Qbeast Overview

Qbeast is a Data Lakehouse optimization product that improves cost efficiency and accelerates queries through superior data layout and cloud integration. It leverages advanced indexing and sophisticated sampling techniques to organize data, targeting the speed-up of analytics and AI workloads while significantly reducing computing costs.

• Multi-dimensional Indexing: Uses a tree structure to efficiently organize and query data.
• Automatic Layout Optimization: Continuously monitors and optimizes data distribution and indexing.
• Multi-Cloud Support: Available as an open-source project with deployments on Amazon Web Services, Google Cloud, and Microsoft Azure Cloud.
• Platform-agnostic: Works the major open table formats: Delta Lake, Apache Iceberg and Apache Hudi and query platforms that support them, including Apache Spark, Trino, Databricks, and many more.
• Principled Sampling: Provides fast and statistically representative approximate query results without loading entire datasets.

Qbeast integrates with Apache Spark through “qbeast-spark” to enhance data management and query performance. It partitions data into smaller cubes organized into a tree structure, ensuring efficient queries by targeting relevant data segments. Automatic indexing and integration of new data streamline data management, reducing the administrative overhead for data engineers.

Quickstart with Qbeast Spark

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