[{"data":1,"prerenderedAt":1926},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":24,"navbar-pricing-featured":306,"blog-\u002Fblog\u002Fstreamnatives-2025-year-in-review":1086,"blog-authors-\u002Fblog\u002Fstreamnatives-2025-year-in-review":1888,"related-\u002Fblog\u002Fstreamnatives-2025-year-in-review":1905},{"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":290,"createdAt":289,"date":1876,"description":1877,"extension":8,"featured":7,"image":1878,"isDraft":294,"link":289,"meta":1879,"navigation":7,"order":296,"path":1880,"readingTime":1881,"relatedResources":289,"seo":1882,"stem":1883,"tags":1884,"__hash__":1887},"blogs\u002Fblog\u002Fstreamnatives-2025-year-in-review.md","StreamNative’s 2025 Year in Review",[1090],"Sijie Guo",{"type":15,"value":1092,"toc":1866},[1093,1100,1106,1130,1149,1177,1209,1227,1233,1263,1293,1317,1323,1353,1388,1419,1425,1458,1485,1512,1518,1554,1576,1610,1616,1623,1686,1701,1717,1737,1743,1773,1811,1817,1851],[48,1094,1095,1096,1099],{},"Welcome to the new year! As we kick off 2026, we’re thrilled to take a moment to reflect on 2025—a year of remarkable growth, innovation, and community momentum at StreamNative. From major product milestones like ",[44,1097,1098],{},"Ursa Engine"," reaching general availability to breakthroughs in real-time AI integration, 2025 was a pivotal year that solidified StreamNative’s role at the forefront of lakehouse-native data streaming. In this review, we highlight the key product releases, community achievements, business developments, and events that defined our year – and share a glimpse of what’s ahead in 2026.",[40,1101,1103],{"id":1102},"ursa-engine-goes-ga-and-everywhere-lakehouse-native-streaming-at-scale",[44,1104,1105],{},"Ursa Engine Goes GA and Everywhere – Lakehouse-Native Streaming at Scale",[48,1107,1108,1109,1111,1112,1115,1116,1122,1123,1129],{},"2025 was a breakthrough year for ",[44,1110,1098],{},", StreamNative’s next-generation, lakehouse-native streaming engine for Apache Pulsar and Kafka. ",[44,1113,1114],{},"Ursa Engine reached General Availability (GA) on AWS"," in Q1, delivering on its promise to ",[55,1117,1119,972],{"href":1118},"\u002Fblog\u002Fannouncing-ursa-engine-ga-on-aws-leaderless-lakehouse-native-data-streaming-that-slashes-kafka-costs-by-95#:~:text=We%E2%80%99re%20excited%20to%20announce%20a,compared%20to%20traditional%20Kafka%20deployments",[36,1120,1121],{},"slash streaming costs by up to 95%","compared to traditional Kafka. Built on a leaderless, stateless architecture that writes data directly to cloud object storage in open table formats, Ursa dramatically reduces infrastructure overhead while remaining fully Kafka-compatible. Its innovative design was validated on the world stage when ",[55,1124,1126],{"href":1125},"\u002Fblog\u002Fursa-wins-vldb-2025-best-industry-paper-the-first-lakehouse-native-streaming-engine-for-kafka",[44,1127,1128],{},"our Ursa paper won the Best Industry Paper award at VLDB 2025",", underscoring Ursa as the first “lakehouse-native” streaming engine for Kafka.",[48,1131,1132,1133,1136,1137,1140,1141,1144,1145,1148],{},"We also put a name to the architectural shift we’ve been building toward: ",[36,1134,1135],{},"lakehouse-native data streaming",". By “lakehouse-native,” we mean a streaming system where ",[44,1138,1139],{},"open lakehouse tables (Iceberg\u002FDelta) on object storage are the primary storage layer",", not an after-the-fact destination fed by connector pipelines. Instead of “stream first, copy later,” Ursa makes it possible to ",[44,1142,1143],{},"write once into open table formats"," and make the same data immediately usable for streaming consumers ",[36,1146,1147],{},"and"," analytics\u002FAI engines through catalog integrations — reducing duplication, simplifying governance, and collapsing two infrastructures into one.",[48,1150,1151,1154,1155,1159,1160,1164,1165,1169,1170,1173,1174,1176],{},[44,1152,1153],{},"Ursa expanded to every cloud and cluster."," Following AWS GA, we introduced Ursa on ",[55,1156,1158],{"href":1157},"\u002Fblog\u002Fstreamnative-ursa-is-now-available-for-public-preview-on-microsoft-azure","Microsoft Azure"," and ",[55,1161,1163],{"href":1162},"\u002Fblog\u002Fannouncing-ursa-engine-preview-on-gcp","Google Cloud"," in Public Preview. By late 2025, organizations could deploy Ursa in their own accounts on all three major clouds, or consume it as a fully-managed service. Crucially, Ursa’s lakehouse storage tier ",[55,1166,1168],{"href":1167},"\u002Fblog\u002Fursa-everywhere-lakehouse-native-future-data-streaming#:~:text=our%20next,the%20Classic%20Engine%20to%20Ursa","became available for every StreamNative Cloud cluster (Serverless, Dedicated, BYOC)"," via a new tiered storage extension. This means even ",[36,1171,1172],{},"classic"," Pulsar clusters can now offload data to Iceberg\u002FDelta lakehouse tables, immediately making