DuckDB & Arrow in Action: Scalable Pipelines and Vectorized Queries Python Rust

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Bol Reactive PublishingModern data engineering is shifting toward lightweight, high-performance in-memory analytics. DuckDB & Arrow in Action provides a practical, hands-on guide to combining the fast SQL query processing of DuckDB with Apache Arrow's zero-copy memory format.Whether you are building real-time data pipelines or optimizing local analytical processing, this book delivers clear patterns for high-throughput data manipulation without the overhead of massive distributed clusters.What You Will Learn: - Core Fundamentals: Master DuckDB's columnar execution engine and the memory layout of Apache Arrow.- Zero-Copy Workflows: Pass large datasets seamlessly between DuckDB, PyArrow, and Rust without serialization overhead.- Vectorized Processing: Write high-performance SQL and programmatic queries that leverage SIMD hardware acceleration.- Pipeline Integration: Build efficient ETL and ELT data pipelines in Python and Rust for production environments.- Performance Tuning: Optimize memory usage, indexing, and query execution plans for large-scale datasets.Designed for software engineers, data engineers, and data scientists, this book equips you with the tools to build faster, cost-effective data infrastructure using modern open-source technologies.

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Reactive PublishingModern data engineering is shifting toward lightweight, high-performance in-memory analytics. DuckDB & Arrow in Action provides a practical, hands-on guide to combining the fast SQL query processing of DuckDB with Apache Arrow's zero-copy memory format.Whether you are building real-time data pipelines or optimizing local analytical processing, this book delivers clear patterns for high-throughput data manipulation without the overhead of massive distributed clusters.What You Will Learn: - Core Fundamentals: Master DuckDB's columnar execution engine and the memory layout of Apache Arrow.- Zero-Copy Workflows: Pass large datasets seamlessly between DuckDB, PyArrow, and Rust without serialization overhead.- Vectorized Processing: Write high-performance SQL and programmatic queries that leverage SIMD hardware acceleration.- Pipeline Integration: Build efficient ETL and ELT data pipelines in Python and Rust for production environments.- Performance Tuning: Optimize memory usage, indexing, and query execution plans for large-scale datasets.Designed for software engineers, data engineers, and data scientists, this book equips you with the tools to build faster, cost-effective data infrastructure using modern open-source technologies.


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