Vectorized execution is a common strategy in nearly every modern query engine, and Benjamin Wagner, Director of Engineering at Firebolt, is here to explain how it works.
Explore the differences between geometry and geography, how Firebolt leveraged the S2 library, and how we return geospatial query results as efficiently as possible.
Learn how Firebolt scales up by leveraging multithreading across all of the available CPU cores.
Firebolt allows you to perform a pure metadata operation to clone tables in milliseconds. It works without duplicating data or requiring lengthy ingestion. Learn how that works and how to use it.
Primary indexes are an essential part of speeding up your analytics. See how they work in Firebolt and how you can best leverage them.
When you add more nodes to your Firebolt cluster, it takes some shuffling to ensure correct results. Learn about the shuffle operator and see how Firebolt scales out.
Learn about how Firebolt caches and reuses intermediate query results to save on computation and speed up your queries.
Firebolt’s "Load data” wizard simplifies data onboarding from S3 with support for Parquet, CSV file formats.
Get insights into how Firebolt’s “COPY FROM” command can be used to ingest data from CSV and PARQUET formats with built-in schema inference.
Get a detailed walkthrough of Firebolt's user-friendly interface, designed to simplify data management and analysis, all with the ease of SQL.