each topic a live stream ",[36,1175,1147],{}," an analytics-ready table. Users get the familiar Pulsar\u002FKafka experience while data automatically lands in their cloud storage (e.g. S3 or ADLS) as compacted Parquet files. This “Ursa Everywhere” approach allows seamless upgrades to the full Ursa engine in future, with data already in the right format and place – a pragmatic path to reduce total cost of ownership without disruptive migrations.",[48,1178,1179,1182,1183,1189,1190,1197,1198,1204,1205,1208],{},[44,1180,1181],{},"Deep integration with data lakehouse catalogs"," was another highlight. Ursa now natively integrates with popular governance and catalog systems to unify streaming and batch data under consistent governance. For example, ",[55,1184,1186],{"href":1185},"\u002Fblog\u002Fseamless-streaming-to-lakehouse-unveiling-streamnative-clouds-integration-with-databricks-unity-catalog",[44,1187,1188],{},"Databricks Unity Catalog integration"," allows streaming topics to register as Unity Catalog–governed Delta or Iceberg tables, so real-time data inherits the same access controls and lineage as the rest of the lakehouse. ",[55,1191,1194],{"href":1192,"rel":1193},"https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Fstorage\u002Fseamless-streaming-to-amazon-s3-tables-with-streamnative-ursa-engine\u002F",[264],[44,1195,1196],{},"Amazon S3 Tables integration"," enables Ursa to write streams directly into Iceberg tables backed by AWS S3, using Iceberg’s REST catalog for centralized metadata. And ",[55,1199,1201],{"href":1200},"\u002Fblog\u002Fstreamnative-enables-seamless-streaming-into-apache-iceberg-tm-snowflake-open-catalog",[44,1202,1203],{},"Snowflake Open Catalog integration"," makes Ursa’s Iceberg tables discoverable and queryable from Snowflake, bridging real-time data into Snowflake’s analytical ecosystem. Together, these ",[44,1206,1207],{},"“streaming augmented lakehouse”"," capabilities brought truly unified governance: streaming topics and batch tables can be one and the same, controlled by the same catalog policies.",[48,1210,1211,1212,1215,1216,1222,1223,1226],{},"Finally, StreamNative’s ",[44,1213,1214],{},"Serverless"," offering reached ",[55,1217,1219],{"href":1218},"\u002Fblog\u002Fstreamnative-serverless-is-now-generally-available-on-aws-google-cloud-and-azure",[44,1220,1221],{},"General Availability on AWS, Google Cloud, and Azure"," in 2025. This Serverless mode delivers instant, elastic streams without cluster management, enabling teams to spin up Pulsar\u002FUrsa clusters on-demand across all major clouds. With seamless auto-scaling and multi-tenancy, the GA release of StreamNative Serverless opened real-time streaming to a wider audience by removing operational overhead. Developers can now build real-time applications faster with ",[36,1224,1225],{},"instant start, automatic scaling",", and support for both Pulsar and Kafka APIs on a unified serverless platform.",[40,1228,1230],{"id":1229},"adaptive-universal-linking-seamless-kafka-migrations",[44,1231,1232],{},"Adaptive Universal Linking – Seamless Kafka Migrations",[48,1234,1235,1236,1243,1244,1247,1248,1251,1252,1255,1256,1259,1260],{},"To ease the journey to Ursa and modern streaming, we ",[55,1237,1239,1240],{"href":1238},"\u002Fblog\u002Feffortless-kafka-migration-real-time-data-replication-with-streamnative-universal-linking","introduced ",[44,1241,1242],{},"Universal Linking (“UniLink”)"," – a powerful tool for ",[36,1245,1246],{},"seamless cross-cluster data migration",". In March, ",[44,1249,1250],{},"UniLink entered Public Preview"," as a ",[44,1253,1254],{},"“full-fidelity Kafka-to-Ursa replication tool”",". This allowed organizations to ",[44,1257,1258],{},"live-migrate"," from legacy Kafka (or classic Pulsar clusters) to Ursa Engine with zero downtime. UniLink continuously replicates topics, schemas, and consumer state from the source to Ursa, so teams can cut over applications at their own pace without data loss or dual-writes. By leveraging smart, zone-aware reads and writing directly to Ursa’s object storage, UniLink avoids broker bottlenecks and costly cross-AZ traffic during migration. This made migrating to Ursa’s leaderless architecture faster and cheaper, ",[36,1261,1262],{},"“replicating more while spending less, without compromise.”",[48,1264,1265,1266,1272,1273,1276,1277,1280,1281,1284,1285,1288,1289,1292],{},"Mid-year, ",[55,1267,1269],{"href":1268},"\u002Fblog\u002Foctober-2025-data-streaming-launch-adaptive-linking-cloud-spanner-connector-and-orca-with-langgraph",[44,1270,1271],{},"UniLink evolved with Adaptive Linking"," to support more flexible migration strategies. ",[44,1274,1275],{},"Two linking modes – stateful vs. stateless –"," were introduced to let teams choose how to handle consumer offsets during migration. In ",[44,1278,1279],{},"stateful mode",", UniLink preserves the exact offsets