Apache DataFusion

Apache DataFusion

Apache DataFusion is an open-source query execution framework designed for big data analytics. It is written in Rust and is part of the Apache Arrow ecosystem. DataFusion provides a high-performance, distributed SQL query engine that enables developers to process large datasets using SQL, while benefiting from the efficiency of the Rust programming language and Arrow's in-memory columnar data format.

Web site

Github repository

Tech tags:

Related shared contents:

  • product
    2026-07-10

    This article discusses the construction of a semantic layer on AWS using Stardog's Semantic AI Application integrated with Amazon Aurora and Amazon Redshift. It highlights the importance of a semantic layer in enabling AI agents to reason over fragmented enterprise data without the need for ETL processes. The article also covers the architectural components necessary for implementing agentic analytics, including the model layer, meaning layer, and agent runtime layer, while providing a practical example of a customer 360 agent that utilizes this semantic layer.

  • product
    2026-06-30

    The article discusses the evolution of database architecture from traditional monolithic systems to Lakebase and LTAP, which externalize storage and processing. Lakebase enhances Postgres by decoupling the write-ahead log and data files, leading to improved durability, scalability, and performance. LTAP further innovates by allowing both transactional and analytical processing on a single copy of data in open formats. This architecture addresses common issues faced by traditional OLTP databases, such as data loss and performance degradation during analytics.

  • product
    2025-11-18

    The article discusses how organizations can leverage platform engineering principles to accelerate the development and deployment of generative AI applications. It highlights the challenges faced by organizations in experimenting with generative AI and emphasizes the importance of building reusable components to manage costs and improve efficiency. The article outlines the architecture of generative AI applications, including the integration of various data layers and the role of large language models. It also covers best practices for observability, orchestration, and governance in AI workflows.

  • tech1
    2024-10-24
  • tech1
    2024-10-24

In productions with: