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Embeddable stream processing engine based on Apache DataFusion

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Denormalized is a fast embeddable stream processing engine built on Apache DataFusion. It currently supports kafka as a real-time source and a sink, windowed aggregations, and stream joins.

Quick Start

Prerequisites

  • Docker
  • Rust/Cargo installed

Running an example

  1. Start kafka in docker docker run -p 9092:9092 --name kafka apache/kafka
  2. Start emitting some sample data: cargo run --example emit_measurements
  3. Run a simple streaming aggregation on the data using denormalized: cargo run --example simple_aggregation

More examples

A more powerful example can be seen in our Kafka ridesharing example

Roadmap

  • Stream aggregation
  • Stream joins
  • Checkpointing / restoration
  • Session windows
  • Stateful UDF API
  • DuckDB support
  • Reading/writing from Postgres
  • Python bindings
  • Typescript bindings
  • UI

Credits

Denormalized is built and maintained by Denormalized in San Francisco.

This repo is still a work-in-progress and we are actively seeking design partners. If you have have a specific use-case you'd like to discuss please drop us a line via a github issue or email hello@denormalized.io.

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Embeddable stream processing engine based on Apache DataFusion

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