and ordering between source and destination clusters, so consumers see a continuous stream as if nothing changed. This allows a clean cutover (with full auditability) but requires coordinating a final producer switch in a maintenance window. In ",[44,1282,1283],{},"stateless mode",", UniLink does ",[36,1286,1287],{},"not"," preserve offsets on the target, which greatly relaxes rollout: consumers can start reading from the new cluster independently of when producers move. This mode shines for migrations that may stretch over weeks or involve many independent teams, as it tolerates offset discontinuities that downstream systems can handle. Together, these modes turn “all-or-nothing” migrations into an ",[36,1290,1291],{},"engineering choice"," – tightly coordinated when needed, or gradual and decoupled when possible.",[48,1294,1295,1296,1299,1300,1304,1305,1308,1309,1312,1313,1316],{},"UniLink also added support for ",[44,1297,1298],{},"topic rename mapping",", making re-platforming even smoother. This lets users migrate a topic from one name\u002Fnamespace to a different name on the new cluster – for example, mirror ",[1301,1302,1303],"code",{},"payments.orders"," into ",[1301,1306,1307],{},"finance_orders"," – without breaking schema compatibility or consumer group behavior. Organizations used this to reorganize and clean up topic taxonomy during migration (e.g. consolidating topics or aligning naming conventions) while UniLink kept the data and schema continuity intact. By the end of 2025, ",[44,1310,1311],{},"Adaptive UniLink Linking"," enabled truly seamless ",[44,1314,1315],{},"cross-cluster migrations",", whether upgrading from open-source Kafka, moving from self-managed Kafka to StreamNative Cloud, or consolidating multiple clusters. Companies could “link” their data streams over with confidence, knowing they can preserve critical ordering when required or opt for flexibility when speed is paramount.",[40,1318,1320],{"id":1319},"expanding-connectivity-snowflake-snowpipe-and-google-spanner-integration",[44,1321,1322],{},"Expanding Connectivity: Snowflake Snowpipe and Google Spanner Integration",[48,1324,1325,1326,1329,1330,1336,1337,1340,1341,1344,1345,1348,1349,1352],{},"We also significantly expanded its ",[44,1327,1328],{},"integrations and connectors"," in 2025, making it easier to connect diverse systems into the streaming platform. One major enhancement was ",[55,1331,1333],{"href":1332},"\u002Fblog\u002Fjanuary-data-streaming-launch-organization-profile-ursa-engine-on-azure-enhancements-for-streamnative-cloud-and-more#:~:text=Snowpipe%20Streaming%20Support%20in%20Snowflake,Sink%20Connector",[44,1334,1335],{},"Snowflake Snowpipe Streaming support"," in our Snowflake Sink Connector. The Snowflake Streaming Sink (introduced in private preview in late 2024) was upgraded with Snowpipe Streaming, enabling ",[44,1338,1339],{},"near-real-time loading of data into Snowflake"," tables. Instead of staging files on cloud storage and waiting for batch loads, the connector now uses Snowflake’s Snowpipe Streaming API to push messages directly into Snowflake as soon as they arrive. This delivers ",[44,1342,1343],{},"lower latency"," – data is queryable in Snowflake within seconds, not minutes. It also ",[44,1346,1347],{},"reduces cost and complexity"," by eliminating intermediate storage and batch jobs. In short, streaming pipelines from Pulsar\u002FUrsa into Snowflake became ",[44,1350,1351],{},"faster, cheaper, and simpler",", unlocking use cases like real-time analytics dashboards on Snowflake and up-to-date ML feature tables without complex ETL.",[48,1354,1355,1356,1359,1360,1363,1364,1367,1368,1371,1372,1375,1376,1379,1380,1383,1384,1387],{},"On the source side, StreamNative ",[44,1357,1358],{},"onboarded a suite of Debezium-powered CDC connectors"," in 2025, bringing a rich array of enterprise database integrations into the fold. We added fully-managed source connectors (built on Debezium Kafka Connect) for popular databases: ",[44,1361,1362],{},"MySQL, PostgreSQL, Microsoft SQL Server, MongoDB,"," and a ",[44,1365,1366],{},"universal JDBC"," connector for other relational DBs. These connectors capture ",[44,1369,1370],{},"change data capture (CDC)"," events from databases and stream them into Pulsar topics in real time – all as a native part of StreamNative Cloud (no self-managed Connect cluster needed). For example, the ",[44,1373,1374],{},"Debezium MySQL Source"," connector is available ",[36,1377,1378],{},"built-in"," on StreamNative Cloud; with a few clicks or CLI commands, users can start streaming MySQL binlog events into Pulsar. Similar connectors for Postgres, SQL Server, and MongoDB allow streaming inserts\u002Fupdates\u002Fdeletes with low latency. This year’s additions meant customers could use StreamNative Cloud as a ",[44,1381,1382],{},"universal data pipeline",", seamlessly integrating operational