYo99yROk.png",[48,1420,1421],{},"The rollout of Ursa engine dramatically reduces the time to insights on your data by uniting data streaming and data lakehouse technologies. We are thrilled about the advancements our team has made and the potential that the launch of the Ursa engine has for both your data streaming platform (DSP) and your lakehouse:",[339,1423,1424,1427],{},[342,1425,1426],{},"For the Lakehouse: Data remains perpetually fresh. It is received, processed, and made available in real time, ensuring it's ready for immediate analysis.",[342,1428,1429],{},"For the Data Streaming Platform: Stream processing jobs benefit from access to the entire historical dataset, simplifying tasks such as reprocessing old data or performing complex joins.",[48,1431,1432],{},"Additionally, we are streamlining the data ingestion pipeline to make it more robust and efficient, ensuring that defined data streams seamlessly integrate into your lake without the need for manual intervention.",[40,1434,1436],{"id":1435},"ursa-next-steps","Ursa Next Steps",[48,1438,1439],{},"Ursa represents a significant advancement in data streaming. We're simplifying the deployment and operation of data streaming platforms, accelerating data availability for your applications and lakehouses, and reducing the costs of managing a modern data streaming stack.",[48,1441,1442,1443,1446,1447,1451,1452,190],{},"While Ursa is still in its early stages, our ambitions are high, and we are eager for you to experience its capabilities. The Ursa engine, featuring Kafka API compatibility, Lakehouse storage, and the No Keeper architecture, is available on ",[55,1444,1073],{"href":958,"rel":1445},[264],". If you want to learn more or try it out, ",[55,1448,1450],{"href":958,"rel":1449},[264],"sign up"," today or ",[55,1453,1455],{"href":1454},"\u002Fcontact","talk to our data streaming experts",[48,1457,1122],{},{"title":18,"searchDepth":19,"depth":19,"links":1459},[1460,1461,1462,1463,1464,1465,1466,1467,1471,1472],{"id":1101,"depth":19,"text":1102},{"id":1130,"depth":19,"text":1131},{"id":1167,"depth":19,"text":1168},{"id":1197,"depth":19,"text":1198},{"id":1228,"depth":19,"text":1229},{"id":1256,"depth":19,"text":1257},{"id":1280,"depth":19,"text":1281},{"id":1318,"depth":19,"text":1253,"children":1468},[1469,1470],{"id":1324,"depth":279,"text":1325},{"id":1350,"depth":279,"text":1351},{"id":1379,"depth":19,"text":1380},{"id":1435,"depth":19,"text":1436},"2024-05-14","\u002Fimgs\u002Fblogs\u002F66427ce8a899003a44963824_SN-SM-UrsaAnnounce.png",{},{"title":1088,"description":1088},"blog\u002Fursa-reimagine-apache-kafka-for-the-cost-conscious-data-streaming",[379,1479,1084],"Apache Pulsar","VIB8Ag0NNSAlXlX_AYlI-JCnJjAz6aebysSE8HOVTSs",[1482],{"id":1483,"title":1090,"bioSummary":1484,"email":289,"extension":8,"image":1485,"linkedinUrl":1486,"meta":1487,"position":1494,"stem":1495,"twitterUrl":1496,"__hash__":1497},"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":1488},{"type":15,"value":1489,"toc":1492},[1490],[48,1491,1484],{},{"title":18,"searchDepth":19,"depth":19,"links":1493},[],"CEO and Co-Founder, StreamNative, Apache Pulsar PMC Member","authors\u002Fsijie-guo","https:\u002F\u002Ftwitter.com\u002Fsijieg","krzMgsbADqGZT1TnpWTVzT4HJ9U7oZB9hzOMiDT5Wd0",[1499,1507,1513],{"path":1500,"title":1501,"date":1502,"image":1503,"link":-1,"collection":1504,"resourceType":1505,"score":1506,"id":1500},"\u002Fblog\u002Fdiskless-stateless-leaderless---a-comic-guide-to-modern-data-streaming","Diskless, Stateless, Leaderless – A Comic Guide to Modern Data Streaming","2025-04-30","\u002Fimgs\u002Fblogs\u002F6814230cb271e5a961040ae1_comic-cost-less.jpg","blogs","Blog",1,{"path":1508,"title":1509,"date":1510,"image":1511,"link":-1,"collection":1504,"resourceType":1505,"score":1512,"id":1508},"\u002Fblog\u002Fannouncing-one-cli","Introducing snctl 1.0: Your One-Stop CLI for All StreamNative Interactions","2025-05-13","\u002Fimgs\u002Fblogs\u002F682310d54ff0303db56a9104_snctl-v1.0.png",0.75,{"path":1514,"title":1515,"date":1516,"image":1517,"link":-1,"collection":1504,"resourceType":1505,"score":1512,"id":1514},"\u002Fblog\u002Fdata-streaming-for-generative-ai","Streaming Data into the Future of Generative AI","2024-05-31","\u002Fimgs\u002Fblogs\u002F666a1201cfa40fabc459c526_Blog-3.png",1776707234899]