The most advanced open-source format for data lakes
Works in any Data Lake
Store the data in the Object Storage of your preference.
Compatible with all BI tools
Plug in your visualizer and get faster insights.
How we organize data
Qbeast Metadata
Based on Delta Lake format, Qbeast adds the necessary information to query efficiently
We organize the data in what we call “cubes”. Each cube’s elements are written in a single parquet file, allowing the query engine to filter out some of them before reading their content.
Data Skipping
The usage of an index helps avoid reading the entire dataset, reducing the amount of data transfer involved and speeding up the query. Qbeast Format allows you to index your data on as many columns as you need and filter directly the files to answer the search.
Approximate Queries
Qbeast enables approximate queries, the ability to provide approximate answers to queries at a fraction of the cost of executing the query. With the Qbeast Format, you can access a statistical representative sample of the dataset and return the result of the query within a margin of error.
File Optimization
When writing new data, the file layout could be harm, producing lots of small files or heavily large ones, making uneasy to retrieve the results with the less noise possible. Optimization fixes the overflowed areas and improves the query useful payload by reading more fine-grained files.
Easy to Deploy
It works with any Data Lake storage (S3, Azure..) and is compatible with any BI/ML tool of your choice. Only takes 10 minutes to deploy and enjoy the benefits of querying Qbeast Tables.