databases into their event streams. With these ",[44,1385,1386],{},"new CDC sources",", microservices can react to DB changes (e.g. an order status update) in real time, and data lakes can ingest fresh transactional data continuously rather than via nightly dumps.",[48,1389,1390,1391,1399,1400,1403,1404,1407,1408,1411,1412,1418],{},"Another noteworthy integration was the ",[55,1392,1393,972,1396],{"href":1268},[44,1394,1395],{},"Debezium Cloud Spanner Source",[44,1397,1398],{},"connector"," introduced in Q4. Google Cloud Spanner – a globally-distributed SQL database – can emit change streams, and StreamNative’s managed connector now taps into those to produce Pulsar events. This connector listens to Spanner’s change streams and publishes every row-level insert\u002Fupdate\u002Fdelete event into a Pulsar topic in near real-time. It is fully managed and ",[44,1401,1402],{},"handles all the heavy lifting"," (scaling, partitioning, offset management), so users simply provide their Spanner instance details and let the platform stream the changes. ",[44,1405,1406],{},"Google Spanner integration"," unlocks powerful patterns: for example, applications can subscribe to Spanner change topics to trigger downstream processes the moment critical data changes (fraud detection, cache updates), and analytics pipelines can keep BigQuery or lakehouse tables in sync without batch jobs. All Debezium-based connectors include rich observability (throughput, lag, error rates in our console) and are designed for reliability at scale. With ",[44,1409,1410],{},"Snowpipe Streaming + a growing connector roster",", 2025 solidified StreamNative’s vision of ",[55,1413,1415],{"href":1414},"\u002Fproducts\u002Funiconn",[44,1416,1417],{},"Universal Connectivity",": whatever data source or sink you use – cloud data warehouse, relational database, NoSQL store – we likely have a native integration to plug it into your streaming pipeline.",[40,1420,1422],{"id":1421},"orca-eventdriven-ai-agents-come-to-life",[44,1423,1424],{},"Orca: Event‑Driven AI Agents Come to Life",[48,1426,1427,1428,1434,1435,1438,1439,1445,1446,1449,1450,1453,1454,1457],{},"Perhaps the most futuristic development of 2025 was the advent of ",[55,1429,1431],{"href":1430},"\u002Fproducts\u002Forca-agent-engine",[44,1432,1433],{},"Orca",", Our new ",[44,1436,1437],{},"Event-Driven Agent Engine"," for AI. Unveiled at the Data Streaming Summit in San Francisco, ",[55,1440,1442],{"href":1441},"\u002Fblog\u002Fintroducing-orca-agent-engine-private-preview",[44,1443,1444],{},"Orca entered Private Preview"," as the industry’s first event-driven runtime for production AI agents. The idea behind Orca is simple but powerful: if your enterprise data already ",[36,1447,1448],{},"streams through Pulsar",", why not host your AI “agents” directly in the stream? Traditional LLM-powered agents often run as stateless APIs or notebook experiments, but ",[44,1451,1452],{},"Orca transforms AI agents from passive, request\u002Fresponse bots into persistent, real-time actors",". An Orca agent can subscribe to one or more topics, ",[36,1455,1456],{},"maintain state"," (memory) between events, take actions (call APIs or trigger workflows), and emit new events – all with the resilience and scalability of Pulsar behind it.",[48,1459,1460,1461,1464,1465,1468,1469,1472,1473,1476,1477,1480,1481,1484],{},"In practice, ",[44,1462,1463],{},"Orca provides a production-grade sandbox for autonomous AI",". Agents run inside a ",[36,1466,1467],{},"durable event loop",": they consume messages from streams (e.g. a customer event topic), use an LLM or other AI logic to decide on an output, and produce results or commands to other topics. Unlike ephemeral Lambda functions, Orca agents can keep long-lived state (via in-memory or streaming storage), allowing them to “remember” past interactions or maintain a chain of thought over time. The Orca engine handles ",[44,1470,1471],{},"concurrency, fault tolerance, and observability"," – multiple agents can coordinate, no single agent stalls the system, and every decision or action is logged and traceable. In essence, Orca enables an ",[44,1474,1475],{},"“agent mesh”"," architecture where multiple AI agents collaborate via the Pulsar event bus, sharing context and tasks in real time. Notably, Orca is ",[36,1478,1479],{},"polyglot",": it leverages Pulsar’s multi-protocol support, meaning it can work with ",[44,1482,1483],{},"OpenAI functions\u002Fagents, Google’s Agent Framework (ADK), LangChain\u002FLangGraph",", or custom Python agents without heavy rewrites.",[48,1486,1487,1488,1491,1492,1495,1496,1499,1500,1503,1504,1507,1508,1511],{},"The ",[44,1489,1490],{},"use cases for Orca are ground-breaking",". Imagine a cybersecurity agent that subscribes to network intrusion events and ",[36,1493,1494],{},"autonomously orchestrates"," containment actions, or a customer support