[{"data":1,"prerenderedAt":1738},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":24,"navbar-pricing-featured":306,"blog-\u002Fblog\u002Fstreaming-war-and-how-apache-pulsar-is-acing-the-battle":1086,"blog-authors-\u002Fblog\u002Fstreaming-war-and-how-apache-pulsar-is-acing-the-battle":1688,"related-\u002Fblog\u002Fstreaming-war-and-how-apache-pulsar-is-acing-the-battle":1715},{"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":1092,"canonicalUrl":289,"category":1153,"createdAt":289,"date":1676,"description":1677,"extension":8,"featured":294,"image":1678,"isDraft":294,"link":289,"meta":1679,"navigation":7,"order":296,"path":1680,"readingTime":1681,"relatedResources":289,"seo":1682,"stem":1683,"tags":1684,"__hash__":1687},"blogs\u002Fblog\u002Fstreaming-war-and-how-apache-pulsar-is-acing-the-battle.md","Streaming War and How Apache Pulsar is Acing the Battle",[1090,1091],"Shivji Kumar Jha","Sachidananda Maharana",{"type":15,"value":1093,"toc":1653},[1094,1097,1100,1104,1107,1110,1142,1146,1170,1173,1176,1179,1182,1190,1193,1196,1199,1203,1212,1235,1244,1247,1250,1253,1256,1259,1262,1265,1288,1291,1294,1303,1306,1309,1317,1320,1324,1327,1344,1348,1351,1364,1374,1377,1380,1386,1389,1399,1403,1410,1416,1419,1425,1438,1442,1445,1466,1470,1473,1477,1485,1497,1508,1512,1521,1525,1534,1538,1557,1569,1571,1574,1578,1593],[48,1095,1096],{},"Over the past few years, we have used different streaming solutions for a variety of use cases. They have helped us meet different requirements for scalability, high availability, disaster recovery, load balancing, low costs, multi-tenancy, and many more. With so many tools in the market, streaming becomes more like a battlefield where each character spares no effort to survive and thrive.",[48,1098,1099],{},"In this blog, we will first talk about the streaming war facing different streaming systems and what tools they should have in their arsenal. Next, we will compare each of them and explain why we think Apache Pulsar will win the battle. Lastly, we will demonstrate how to migrate to Pulsar from other platforms.",[40,1101,1103],{"id":1102},"game-and-arsenal","Game and arsenal",[48,1105,1106],{},"Simply put, the game has producers that publish messages to the middleware solution, which are then consumed by applications or microservices. They can either keep them to themselves or sink them into a big data store.",[48,1108,1109],{},"If you want to win a battle, you need to have sharpened tools and sufficient ammunition. Similarly, when choosing a streaming framework that backs your production workloads, you want it to have the best features so you can be well-prepared to ace any production use cases. Here are some of the weapons that are required for a modern streaming arsenal.",[339,1111,1112,1115,1118,1121,1124,1127,1130,1139],{},[342,1113,1114],{},"Real-time messaging. Today, speed plays an important role in a variety of use cases. For an e-commerce application, for example, if there's something wrong on the checkout page, you want to detect it and fix it as soon as possible. Otherwise, you may lose business for the delayed time, as your customers have poor user experiences like payment failure.",[342,1116,1117],{},"Scalability. Mobile phones, IoT devices, and microservices are producing large amounts of data every day. Therefore, your application needs to be able to work with a framework that can scale flexibly to handle the traffic if required.",[342,1119,1120],{},"High availability. If your website or application is not highly available, you will effectively lose revenue during the downtime (for example, due to single points of failure).",[342,1122,1123],{},"Disaster recovery. If your data is synchronized and replicated across regions, you need to have the redundancy to recover from any disaster situation.",[342,1125,1126],{},"Load balancing. Load balancing allows you to distribute data across your storage and computing clusters judiciously. It prevents nodes from being more loaded or less loaded. It is also one of Pulsar’s distinguishing features.",[342,1128,1129],{},"Low cost of operations. Cost is undoubtedly very important for the infrastructure you are using. Currently, we are running Pulsar at a low operation cost. We will compare the costs of using Kafka, Pulsar, and Kinesis later.",[342,1131,1132,1133,1138],{},"Multi-tenancy. A multi-tenant system allows you to ",[55,1134,1137],{"href":1135,"rel":1136},"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=xjKNcKLuDZI&list=PLA7KYGkuAD071myyg4X5ShsDHsOaIpHOq&index=7",[264],"separate out different use cases"," and run them in isolation. If there are heavy loads in one of the use cases, only that one is stressed while other parts of the cluster work properly.",[342,1140,1141],{},"Flexibility. For a given use case, you may need high availability while another may require strong consistency. A flexible system should support configurations for varied use cases that allow you to perform operations at different tenancy levels.",[40,1143,1145],{"id":1144},"game-characters","Game characters",[48,1147,1148,1149,1154,1155,1154,1159,1164,1165,190],{},"Next, we will be looking at the pros and cons of popular streaming frameworks including ",[55,1150,1153],{"href":1151,"rel":1152},"https:\u002F\u002Fpulsar.apache.org\u002F",[264],"Apache Pulsar",", ",[55,1156,1084],{"href":1157,"rel":1158},"https:\u002F\u002Fkafka.apache.org\u002F",[264],[55,1160,1163],{"href":1161,"rel":1162},"https:\u002F\u002Faws.amazon.com\u002Fkinesis\u002Fdata-streams\u002F",[264],"Amazon Kinesis Data Streams",", and ",[55,1166,1169],{"href":1167,"rel":1168},"https:\u002F\u002Fnats.io\u002F",[264],"NATS",[32,1171,1153],{"id":1172},"apache-pulsar",[48,1174,1175],{},"As an open-source project, Pulsar is completely driven by the community. If there are any major improvements, like API changes, the community will send out a voting request so that community members can discuss how Pulsar moves forward.",[48,1177,1178],{},"Pulsar has a multi-layered architecture where storage is separated from computing. This means that you can scale either part independently. For us, as we deploy our Pulsar cluster on AWS, this loosely-coupled structure makes it very easy to select the type of nodes for scaling if required.",[48,1180,1181],{},"Multi-tenancy achieves different levels of resource isolation. It is one of Pulsar’s enterprise features from day one. All other features that came after were well integrated with all of its enterprise features.",[48,1183,1184,1185,1189],{},"On the flip side, since Pulsar is a modular system, you may feel intimidated when you start installing it for the first time. The deployment time is longer than Kafka and Kinesis. That said, if you are already using Kubernetes, you can use ",[55,1186,1188],{"href":1187},"\u002Fblog\u002Fpulsar-operators-tutorial-part-1-create-apache-pulsar-cluster-kubernetes","operators to quickly deploy Pulsar",". Another thing that may cause trouble to Pulsar users is its ecosystem (like connectors), which is relatively small compared to Kafka. However, this is by no means a problem as we do have workarounds, which will be covered later.",[32,1191,1084],{"id":1192},"apache-kafka",[48,1194,1195],{},"Kafka is also an open-source Apache project. It is battle-tested for the longest time in the streaming space with a mature community and an amazing ecosystem of connectors.",[48,1197,1198],{},"Unlike Pulsar, Kafka has a monolithic architecture, which means storage and computing are bundled together. When scaling your Kafka cluster, you may find it complicated and tricky to select the right types of nodes (for example, on AWS). Therefore, it is less flexible compared with Pulsar in terms of scaling.",[32,1200,1202],{"id":1201},"pulsar-vs-kafka","Pulsar vs. Kafka",[48,1204,1205,1206,1211],{},"Before we introduce the next character, let’s look at how Pulsar performs compared with Kafka. We carried out extensive performance tests on both Pulsar and Kafka and ",[55,1207,1210],{"href":1208,"rel":1209},"https:\u002F\u002Fmedium.com\u002F@yuvarajl\u002Fwhy-nutanix-beam-went-ahead-with-apache-pulsar-instead-of-apache-kafka-1415f592dbbb",[264],"ultimately chose Pulsar",". The following is a summary of our understanding based on the tests.",[339,1213,1214,1217,1220,1223,1226,1229,1232],{},[342,1215,1216],{},"2.5x maximum throughput compared to Kafka",[342,1218,1219],{},"100x lower single-digit publish latency than Kafka",[342,1221,1222],{},"1.5x faster historical read rate than Kafka",[342,1224,1225],{},"Preinstalled schema registry in Pulsar",[342,1227,1228],{},"Pulsar is enterprise-ready from day one",[342,1230,1231],{},"Local disk (Kafka) vs. Tiered\u002Fdecoupled storage (Pulsar)",[342,1233,1234],{},"Kafka wins on community support and ecosystem",[48,1236,1237,1238,1243],{},"Our tests show that Pulsar outperforms Kafka in terms of throughput, latency, and historical read rate. Pulsar features a segment-oriented architecture for every topic (partition), so you don’t have the problem of some topics being hot but others being cold. ",[55,1239,1242],{"href":1240,"rel":1241},"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=xIibbB5Y0MM&list=PLA7KYGkuAD071myyg4X5ShsDHsOaIpHOq&index=5",[264],"The data is spread wonderfully across the storage nodes of Pulsar",", which are managed by BookKeeper. This is also the reason why Pulsar has faster historical reads.",[48,1245,1246],{},"Pulsar has almost everything open-sourced. Its schema registry is already integrated with Pulsar's broker. Kafka, on the other hand, does not have it in the open-source version.",[48,1248,1249],{},"As mentioned above, Pulsar is enterprise-ready with an arsenal of effective weapons. Geo-replication has been one of them since day one. As opposed to Pulsar, Kafka implements geo-replication through MirrorMaker while there are still some rough edges. Some features are probably not working that well with it.",[48,1251,1252],{},"There are also many other blogs comparing Pulsar and Kafka. For more information, see the Reference section below.",[32,1254,1163],{"id":1255},"amazon-kinesis-data-streams",[48,1257,1258],{},"Amazon Kinesis Data Streams is a serverless streaming data service provided by AWS. You can use it to collect and process large streams of data in real time. It’s available as a service so you can easily get started with only a few clicks.",[48,1260,1261],{},"The benefit of using Amazon Kinesis Data Streams is that it requires less maintenance work as AWS manages the infrastructure for you. Additionally, it works seamlessly with other AWS services like S3, DynamoDB, and Lambda.",[48,1263,1264],{},"However, it is not an ideal solution for us due to the following reasons:",[339,1266,1267,1270,1273,1276,1279,1282,1285],{},[342,1268,1269],{},"It is more costly compared to Pulsar, which will be explained later.",[342,1271,1272],{},"It is a closed-source system, which means we cannot customize it based on our needs.",[342,1274,1275],{},"As AWS manages everything for you, it is less flexible.",[342,1277,1278],{},"~ The retention period is fixed at 24 hours or up to 365 days. Storing data for a longer period of time than your need means unnecessary overhead.",[342,1280,1281],{},"~ The average record size cannot be more than 1 MB.",[342,1283,1284],{},"~ Data is always replicated to 3 availability zones (AZs). If your use case does not require more than 2 AZs, it comes with more cost.",[342,1286,1287],{},"Vendor lock-in.",[32,1289,1169],{"id":1290},"nats",[48,1292,1293],{},"Another character in the game is NATS, a CNCF incubating open-source project. It is a connective technology that powers modern distributed systems.",[48,1295,1296,1297,1302],{},"The design philosophy behind NATS is simple, agile, performant, secure, and resilient. Users can easily get started with NATS as it supports various flexible deployments. You can literally deploy it anywhere (on-premises, IoT, edge, and hybrid use cases). Additionally, it provides a base set of functionalities and qualities, also known as ",[55,1298,1301],{"href":1299,"rel":1300},"https:\u002F\u002Fdocs.nats.io\u002Fnats-concepts\u002Fcore-nats",[264],"Core NATS",", which supports models like Publish-Subscribe, Request-Reply, and Queue Groups.",[48,1304,1305],{},"NATS has a built-in distributed persistence system called JetStream. It enables new functionalities and higher qualities of service on top of the base Core NATS. However, it's not as mature as Kafka or Pulsar, and it still has much room for improvement, especially in disaster recovery.",[48,1307,1308],{},"For the consumer metadata like offsets, we want to duplicate and store them on different