Apache Arrow is an open source, columnar in-memory data format designed for efficient data processing and analytics. This book harnesses the author’s 15 years of experience to show you a standardized way to work with tabular data across various programming languages and environments, enabling high-performance data processing and exchange.
This updated second edition gives you an overview of the Arrow format, highlighting its versatility and benefits through real-world use cases. It guides you through enhancing data science workflows, optimizing performance with Apache Parquet and Spark, and ensuring seamless data translation. You’ll explore data interchange and storage formats, and Arrow's relationships with Parquet, Protocol Buffers, FlatBuffers, JSON, and CSV. You’ll also discover Apache Arrow subprojects, including Flight, SQL, Database Connectivity, and nanoarrow. You’ll learn to streamline machine learning workflows, use Arrow Dataset APIs, and integrate with popular analytical data systems such as Snowflake, Dremio, and DuckDB. The latter chapters provide real-world examples and case studies of products powered by Apache Arrow, providing practical insights into its applications.
By the end of this book, you’ll have all the building blocks to create efficient and powerful analytical services and utilities with Apache Arrow.

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Spis treści

In-Memory Analytics with Apache Arrow. Accelerate data analytics for efficient processing of flat and hierarchical data structures - Second Edition eBook -- spis treści

• 1. Getting Started with Apache Arrow
• 2. Working with Key Arrow Specifications
• 3. Format and Memory Handling
• 4. Crossing the Language Barrier with the Arrow C Data API
• 5. Acero: A Streaming Arrow Execution Engine
• 6. Using the Arrow Datasets API
• 7. Exploring Apache Arrow Flight RPC
• 8. Understanding Arrow Database Connectivity (ADBC)
• 9. Using Arrow with Machine Learning Workflows
• 10. Powered by Apache Arrow
• 11. How to Leave Your Mark on Arrow
• 12. Future Development and Plans