AI that listens to user activity streams and ",[36,1497,1498],{},"proactively engages"," with personalized responses. With Orca, such agents run ",[36,1501,1502],{},"natively in the streaming platform",", eliminating latency and integration barriers. They don’t poll for data – they react ",[44,1505,1506],{},"the instant events occur",". StreamNative built Orca with enterprise needs in mind: integration with corporate single sign-on and secrets management, role-based controls on what tools an agent can use, and full audit logs of agent decisions. By year’s end, Orca remained in Private Preview (initially available for BYOC deployments), but it had already sparked imagination among early users. Orca’s debut signals that ",[44,1509,1510],{},"autonomous, event-driven AI is no longer science fiction","; it’s the next chapter of streaming, where data streams feed AI agents that continuously perceive and act.",[40,1513,1515],{"id":1514},"security-and-governance-rbac-ga-and-schema-governance-previews",[44,1516,1517],{},"Security and Governance: RBAC GA and Schema Governance Previews",[48,1519,1520,1521,1533,1534,1537,1538,1541,1542,1545,1546,1549,1550,1553],{},"StreamNative Cloud matured its enterprise security and governance features in 2025, making it easier for organizations to confidently run multi-tenant, production workloads. ",[55,1522,1524,1525,1528,1529,1532],{"href":1523},"\u002Fblog\u002Fq3-2025-data-streaming-launch-lakehouse-streaming-governed-analytics-and-event-driven-agents#:~:text=RBAC%20GA%3A%20least,tenant%20streaming","A major milestone was ",[44,1526,1527],{},"Role-Based Access Control (RBAC)"," reaching ",[44,1530,1531],{},"General Availability"," in Q3",". ",[44,1535,1536],{},"RBAC in StreamNative Cloud is now GA",", bringing a consistent, fine-grained security model across all Pulsar and Kafka interfaces. This means platform admins can centrally define who is allowed to do what – e.g. ",[44,1539,1540],{},"who can create or delete topics, publish or subscribe on a given namespace, or evolve a schema"," – all through a unified roles and permissions system. Roles can mirror real-world teams and least-privilege principles (for example, a ",[36,1543,1544],{},"Data Producer"," role that grants publish rights on specific topics but no consume rights). These permissions apply uniformly whether clients connect via Pulsar protocols or the Kafka API, ensuring no backdoor by using a different interface. With RBAC GA, enterprises no longer need ad-hoc ACL scripts or manual enforcement – they get a ",[44,1547,1548],{},"single source of truth for access control",", manageable in the Cloud Console or via API\u002FTerraform for automation. As noted in the announcement, ",[36,1551,1552],{},"“consolidating onto one platform doesn’t mean compromising on governance”"," – RBAC provides the guardrails to confidently host many applications and teams on the same streaming cluster.",[48,1555,1556,1557,1560,1561,1564,1565,1568,1569,1572,1573,1575],{},"StreamNative also introduced new ",[44,1558,1559],{},"schema governance"," capabilities. Since Pulsar’s schema registry is built-in, RBAC now covers ",[44,1562,1563],{},"who can register or update schemas"," for each topic, adding protection against unauthorized or incompatible schema changes. Moreover, in January we launched ",[44,1566,1567],{},"Kafka Schema Registry RBAC"," in Private Preview. This feature extends fine-grained access control to the Kafka-compatibility Schema Registry API, allowing enterprises to enforce who can read or write schema definitions on a per-subject basis. By locking down schema evolution, companies can ensure only approved data models make it to production – a big win for compliance and data quality. These schema governance tools, combined with RBAC, move StreamNative Cloud toward a ",[36,1570,1571],{},"“secure by default”"," posture: no more open access by default; everything is governed by roles that map to business needs. It shifts access management from scattered configs to a single auditable model. And because RBAC applies to Pulsar ",[36,1574,1147],{}," Kafka endpoints, security teams have one framework to understand, rather than separate ACL systems.",[48,1577,1578,1579,1582,1583,1589,1590,1593,1594,1597,1598,1601,1602,1605,1606,1609],{},"Other enhancements focused on ",[44,1580,1581],{},"administrative ease and platform hardening",". We rolled out a ",[55,1584,1586],{"href":1585},"\u002Fblog\u002Fjanuary-data-streaming-launch-organization-profile-ursa-engine-on-azure-enhancements-for-streamnative-cloud-and-more",[44,1587,1588],{},"new Organization Profile page"," in the Console for centralized org management. Administrators can now easily update key info like billing contacts and technical contacts, ensuring they don’t miss critical notifications. The profile page also provides a clear overview of the organization’s clusters and