servers for better redundancy. As NATS makes a raft group for every consumer application, millions of consumers mean millions of Raft groups. This will cause considerable pressure on the network.",[48,1310,1311,1312,190],{},"To learn more about NATS, see ",[55,1313,1316],{"href":1314,"rel":1315},"https:\u002F\u002Fcontent.red-badger.com\u002Fwe-love-tech\u002Fnats\u002Fthe-power-of-nats",[264],"The power of NATS: Modernising communications on a global scale",[48,1318,1319],{},"With a basic understanding of these systems, we think Pulsar is a better choice for cloud-native applications. For IoT, edge, or hybrid use cases, we recommend NATS because of its lightweight server. Users can deploy NATS anywhere and use one cluster that spreads across different environments like cloud, IoT devices, edge, and even on-premises.",[40,1321,1323],{"id":1322},"let-the-battle-begin","Let the battle begin",[48,1325,1326],{},"To make a more comprehensive evaluation, we conducted some performance tests in one of our use cases and compared their respective costs. Here are the rules of the game:",[339,1328,1329,1332,1335,1338,1341],{},[342,1330,1331],{},"Ingest 12 TB\u002Fday: about 5 million messages daily with a size of 2.5 MB per message",[342,1333,1334],{},"Retention period: 24 hours",[342,1336,1337],{},"Replication factor: 2 (24 TB\u002Fday)",[342,1339,1340],{},"Data stored in multiple availability zones",[342,1342,1343],{},"Producers\u002Fconsumers preferably in the same availability zone as stream data",[32,1345,1347],{"id":1346},"pulsar-cost","Pulsar cost",[48,1349,1350],{},"Figure 1 depicts the schematic architecture of our Pulsar cluster.",[48,1352,1353,1354,1357,1358,1363],{},"‍\n",[351,1355],{"alt":18,"src":1356},"\u002Fimgs\u002Fblogs\u002F63c5fb42fcd1374b6f39edfb_AznQ_SCue_kY3nfaSt2uqFPEIP4Nj25qutwFyOathaoXlrysn7cR9oQDS8Rm08YfAPbYPkm35zgy6NYbdD3a1w5Mh0zSpg1V29na4DWXkMOQNKXKajpAYzoLUgPy4OXzYpDnKv8zxOveBqPR2Z4zxKgiHeHGLK8prDJNm5IILbkGGHPT8SAFVBqbpl5IXw.png","Figure 1. Pulsar cluster schematic architecture\nWe used the following commands to configure bookies and ",[55,1359,1362],{"href":1360,"rel":1361},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002Fnext\u002Fadministration-isolation-bookie\u002F#configure-bookie-affinity-groups",[264],"affinity groups"," in different AZs, with group-bookie1 (bk1) being the primary group and group-bookie2 (bk2 and bk3) being the secondary group. Because Pulsar brokers are stateless, we put all the brokers in the same AZ (az1).",[1365,1366,1371],"pre",{"className":1367,"code":1369,"language":1370},[1368],"language-text","bin\u002Fpulsar-admin bookies set-bookie-rack --bookie pulsar-bookie-1:3181 --hostname pulsar-bookie-1:3181 --groupgroup-bookie1 --rack rack1\n\nbin\u002Fpulsar-admin bookies set-bookie-rack --bookie pulsar-bookie-2:3181,pulsar-bookie-3:3181 --hostname pulsar-bookie-2:3181,pulsar-bookie-3:3181 --group group-bookie2 --rack rack2\n\nbin\u002Fpulsar-admin namespaces set-bookie-affinity-group public\u002Fdefault --primary-group group-bookie1 --secondary-group group-bookie2\n","text",[1372,1373,1369],"code",{"__ignoreMap":18},[48,1375,1376],{},"These configurations mean that whenever a broker is trying to write data on bookies, it will put one copy on bk1 and the other on either bk2 or bk3. This helps reduce data transfer costs between the AZs.",[48,1378,1379],{},"If brokers need to be deployed in different AZs, you can use the following command to set the primary brokers for the namespace.",[1365,1381,1384],{"className":1382,"code":1383,"language":1370},[1368],"bin\u002Fpulsar-admin ns-isolation-policy set --auto-failover-policy-type min_available --auto-failover-policy-params min_limit=1,usage_threshold=80 --namespaces public\u002Fdefault --primary pulsar-broker-node-1 --secondary pulsar-broker-node-2\n",[1372,1385,1383],{"__ignoreMap":18},[48,1387,1388],{},"Our Pulsar cluster was deployed on AWS instances. Table 1 and Table 2 show the breakdown of the cost.",[48,1390,1391,1394,1395,1398],{},[351,1392],{"alt":18,"src":1393},"\u002Fimgs\u002Fblogs\u002F63c603ef94c96da8983df22f_pulsar-cluster-cost-breakdown.png","Table 1. Pulsar cluster cost