Rootconf 2026 Annual Conference

Overview:

FlareDB is an Apache Beam native streaming database for running Beam data pipelines. It’s built in Rust and it uses a streams-tables architecture inspired by the ideas described in the Streaming Systems book (Chapter 6).

The idea is that streams are data in motion, produced by computations (transforms), and a table is the same data at rest, during a windowing or grouping operation. FlareDB persists the results as durable streams/tables on an append-only Apache Paimon table and runs the computations/transforms over the table using Apache DataFusion.

As a result, FlareDB is lightweight, takes fewer resources to run compared to Flink and Spark runners and makes the computational results queryable without the need for an external database.

Who is the intended audience?
Data Engineers, Platform Engineers

Level:
intermediate/advanced

List one or two practical takeaways:

• Quick introduction to the Apache Beam.
• How FlareDB executes Beam data pipelines written in Java/Python on its rust based engine.
• Demo on how to get started with FlareDB and run Beam pipelines

Relevant experience/What is your experience with this problem:
Production Beam pipelines on GCP and Contributing to Apache Beam

How can this help other practitioners:
Chance to learn and explore an alternative to their Flink and Spark systems.

Current state:
The project is open source, recently launched V0.3.2 with support for python pipelines and stateful processing. GitHub: https://github.com/flare-db/flare-db

#database #demo