Firebolt

🚀 Big News! Firebolt just got even faster & more flexible --- Introducing Firebolt’s new editions & compute families! At Firebolt, we know that one size doesn’t fit all when it comes to analytical workloads and data-intensive AI applications. That’s why we’re launching Standard & Enterprise editions alongside storage-optimized & compute-optimized compute families, giving you more flexibility, control, and cost efficiency than ever before. 🔹 Standard Edition – A cost-effective way to get sub-second analytics. 🔹 Enterprise Edition – Advanced security, scalability & compliance for mission-critical workloads. 🔹 Storage-optimized – Ideal for massive datasets & caching-heavy workloads. 🔹 Compute-optimized – Designed for high-concurrency workloads that need raw processing power. Now you can choose the best setup for your needs and optimize both performance & costs seamlessly. Read the full blog from Manish Agarwal to learn more about the launch: https://lnkd.in/gjdv2Gba #Firebolt #DataAnalytics #BigData #CloudComputing #DataWarehouse #TechLaunch

Firebolt at BTW: Advancing Database Research & Industry Impact 🔥 Last week, members of our engineering team, Pascal Schulze, Leonard von Merzljak, Zhen Li, and Benjamin Wagner, attended BTW, the bi-annual research conference for the German-speaking database community. This event was all about deep database research and the people shaping the future of data infrastructure. At Firebolt, we don’t just build a high-performance cloud data warehouse—we push the boundaries of what’s possible in: ✔️ Query execution ✔️ Optimization ✔️ Metadata management 🔍 Key Highlights: 1. Query Planner Deep Dive – Zhen Li presented our query planner to 200+ researchers, showcasing how Firebolt optimizes queries in ways that many traditional systems struggle with. 2. Metadata Layer Innovations – Benjamin Wagner presented our work on Firebolt’s metadata layer, detailing years of engineering effort behind one of the most efficient and scalable metadata architectures in modern data warehousing. 3. The Next Generation of Database Engineers – Strong engagement from students interested in database engineering, with TUMuchData, the database student initiative of TU Munich, having a particularly strong presence at BTW this year. 📈 Building on Our Momentum It was exciting to present a technical deep dive into some of our cutting-edge components at BTW. Next step? Expanding this momentum beyond research and making Firebolt the go-to choice for teams provisioning and deploying modern data infrastructure. 🔜 This year, we are doubling down on: ➡️ Attending more meetups & conferences ➡️ Engaging with customers & researchers ➡️ Consistently proving why Firebolt is the best data warehouse for high-performance analytics at scale Excited for what’s ahead! 🚀 #Firebolt #DatabaseResearch #CloudDataWarehouse #QueryProcessing

Why do AI applications need a new kind of data infrastructure? 🤔 GenAI, Agentic AI, and vector-based retrieval demand highly concurrent, low-latency queries, something legacy warehouses weren’t built for. We’ll break it all down at Firebolt Forward. 🔗 Register Now: https://lnkd.in/gS9cFPnt

🔥 FireX Bangalore: AI, Data, and the Future of Scale Recently, we brought together top CTOs, CDOs, and AI leaders for an exclusive roundtable on building AI-ready data infrastructure that can handle scale, speed, and efficiency. Key takeaways: 1. AI is pushing data platforms to their limits; latency, cost, and complexity are the most significant bottlenecks. 2. Speed + efficiency = AI success- Organizations must rethink architectures for sub-second analytics at scale. 3. Community matters: great conversations, real-world challenges, and strategies from leaders driving AI innovation. A huge thanks to the incredible executives who joined us: Biocon, Platformatory, PharmEasy, MakeMyTrip, Games24x7, Ujjivan Small Finance Bank, Saarthi.ai, Postman, Perfios, Tata CLiQ Fashion, Flipkart, Algonomy and others. And of course, to Eldad Farkash, Hemanth Vedagarbha and Sandeep Mathur for sharing Firebolt’s vision for building the data warehouse for data-intensive AI applications. This is just the beginning—let’s keep the conversation going! 👇 #AI #DataInfrastructure #CTO #DataEngineering #FireX #Firebolt