breakdown",[351,1396],{"alt":18,"src":1397},"\u002Fimgs\u002Fblogs\u002F63c6044c6de9b7d4f20f2c69_bookkeeper-ebs-monthly-cost.png","Table 2. BookKeeper EBS monthly cost\nWe deployed bookies on m5.4xlarge instances (16 vCPUs and 64 GB of memory), with 10 TB of storage attached to each of them for ledgers. We chose r5.2xlarge (8 vCPUs and 64 GB of memory) instances for brokers and t3.large (2 vCPUs and 8 GB of memory) instances for ZooKeeper. The total monthly cost was about $7.4K without data transfer. If we included the data transfer cost for 360 TB (about 12 TB of data per day as mentioned above), it would add another $7K, so the total monthly cost would be about $14K.",[32,1400,1402],{"id":1401},"kafka-cost","Kafka cost",[48,1404,1405,1406,1409],{},"Figure 2 depicts the schematic architecture of our Kafka cluster. Kafka has the same options available in replica assignment which uses a comma-separated list of preferred replicas.\n",[351,1407],{"alt":18,"src":1408},"\u002Fimgs\u002Fblogs\u002F63c5fb42ce3bfd413f67cf27_lR0C5TAm81dRvwH8p7MvA3VlwUt6K4H6LF6nP1vFMzxRAwskusarBt9RuYnneXb4pwRPQqF02HF5jr808I9bpU3Cz146lU3M_MTfRDOM0cJAf7r8x95A7VoeTiMMlJjy1W7iF8tw2fGfM8KzarIIr-FmtlpwFXDGvIb2HtrUUbiK9TebqFmRif9aCraXfg.png","Figure 2. Kafka cluster schematic architecture\nWe used the following commands to assign replicas.",[1365,1411,1414],{"className":1412,"code":1413,"language":1370},[1368],"bin\u002Fkafka-topics.sh --create --zookeeper localhost:2181 --topic topicA --replica-assignment 0:1,0:1,0:2 --partitions 3\n\nbin\u002Fkafka-topics.sh --alter --zookeeper localhost:2181 --topic topicA --replica-assignment 0:1,0:1,0:2,0:2 --partitions 4\n",[1372,1415,1413],{"__ignoreMap":18},[48,1417,1418],{},"In order to have all the data available in one AZ, broker 1 should have the leaders of all partitions. In Kafka, writes only go to the leaders. In our case, all the data was available in one broker for less data transfer costs between the AZs.",[1420,1421,1422],"blockquote",{},[48,1423,1424],{},"Whenever a broker goes down, Kafka will restore the leadership to the broker that comes first in the list with the preferred replicas. This is the default behavior enabled in the latest version of Kafka with auto.leader.rebalance.enable=true.",[48,1426,1427,1428,1433,1434,1437],{},"In our performance test, the Kafka cluster was managed by ",[55,1429,1432],{"href":1430,"rel":1431},"https:\u002F\u002Faws.amazon.com\u002Fmsk\u002F",[264],"Amazon MSK",", using 5 m5.4xlarge broker instances, each having 16 vCPUs, 64 GB of memory, and 10 TB of storage. There is 50 TB in total, enough for 24 TB of data (replication factor set to 2) per month. In addition to the infrastructure, we had to pay for the intra-region data transfer cost between the AZs. The total monthly cost was almost $20K.\n",[351,1435],{"alt":18,"src":1436},"\u002Fimgs\u002Fblogs\u002F63c5fb4272078a700dd6500c_V1ZtwxejOwL3R8XKouOT0b6KHg8DBgH9uVjmG9pmLojBqO3IKK0Ssztsg-vlLH5hDH2dpAjxLuRj1nkajGoEqYRoAU11GUqHOpHrguH-_xYsfQRyAQ1Jly5sjqYQRB8p7ujSM_bkNToXaOPr0OEatUsRliQkDyMEBk-syfTmKGCbOCYss6R1tpr3WARQOA.png","Figure 3. Kafka cluster monthly cost",[32,1439,1441],{"id":1440},"kinesis-data-streams-cost","Kinesis Data Streams cost",[48,1443,1444],{},"As Amazon manages the Kinesis Data Streams service for us, we will only focus on the cost in this section.",[48,1446,1447,1448,1451,1452,1457,1458,1461,1462,1465],{},"As mentioned above, the average message size cannot be more than 1 MB for Kinesis Data Streams. Our internal team came up with a workaround that they put messages in S3 and provided the S3 path inside Kinesis. This means that the solution had extra S3 costs. To make up for that, I put 180 baseline records per second as per 1 MB of data.\n",[351,1449],{"alt":18,"src":1450},"\u002Fimgs\u002Fblogs\u002F63c5fb43a029e046b6034826_a9H910EDPDkjrhj3l3sIS442ggw4eyZiNM-s-2hAnjqbz873j6cyleayp9JARa_1M07VwBfh4nLxFmeEIVJVyAc3SNM448KGjMi4TfEqJci535QIQrpFCo3quvZsqnvGXJWp0YRPlPhbzP9Y9Bs9t3YEJKHNtIxMD-c2u8wKhyGrKJcVg-1vOxXVeOTXsg.png","Figure 4. 180 baseline records per second\nWe set the buffer for growth to 20% and the number of ",[55,1453,1456],{"href":1454,"rel":1455},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fstreams\u002Flatest\u002Fdev\u002Fbuilding-enhanced-consumers-api.html",[264],"enhanced fan-out consumers"," to 3. Each consumer had dedicated throughput. If you increase the number of fan-out consumers, the cost will increase linearly.