resources in one place, simplifying management for large teams. Under the hood, we delivered a ",[44,1591,1592],{},"“slim” StreamNative Cloud container image"," that uses a Bill of Materials for dependency management. This trimmed the core image size to ~1 GB, improving startup times and reducing the attack surface for security. A smaller image means faster autoscaling and easier upgrades, as well as fewer components to monitor for vulnerabilities. This change, though not visible to end users, exemplifies our commitment to ",[36,1595,1596],{},"enterprise-grade reliability and security",". In sum, by end of 2025 StreamNative Cloud offered a much tighter security and governance story: ",[44,1599,1600],{},"GA-grade RBAC"," for all resources, ",[44,1603,1604],{},"schema controls"," to prevent data chaos, and polished admin experiences – all contributing to a ",[44,1607,1608],{},"trustworthy, governable streaming platform"," for the enterprise.",[40,1611,1613],{"id":1612},"business-growth-and-global-expansion",[44,1614,1615],{},"Business Growth and Global Expansion",[48,1617,1618,1619,1622],{},"StreamNative’s business saw robust growth in 2025, underpinned by new customer wins, cloud footprint expansion, and industry recognition. In 2025, we saw more “AI-native” products depend on ",[44,1620,1621],{},"continuous, high-volume event streams"," — because when your product reacts in real time, your data pipeline can’t be batch.",[48,1624,1625,1631,1632,1635,1636,1639,1640,1642,1648,1649,1652,1653,1159,1656,1659,1660,1663,1664,1667,1668,1670,1671,1677,1678,1681,1682,1685],{},[55,1626,1628],{"href":1627},"\u002Fsuccess-stories\u002Funify-achieves-real-time-go-to-market-scale-with-apache-pulsar-and-streamnative-cloud",[44,1629,1630],{},"Unify",", an AI-native go-to-market platform, built a real-time backbone on ",[44,1633,1634],{},"StreamNative Cloud + Apache Pulsar"," that ingests ",[44,1637,1638],{},"tens of millions of events per day",", replacing batch jobs and legacy queuing so their platform can react to buyer signals in seconds and trigger downstream workflows immediately.",[55,1641,972],{"href":1627},[55,1643,1645],{"href":1644},"\u002Fsuccess-stories\u002Fsafari-ai-cuts-cloud-costs-by-50-while-scaling-real-time-computer-vision-analytics-with-streamnative",[44,1646,1647],{},"Safari AI"," scaled real-time ",[44,1650,1651],{},"computer vision"," analytics on top of customers’ existing camera infrastructure — tracking operational metrics like occupancy and queue wait times — and as they grew to ",[44,1654,1655],{},"10,000+ pipelines",[44,1657,1658],{},"50,000+ cameras",", StreamNative helped them achieve a ",[44,1661,1662],{},"50% infrastructure cost reduction"," while maintaining ",[44,1665,1666],{},"sub‑10‑second"," end-to-end delivery for real-time metrics.",[55,1669,972],{"href":1644},"And in security and fraud prevention, ",[55,1672,1674],{"href":1673},"\u002Fsuccess-stories\u002Fhow-q6-cyber-tamed-85-billion-cyberthreat-records-with-apache-pulsar-streamnative-new",[44,1675,1676],{},"Q6 Cyber"," replaced ",[44,1679,1680],{},"Google Cloud Pub\u002FSub"," with StreamNative’s Pulsar platform to process ",[44,1683,1684],{},"85B+ cyberthreat records",", using StreamNative as the transport layer at the center of their architecture while retaining the control they needed via BYOC.",[48,1687,1688,1689,1692,1693,1696,1697,1700],{},"These fast-growing organizations chose StreamNative for its unique ability to handle ",[36,1690,1691],{},"both"," high-throughput streaming and mission-critical messaging on one platform – a perfect fit for AI use cases that ingest massive data streams and respond in milliseconds. We also continued to serve ",[44,1694,1695],{},"large enterprises"," modernizing their infrastructures: more Fortune 500 companies moved from self-managed Kafka or legacy messaging systems to StreamNative Cloud to cut costs and accelerate development. This broad adoption across startups and enterprises drove our ",[44,1698,1699],{},"cloud usage"," to new heights – In 2025, StreamNative’s Cloud business nearly tripled in revenue, while enterprise cloud revenue grew over 200% year over year—outpacing overall growth as large customers scaled mission-critical workloads.",[48,1702,1703,1704,1712,1713,1716],{},"On the global front, we made StreamNative Cloud more accessible than ever. In August, ",[55,1705,1707,1708,1711],{"href":1706},"\u002Fblog\u002Fstreamnative-cloud-now-available-for-public-preview-on-alibaba-cloud-marketplace#:~:text=We%E2%80%99re%20excited%20to%20announce%20that,on%20the%20Alibaba%20Cloud%20Marketplace","we launched ",[44,1709,1710],{},"StreamNative Cloud on Alibaba Cloud"," Marketplace",", entering the Chinese and Asia-Pacific cloud ecosystem. Now Alibaba Cloud users can subscribe to StreamNative’s fully-managed Pulsar\u002FUrsa service directly through their local cloud account. This public preview