Online upgrades have been in Firebolt for a little while... but did you notice? Andrey Kalganov explains how we quietly give Firebolt users the latest version of Firebolt with no impact to performance: https://lnkd.in/dKNRdRcS

Geospatial data processing can be computationally expensive. Firebolt optimizes for speed, scale, and efficiency using S2 cells, shape indexes, and geospatial pruning. In Part II of our geospatial series, we explore: ✅ How Firebolt validates and normalizes geospatial data ✅ How S2 cells & coverings enable faster queries ✅ How data pruning & partitioning reduce scan time If you're working with large-scale spatial data, this deep dive blog by Demian Hespe (software engineer, Firebolt) on architecture and internal representation of the GEOGRAPHY data type is for you. 🔗 Read more: https://lnkd.in/g5nhVj5q #GeospatialData #DataEngineering #CloudDataWarehouse #Firebolt #GeospatialAnalytics

🚀 Firebolt Forward is Coming! 🚀 AI is reshaping modern applications, though legacy data platforms and outdated data infrastructure are slowing innovation down. Today's modern and data-intensive AI applications demand speed, scale, and flexibility. Firebolt delivers all three—enabling organizations to build AI applications faster, smarter, and without limits. Come join us on March 27, 2025 (8 AM PDT) and discover how Firebolt powers large-scale, high-concurrency, low-latency AI workloads without breaking the bank. 🔗 Register now → https://lnkd.in/gZ-uQMDD #FireboltForward #datainfrastructure #aiatscale #datawarehouse #ai #generativeai #genai #agenticai

Firebolt is at BTW Bamberg this week! We have Benjamin Wagner Zhen Li, Leonard von Merzljak, Pascal Schulze, and others diving deep into query optimization and ACID multi-writer transactions at the event. 📢 Talks you won’t want to miss: 🔹 Zhen presents "Optimizing Correlated Aggregate Subqueries in Firebolt" – our first deep dive into Firebolt’s query optimizer. 🔹 Pascal covers "Firebolt Transactions: Consistency, Availability, and Performance - Pick All Three" – how we achieve fast, ACID, multi-writer transactions. Plus, we’re at the sponsor booth – come say hi or drop us a message! #BTW2024 #DataEngineering #QueryOptimization #CloudData #Firebolt

𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐲𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐅𝐢𝐫𝐞𝐛𝐨𝐥𝐭 Managing schema definitions, indexing, and scaling ingestion workloads shouldn’t be a headache for data engineers. Firebolt makes it seamless with: ✅ Automatic Schema Discovery – Let Firebolt detect schema on the fly. ✅ Elastic Scaling – Add nodes with a simple SQL command to boost ingestion speed. ✅ Synchronous Aggregating Indexes – Speed up subsequent queries without user intervention. With one SQL command, point at your S3 bucket and get started. Firebolt’s auto start/stop and performance-tuned indexing make ingestion not just faster but also smarter. Read how Firebolt streamlines ingestion with Philip Simko: https://lnkd.in/gN24pxYQ #DataEngineering #DataIngestion #CloudDataWarehouse #Firebolt #SQL #BigData #Ingestion

📊 Master Primary Indexes in Firebolt 📊 Indexes aren’t just a feature; they’re a game-changer for query performance. Firebolt’s Primary Indexes let you: ✅ Dramatically reduce query latency ✅ Optimize data pruning and scanning ✅ Handle massive datasets with precision In this blog, Hiren Patel (Head of Product) breaks down: - How primary indexes work in Firebolt. - Strategies for designing and selecting the right indexes for your workloads. - Common use cases and best practices for optimization. 👉 Read, listen, and optimize: https://lnkd.in/gjr-4WxD #DataEngineering #CloudDataWarehouse #FireboltTech #PerformanceOptimization #Indexes