\n",[351,1459],{"alt":18,"src":1460},"\u002Fimgs\u002Fblogs\u002F63c5fb43f33b34b630d70b78_XcsCgqkD_rR0YObXPwsWh-8LiE-LIFhF4roXB641WDILFNLcAzrhR3bijZ5OCqZe-eYADp57SdKqTs3NVnYgY_NxKkok3DvnKzBdS0BW4r76wdlwNeq931wqwKd6wEIu-W-VG5IxyrveAFzqTb_L9I5oYjJqcVW1w89-HhKvgShJLdGju8CJPeeEeUerQA.png","Figure 5. The growth buffer and the number of enhanced fan-out consumers\nThe total monthly cost reached about 28K. Figure 6 shows the cost details.\n",[351,1463],{"alt":18,"src":1464},"\u002Fimgs\u002Fblogs\u002F63c5fb428028b3659e5b1536_iTWOoutFGftfiIzux2YtQSQ5FxysmrbBHFf-zY8z4gRakx2UKxfZ2Tiv3OB--ePqD6qYN9d96SpHnLCQvApipx66WfeyvCcE5B7GkouMbxYryx2yxFpHPNRZF1EVxafmd04sgNv2rtdn1wMJOBbcu60YLeIcwwouJswbGitxfctC8uNs_E9SdWbqkM0axw.png","Figure 6. Amazon Kinesis Data Streams monthly cost\nThe results show that Pulsar stands out as the most cost-effective option with only $14K per month compared to Kafka ($20K) and Kinesis ($28K). That's why our primary selection for streaming data is Pulsar.",[40,1467,1469],{"id":1468},"change-the-game-migrate-to-pulsar","Change the game: Migrate to Pulsar",[48,1471,1472],{},"Now that we know how Pulsar aces the game, let’s look at how to migrate to Pulsar from other systems like Kafka.",[32,1474,1476],{"id":1475},"kafka-on-pulsar-kop","Kafka-on-Pulsar (KoP)",[48,1478,1479,1484],{},[55,1480,1483],{"href":1481,"rel":1482},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Fkop",[264],"KoP"," leverages a Kafka protocol handler on Pulsar brokers, which processes Kafka messages. It allows you to easily migrate your existing Kafka applications and services to Pulsar without modifying the code. You only need to make minor changes on the Pulsar server side. Clients do not even need to know whether they are connected to Kafka or Pulsar. Specifically, to get started with KoP, you need to do the following:",[1486,1487,1488,1491,1494],"ol",{},[342,1489,1490],{},"Add KoP configurations (for example, messagingProtocols and entryFormat) to broker.conf or standalone.conf.",[342,1492,1493],{},"Add the protocol handler (a nar file) to your server.",[342,1495,1496],{},"Remember to change the Kafka cluster URL to the Pulsar cluster URL in your client code.",[48,1498,1353,1499,1502,1503,190],{},[351,1500],{"alt":18,"src":1501},"\u002Fimgs\u002Fblogs\u002F63c5fb43c09588c381dedfa6_X05HC_VQd6Zr0N2hZacvKcL0NR7jhXQ6GttSnYvQlmnc17JhX_oIyE6T-Uh_SiEZvhEuEJRuMXdaiw1V7zsIMLZToxdGDH3KHCaPsjVxj3eF3nd7cTvKi-CiS-lzf1eiCkI7xkLjxomyDiDFpBxetxHUnmAD1EZeGY2W6qJGDvIf3nNEJdj57PYuEucVsQ.png","Figure 7. KoP architecture\nFor more information, see the ",[55,1504,1507],{"href":1505,"rel":1506},"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=v7qYBQVFz_k#t=29m33s",[264],"demo video",[32,1509,1511],{"id":1510},"pulsar-adaptor-for-apache-kafka","Pulsar adaptor for Apache Kafka",[48,1513,1514,1515,1520],{},"This ",[55,1516,1519],{"href":1517,"rel":1518},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002F2.10.x\u002Fadaptors-kafka\u002F",[264],"tool"," was developed before KoP to help users migrate to Pulsar. To use it, you need to change the regular Kafka client dependency and replace it with the Pulsar Kafka wrapper. We don’t recommend it as it is only applicable to Java-based clients and it is not suitable for our use case.",[32,1522,1524],{"id":1523},"amqp-on-pulsar-aop","AMQP-on-Pulsar (AoP)",[48,1526,1527,1528,1533],{},"Similar to KoP, ",[55,1529,1532],{"href":1530,"rel":1531},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Faop",[264],"AoP"," is implemented as a Pulsar protocol handler. Messaging systems like RabbitMQ and ActiveMQ use the AMQP protocol for their messages.",[40,1535,1537],{"id":1536},"connectors-how-to-make-the-best-use-of-them","Connectors: How to make the best use of them",[48,1539,1540,1541,1154,1546,1164,1551,1556],{},"Pulsar’s ecosystem still has a long way to go compared with Kafka’s, especially for Pulsar connectors. Currently, Pulsar supports connectors for popular systems like ",[55,1542,1545],{"href":1543,"rel":1544},"https:\u002F\u002Fhub.streamnative.io\u002Fdata-processing\u002Fpulsar-spark\u002F3.1.1\u002F",[264],"Spark",[55,1547,1550],{"href":1548,"rel":1549},"https:\u002F\u002Fhub.streamnative.io\u002Fdata-processing\u002Fpulsar-flink\u002F1.15.1.1\u002F",[264],"Flink",[55,1552,1555],{"href":1553,"rel":1554},"https:\u002F\u002Fhub.streamnative.io\u002Fconnectors\u002Felasticsearch-sink\u002F2.9.2\u002F",[264],"Elasticsearch",", while there are more Kafka connectors available. In this connection, you can use the KoP-enabled Pulsar cluster with Kafka connectors to implement Pulsar connectors.",[48,1558,1559,1560,1564,1565,1568],{},"One such connector we created is the Pulsar-Druid connector. As the Kafka-Druid connector is already available, you can enable KoP for your Pulsar cluster; after Kafka clients publish live events to the Pulsar cluster, end users can query the live data as they are synchronized by the Kafka-Druid connector. For more information, see the ",[55,1561,1507],{"href":1562,"rel":1563},"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=v7qYBQVFz_k#t=34m25s",[264],".\n",[351,1566],{"alt":18,"src":1567},"\u002Fimgs\u002Fblogs\u002F63c5fb431437ea90457d1c30_-B63mPXLiCqpsxVzdxKNQCi0NA7GQQhBxDl2VPbvS8ukHY-u15AyjBkNg4H2-fCrQ_evllSISPD4QmDmJskxY9bgV0ntx63ij9f7Jb00iWxcjsvgs3Ee9Yot2nOLvLdl7i5WVTY35lR4lsr7-lpGnsyccETEy4ibOn0HRY8t-3ErSej-tessq14HOmrnFQ.png","Figure 8. Implement the Pulsar-Druid connector with the KoP-enabled Pulsar cluster",[40,1570,931],{"id":930},[48,1572,1573],{},"In this blog, we discussed some common requirements for data streaming and introduced popular systems with their advantages and disadvantages. We performed some tests on these systems and explained why we ultimately selected Apache Pulsar as our messaging platform. To help migrate from other tools to Pulsar, the Pulsar community provides the protocol handler plugin to support a safe transition. In addition, you can also use it to achieve new Pulsar connectors with existing Kafka connectors.",[40,1575,1577],{"id":1576},"more-resources","More Resources",[48,1579,1580,1581,1586,1587,1592],{},"Pulsar Summit Europe 2023 is taking place virtually on May 23rd. Engage with the community by ",[55,1582,1585],{"href":1583,"rel":1584},"https:\u002F\u002Fsessionize.com\u002Fpulsar-virtual-summit-europe-2023\u002F",[264],"submitting a CFP"," or ",[55,1588,1591],{"href":1589,"rel":1590},"https:\u002F\u002F6585952.fs1.hubspotusercontent-na1.net\u002Fhubfs\u002F6585952\u002FSponsorship%20Prospectus%20Pulsar%20Virtual%20Summit%20Europe%202023.pdf",[264],"becoming a community sponsor"," (no fee required). Meanwhile, check out the following resources:",[339,1594,1595,1601,1608,1614,1621,1626,1633,1640,1647],{},[342,1596,1597],{},[55,1598,1600],{"href":1599},"\u002Fblog\u002Fapache-pulsar-vs-apache-kafka-2022-benchmark","Apache Pulsar vs. Apache Kafka 2022 Benchmark",[342,1602,1603],{},[55,1604,1607],{"href":1605,"rel":1606},"https:\u002F\u002Fhevodata.com\u002Flearn\u002Fpulsar-vs-kafka\u002F",[264],"Apache Pulsar vs Kafka: Which is Better?",[342,1609,1610],{},[55,1611,1613],{"href":1208,"rel":1612},[264],"Why Nutanix Beam went ahead with Apache Pulsar instead of Apache Kafka?",[342,1615,1616],{},[55,1617,1620],{"href":1618,"rel":1619},"https:\u002F\u002Faws.amazon.com\u002Fkinesis\u002Fdata-streams\u002Ffaqs\u002F",[264],"Amazon Kinesis Data Streams FAQs",[342,1622,1623],{},[55,1624,1316],{"href":1314,"rel":1625},[264],[342,1627,1628],{},[55,1629,1632],{"href":1630,"rel":1631},"https:\u002F\u002Fnats.io\u002Fblog\u002Fmatrix-dendrite-kafka-to-nats\u002F",[264],"The Matrix Dendrite Project move from Kafka to NATS\t",[342,1634,1635],{},[55,1636,1639],{"href":1637,"rel":1638},"https:\u002F\u002Fcwiki.apache.org\u002Fconfluence\u002Fdisplay\u002FKAFKA\u002FReplication+tools#Replicationtools-Howtousethetool?.2",[264],"How to use the Kafka replication tool",[342,1641,1642],{},[55,1643,1646],{"href":1644,"rel":1645},"https:\u002F\u002Fdocs.streamnative.io\u002Fplatform\u002Flatest\u002Fconcepts\u002Fkop-concepts",[264],"KoP