on Alibaba Cloud opened the door to organizations in regulated or region-specific markets who prefer Alibaba’s infrastructure. The offering brought the ",[44,1714,1715],{},"StreamNative’s Data Streaming Platform"," (messaging + lakehouse streaming) to Alibaba’s customer base, with seamless integration to Alibaba services like OSS (object storage) for lakehouse tiered storage. In addition, we extended our marketplace availability – by end of year, StreamNative Cloud listings existed on all three major cloud marketplaces, simplifying procurement for cloud-first enterprises.",[48,1718,1719,1720,1723,1724,1728,1729,1732,1733,1736],{},"Industry analysts took note of StreamNative’s rise. ",[44,1721,1722],{},"Forrester Research"," included StreamNative in ",[55,1725,1726],{"href":57},[36,1727,62],{},", marking our first appearance in this influential evaluation of streaming vendors. We were ",[44,1730,1731],{},"recognized as a “Contender”"," in the Wave – an impressive showing for our debut year – with Forrester highlighting that “",[36,1734,1735],{},"StreamNative excels at messaging and resource optimization","” and supports real-time analytics and event-driven use cases with strong scalability. The report noted our cost-efficient, Kafka-compatible architecture as a key strength appreciated by customers. This independent validation echoed an earlier recognition from GigaOm, which named StreamNative a Leader in its 2024 Radar for Streaming Data Platforms. Such accolades boosted our credibility in the market and have driven an uptick in inbound interest from enterprises looking to modernize their data infrastructure.",[40,1738,1740],{"id":1739},"community-events-and-thought-leadership",[44,1741,1742],{},"Community Events and Thought Leadership",[48,1744,1745,1746,1753,1754,1760,1761,1764,1765,1768,1769,1772],{},"Throughout 2025, StreamNative invested heavily in community education and thought leadership, convening the ",[55,1747,1750],{"href":1748,"rel":1749},"https:\u002F\u002Fdatastreaming-summit.org\u002F",[264],[44,1751,1752],{},"Data Streaming Summit"," series as a forum for practitioners. In the spring, we hosted ",[55,1755,1757],{"href":1756},"\u002Fblog\u002Fdata-streaming-summit-virtual-2025-recap",[44,1758,1759],{},"Data Streaming Summit Virtual 2025"," (May 29), a free two-day online conference that attracted thousands of attendees from around the globe. The virtual summit featured ",[44,1762,1763],{},"36+ sessions"," over multiple tracks, showcasing the latest trends and best practices in real-time data. A central theme was the emergence of ",[44,1766,1767],{},"“Agentic AI”"," – the idea of AI agents driven by streaming data – which was fitting given our Orca announcement. Talks from industry leaders explored how real-time streaming, unified lakehouse architectures, and open source technologies are converging to enable this next wave of intelligent systems. Other sessions dove into Pulsar 4.1’s improvements, user case studies of Pulsar replacing Kafka, and deep-dives into Ursa’s design. By removing geographical barriers, the ",[44,1770,1771],{},"virtual summit democratized knowledge",", allowing anyone to learn from streaming experts. The engagement was tremendous – live Q&As, community Slack discussions, and thousands of views on session recordings.",[48,1774,1775,1776,1782,1783,1786,1787,1790,1791,1794,1795,1798,1799,1802,1803,1806,1807,1810],{},"Building on that momentum, ",[55,1777,1779],{"href":1778},"\u002Fblog\u002Fdata-streaming-summit-2025-on-demand-is-live",[44,1780,1781],{},"Data Streaming Summit San Francisco 2025"," took place in-person on September 29–30 at the Grand Hyatt SFO. This marked the return of an in-person community conference (after prior Pulsar Summits), and it did not disappoint. Over 300 practitioners gathered to network and learn. The summit offered ",[44,1784,1785],{},"30+ sessions across four dedicated tracks",": ",[36,1788,1789],{},"Deep Dive"," (covering architecture and internals), ",[36,1792,1793],{},"Use Cases"," (real-world deployments), ",[36,1796,1797],{},"AI + Stream Processing",", and ",[36,1800,1801],{},"Streaming Lakehouse",". The agenda was packed with exciting content – from how Netflix runs Kafka at massive scale, to insider talks from LinkedIn, Uber, and OpenAI on their streaming infrastructures. Notably, the event was intentionally ",[44,1804,1805],{},"vendor-neutral and multi-technology",". While StreamNative played host, speakers and sponsors came from across the ecosystem: Amazon Web Services, Redpanda, Confluent, RisingWave, and more. This fostered honest discussions on comparing approaches and the future direction of streaming. A highlight was a keynote panel on ",[36,1808,1809],{},"real-time AI in production",", featuring contributors from both Pulsar and Kafka communities discussing how streaming systems must evolve to support AI workloads. The