Technical Deep Dive: Automated Column Statistics · Product
Firebolt’s automated column statistics keep optimizer insights up to date, improving query plans and performance automatically—no query changes required.
Explore technical tips and topics from Firebolt experts and the community.
Firebolt’s automated column statistics keep optimizer insights up to date, improving query plans and performance automatically—no query changes required.
Technical Deep Dive: Efficient and ACID Compliant Vector Search Indexes in Firebolt
Learn how Late Materialization speeds up top-K queries by delaying column scans.
Firebolt FuzzBerg to accelerate security testing of Iceberg and other file based readers.
Firebolt ARM Rollout; optimising price performance
Firebolt supports explicit, multi-statement transactions using BEGIN, COMMIT, and ROLLBACK syntax while maintaining ACID compliance and stateless architecture.
Technical deep dive on the powerful MERGE SQL command, enabling simultaneous operations on a single table.
Faster queries from the start with smart cache loading on engine boot, upgrade, and scale
Firebolt built Auror to securely validate container images with low latency in Kubernetes clusters.
Firebolt removes redundant joins to boost SQL performance and optimize complex subqueries.
This features enables users to not use precious resources on just maintaining a connection when in fact their client is not doing anything.
Firebolt's CTO and VP of Engineering discuss the launch of Firebolt's self-managed version, Firebolt Core.
Hear about the different methods deployed in Firebolt for reducing the number of scanned rows (aka pruning).
Discover how Firebolt delivers seamless, no-downtime upgrades using shadow clusters and real-time performance verification to ensure peak reliability.
Firebolt's new READ_ICEBERG capability does a lot of heavy lifting to provide low-latency access to your Iceberg tables.
Discover how Firebolt implements SQL functions for data exploration.
Connect Firebolt to AI tools like Claude and Copilot using the new MCP Server to streamline workflows, run smart queries, and boost data engineering efficiency.
Explore how Firebolt's transaction system maps to the four essential steps—Execute, Validate, Order, Persist. Learn how Firebolt uses MVCC, OCC, and Foundation
We will explore in more detail how Firebolt implements robust operations on geospatial data.
Explore how Firebolt processes & optimizes GEOGRAPHY data using S2 cells, shape indexes, and query pruning for peak performance.
Implementing fast geospatial queries in Firebolt using the S2 Geometry Library.
Discover Firebolt’s Zero-Copy Clone feature: a cost-efficient way to clone massive tables instantly without duplicating data.
Learn how Firebolt identifies zero-day vulnerabilities as efficiently as its query processor.
Gain insights into how Firebolt was built to redefine cloud data performance and scalability.
Enhance query performance with Firebolt's caching and subresult reuse features.
Learn about making a query engine Postgres-compliant in part one of this in-depth series.
Firebolt engines provide multi-dimensional elasticity to our customers allowing them to achieve desired price-performance without causing downtime for customers