documentation",[342,1648,1649],{},[55,1650,1511],{"href":1651,"rel":1652},"https:\u002F\u002Fpulsar.apache.org\u002Fdocs\u002Fadaptors-kafka\u002F",[264],{"title":18,"searchDepth":19,"depth":19,"links":1654},[1655,1656,1663,1668,1673,1674,1675],{"id":1102,"depth":19,"text":1103},{"id":1144,"depth":19,"text":1145,"children":1657},[1658,1659,1660,1661,1662],{"id":1172,"depth":279,"text":1153},{"id":1192,"depth":279,"text":1084},{"id":1201,"depth":279,"text":1202},{"id":1255,"depth":279,"text":1163},{"id":1290,"depth":279,"text":1169},{"id":1322,"depth":19,"text":1323,"children":1664},[1665,1666,1667],{"id":1346,"depth":279,"text":1347},{"id":1401,"depth":279,"text":1402},{"id":1440,"depth":279,"text":1441},{"id":1468,"depth":19,"text":1469,"children":1669},[1670,1671,1672],{"id":1475,"depth":279,"text":1476},{"id":1510,"depth":279,"text":1511},{"id":1523,"depth":279,"text":1524},{"id":1536,"depth":19,"text":1537},{"id":930,"depth":19,"text":931},{"id":1576,"depth":19,"text":1577},"2023-01-17","This blog introduces and compares different streaming systems like Kafka, Pulsar, Amazon Kinesis, and NATS, and then explains how to migrate to Pulsar from other platforms.","\u002Fimgs\u002Fblogs\u002F63c60236f3fd0cf51bccd8c6_streaming-war-top-image.jpg",{},"\u002Fblog\u002Fstreaming-war-and-how-apache-pulsar-is-acing-the-battle","11 min read",{"title":1088,"description":1677},"blog\u002Fstreaming-war-and-how-apache-pulsar-is-acing-the-battle",[1685,1153,1686,1083],"Intro","Multi-Tenancy","uox2TZDdaol8yP1s6QZ0saCOfefJ3Fl1m6gMO0xKXyI",[1689,1702],{"id":1690,"title":1090,"bioSummary":1691,"email":289,"extension":8,"image":1692,"linkedinUrl":1693,"meta":1694,"position":1699,"stem":1700,"twitterUrl":289,"__hash__":1701},"authors\u002Fauthors\u002Fshivji-kumar-jha.md","Shivji Kumar Jha is an Architect at Nutanix and leads the Cloud Data Platform team, helping Nutanix products with data storage, databases, messaging, analytics, etc. With a team of 6, Shiv works on making Apache Pulsar, NATS, Apache Druid, Debezium, Presto, and other systems available as a platform for all Nutanix cloud products.","\u002Fimgs\u002Fauthors\u002Fshivji-kumar-jha.jpeg","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fshivjijha\u002F",{"body":1695},{"type":15,"value":1696,"toc":1697},[],{"title":18,"searchDepth":19,"depth":19,"links":1698},[],"Architect, Nutanix","authors\u002Fshivji-kumar-jha","Cj44i975O0v1hc66DwftdgPlvJvdmfx4EVXOcsORQ3E",{"id":1703,"title":1091,"bioSummary":1704,"email":289,"extension":8,"image":1705,"linkedinUrl":1706,"meta":1707,"position":1712,"stem":1713,"twitterUrl":289,"__hash__":1714},"authors\u002Fauthors\u002Fsachidananda-maharana.md","Sachidananda Maharana is a Member of Technical Staff IV at Nutanix and works on the Cloud Data Platform team. Sachidananda focuses on streaming platforms like Apache Pulsar, and database platforms like Apache Druid, to support different Nutanix products.","\u002Fimgs\u002Fauthors\u002Fsachidananda-maharana.jpeg","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fsachidanandamaharana\u002F",{"body":1708},{"type":15,"value":1709,"toc":1710},[],{"title":18,"searchDepth":19,"depth":19,"links":1711},[],"MTS IV, Nutanix","authors\u002Fsachidananda-maharana","SGv-HJ7i-hqfHt8-FtIsPACqRmKklkKjKvh8UOOC3Ts",[1716,1724,1732],{"path":1717,"title":1718,"date":1719,"image":1720,"link":-1,"collection":1721,"resourceType":1722,"score":1723,"id":1717},"\u002Fblog\u002F10-useful-pulsarctl-commands-manage-cluster","10 Useful Pulsarctl Commands to Manage Your Cluster","2021-11-23","\u002Fimgs\u002Fblogs\u002F63c7fb829046660d7d11b4aa_63b3553d893b10bc4f8bb89a_screen-shot-2021-11-23-at-12.07.13-pm.png","blogs","Blog",0.6,{"path":1725,"title":1726,"date":1727,"image":1728,"link":-1,"collection":1729,"resourceType":1730,"score":1731,"id":1725},"\u002Fsuccess-stories\u002Fqraft","Qraft Technologies Increases AI-Powered Order Execution Performance with Apache Pulsar","2022-12-22","\u002Fimgs\u002Fsuccess-stories\u002F67956ab102bb4936fb197cfd_SN-SuccessStories-qraft.webp","successStories","Case Study",0.55,{"path":1733,"title":1734,"date":1735,"image":1736,"link":-1,"collection":1721,"resourceType":1722,"score":1737,"id":1733},"\u002Fblog\u002Fapache-pulsar-seven-years-on-what-we-built-what-we-learned-whats-next","Apache Pulsar, Seven Years On: What We Built, What We Learned, What’s Next","2025-09-25","\u002Fimgs\u002Fblogs\u002F68d4ddd72eeca005c8fc8334_Pulsar-7-years.png",0.5,1776749907789]