energy at the summit was electric – it underscored that the real-time data community is vibrant and united by common challenges regardless of the tool. By convening these events (virtual and in-person), we continue to support the broad data streaming community and ecosystem, facilitating knowledge-sharing that benefits the entire industry.",[40,1812,1814],{"id":1813},"looking-ahead-to-2026",[44,1815,1816],{},"Looking Ahead to 2026",[48,1818,1819,1820,1823,1824,1827,1828,1831,1832,1835,1836,1839,1840,1843,1844,1847,1848,1850],{},"As we celebrate the successes of 2025, we’re already gearing up for what’s next. ",[44,1821,1822],{},"Data streaming"," will continue to evolve from a siloed pipeline to an integrated ",[36,1825,1826],{},"“data backbone”"," for all enterprise analytics and AI. In 2026, StreamNative will double down on enabling the ",[44,1829,1830],{},"streaming lakehouse"," paradigm – expect even tighter integrations with lakehouse ecosystems, more connectors for real-time analytics, and features that make streaming data ",[36,1833,1834],{},"immediately usable"," for AI\u002FML. Our recently announced ",[44,1837,1838],{},"Agent Engine (Orca)"," will progress toward general availability, bringing ",[44,1841,1842],{},"event-driven agents"," into mainstream use. We plan to expand Orca’s capabilities, adding richer developer tooling, library integrations, and guardrails so that any organization can safely deploy AI agents that live in the stream. On the ",[44,1845,1846],{},"governance"," front, 2026 will see us delivering full ",[44,1849,1559],{}," and auditing features – from Schema Registry ACLs graduating to GA, to advanced schema validation and lineage tracking for streaming data.",[48,1852,1853,1854,1857,1858,1861,1862,1865],{},"In short, ",[44,1855,1856],{},"StreamNative’s vision for 2026"," is an open platform where data streams, batch data, and AI agents all come together in a governed, seamless fashion. We anticipate more enterprises will converge their messaging queues, streaming logs, and data lakes into one cohesive system – and we aim to be the backbone for that transformation. The team is already hard at work on ",[44,1859,1860],{},"Pulsar 5.0"," features, further performance optimizations, and one-click cloud experiences that push the envelope of simplicity and scale. Thank you to our customers, community, and partners for an incredible 2025 – and ",[44,1863,1864],{},"get ready for an even more exciting 2026",", where real-time data powers intelligence like never before!",{"title":18,"searchDepth":19,"depth":19,"links":1867},[1868,1869,1870,1871,1872,1873,1874,1875],{"id":1102,"depth":19,"text":1105},{"id":1229,"depth":19,"text":1232},{"id":1319,"depth":19,"text":1322},{"id":1421,"depth":19,"text":1424},{"id":1514,"depth":19,"text":1517},{"id":1612,"depth":19,"text":1615},{"id":1739,"depth":19,"text":1742},{"id":1813,"depth":19,"text":1816},"2026-01-06","Reflect on StreamNative’s 2025: Ursa Engine GA, lakehouse-native streaming, AI agents, global growth, and what’s ahead for data streaming in 2026.","\u002Fimgs\u002Fblogs\u002F695cfb58adb67ac386d1bab0_2025-in-review.png",{},"\u002Fblog\u002Fstreamnatives-2025-year-in-review","10 min read",{"title":1088,"description":1877},"blog\u002Fstreamnatives-2025-year-in-review",[379,1885,1433,1886,1073,1752],"UniLink","RBAC","Q0kcsQmKa8tPnufdr1NztIR3fZG2-rw490hCSeyYmKo",[1889],{"id":1890,"title":1090,"bioSummary":1891,"email":289,"extension":8,"image":1892,"linkedinUrl":1893,"meta":1894,"position":1901,"stem":1902,"twitterUrl":1903,"__hash__":1904},"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":1895},{"type":15,"value":1896,"toc":1899},[1897],[48,1898,1891],{},{"title":18,"searchDepth":19,"depth":19,"links":1900},[],"CEO and Co-Founder, StreamNative, Apache Pulsar PMC Member","authors\u002Fsijie-guo","https:\u002F\u002Ftwitter.com\u002Fsijieg","krzMgsbADqGZT1TnpWTVzT4HJ9U7oZB9hzOMiDT5Wd0",[1906,1913,1921],{"path":1778,"title":1907,"date":1908,"image":1909,"link":-1,"collection":1910,"resourceType":1911,"score":1912,"id":1778},"Data Streaming Summit 2025 — On-Demand Is Live","2025-11-11","\u002Fimgs\u002Fblogs\u002F6912cc58492764d8ea702956_DSS-Video-on-demand.png","blogs","Blog",0.5,{"path":1914,"title":1915,"date":1916,"image":1917,"link":-1,"collection":1918,"resourceType":1919,"score":1920,"id":1914},"\u002Fwebinars\u002Fdata-streaming-launch-march-2025","Data Streaming Launch - March 2025","2025-03-18","\u002Fimgs\u002Fwebinars\u002F67d77e2fc5c4b283a4de8123_image%20(48).png","webinars","Webinar",0.367,{"path":1268,"title":1922,"date":1923,"image":1924,"link":-1,"collection":1910,"resourceType":1911,"score":1925,"id":1268},"October 2025 Data Streaming Launch: Adaptive Linking, Cloud Spanner Connector, and Orca with LangGraph","2025-10-28","\u002Fimgs\u002Fblogs\u002F6900d991c47c2404dda9b55f_Oct-Data-Streaming-Launch.png",0.286,1776707232747]