Show HN: Denormalized – Embeddable Stream Processing in Rust and DataFusion
📰 Hacker News · ambrood
Learn about Denormalized, an embeddable stream processing engine in Rust using Apache DataFusion, and how it can improve performance and cost for streaming workloads
Action Steps
- Explore the Denormalized repository on GitHub to learn more about its architecture and implementation
- Run the Denormalized engine using the provided examples to understand its performance and capabilities
- Configure Denormalized to work with Apache DataFusion for optimized stream processing
- Test Denormalized with sample streaming data to evaluate its efficiency and scalability
- Compare Denormalized with other stream processing engines, such as Spark or Snowflake, to determine its advantages and disadvantages
Who Needs to Know This
Data engineers and developers can benefit from Denormalized to build high-performance stream processing applications, and its embeddable nature makes it suitable for various use cases
Key Insight
💡 Denormalized leverages the power of Apache DataFusion and Rust to provide a high-performance, embeddable stream processing engine for modern data processing needs
Share This
🚀 Introducing Denormalized, an embeddable stream processing engine in Rust using Apache DataFusion! 📈 Improve performance and reduce costs for your streaming workloads 📊
Key Takeaways
Learn about Denormalized, an embeddable stream processing engine in Rust using Apache DataFusion, and how it can improve performance and cost for streaming workloads
Full Article
tl;dr we built an embeddable stream processing engine in Rust using apache DataFusion, check us out at https://github.com/probably-nothing-labs/denormalized Hey HN, We’d like to showcase a very early version of our embeddable stream processing engine called Denormalized. The rise of DuckDB has abundantly made it clear that even for many workloads of Terabyte scale, a single node system outshines the distributed query engines of previous generation such as Spark, Snowflake etc in terms of both performance and cost. Now a lot of workloads DuckDB is used for were normally considered to be “big data” in the previous generation, but no more. In the context of streaming especially, this problem is more acute. A streaming system is designed to incrementally process large amounts of data over a period of time. Even on the upper end of scale, productionized use-cases of stream processing are rarely performing compute on more than tens of gigabytes of data at a given time. Ev
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