Professional DuckDB Programming : A Practical Guide to Embedded OLAP, Analytical SQL, Data Engineering, and High-Performance Data Processing
Language: English
Published by Amazon Digital Services LLC - Kdp Sep 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 69.49
Quantity: 2 available
Add to basketItem description from seller
Neuware - Picture this: a ten-million-row CSV file, a laptop, and a deadline in twenty minutes.No server to spin up. No cluster to provision. No waiting on a data platform team to grant you access. You open a terminal, run one command, and get your answer before your coffee goes cold.That is the promise DuckDB makes - and the promise this book helps you actually keep. You will start by understanding why this promise works: the columnar storage, the vectorized execution, the zero-copy querying that lets you skip loading data altogether. Then you will put it to work - joining a live Postgres table against a Parquet archive sitting in S3, all in one query. You will embed DuckDB inside a Python service, a Go backend, a Rust application. You will build pipelines that run without complaint at 3 a.m., and you will learn to read a query plan the way a mechanic reads an engine, spotting exactly where the time goes.By the final page, DuckDB will not feel like a new tool you picked up. It will feel like the one you reach for automatically.What's Inside- Foundations: OLAP vs. OLTP, DuckDB's architecture, and where it fits beside Pandas, Postgres, Snowflake, and Spark- Analytical SQL: window functions, CTEs, advanced aggregation, and nested/JSON data- Data engineering: reading and writing CSV, Parquet, and JSON at scale; connecting to Postgres, MySQL, and SQLite- Embedded applications: building with DuckDB in Python, Node.js, Java, Go, and Rust, including transactions and concurrency- Cloud-native workflows: querying S3, Azure, and GCS directly; Iceberg and Delta Lake; dbt, Airflow, and Dagster pipelines; MotherDuck- Performance tuning: profiling with EXPLAIN ANALYZE, data layout strategies, and real benchmark results- Advanced extension: building custom extensions, UDFs, geospatial analytics, and machine learning feature engineeringWho It's Meant ForThis book is written for: - Data engineers tired of spinning up heavy infrastructure for jobs that fit on one machine- Backend developers who want a genuinely fast embedded database inside their applications- Data analysts and scientists ready to move past Pandas' limits without learning a whole new stack- Anyone comfortable with basic SQL, looking for a serious, practical upgradeNo prior DuckDB experience is assumed. A working knowledge of SQL is all you need to start.Every day you keep provisioning infrastructure for problems a laptop could solve is a day you are working harder than you need to. The tool is free, open source, and already faster than what you are using now.The only question left is whether you start today, or after your next deadline forces your hand.Open Chapter 1. Find out what your data has been waiting to tell you.…
Seller Inventory # 9798172621253
- Title
- Professional DuckDB Programming : A Practical Guide to Embedded OLAP, Analytical SQL, Data Engineering, and High-Performance Data Processing
- Author
- Willie K Cooke
- Publisher
- Amazon Digital Services LLC - Kdp Sep 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798172621253
- Item weight
- 390 grams
- Dimensions
- 244x170x13 mm
Picture this: a ten-million-row CSV file, a laptop, and a deadline in twenty minutes.
No server to spin up. No cluster to provision. No waiting on a data platform team to grant you access. You open a terminal, run one command, and get your answer before your coffee goes cold.
That is the promise DuckDB makes — and the promise this book helps you actually keep. You will start by understanding why this promise works: the columnar storage, the vectorized execution, the zero-copy querying that lets you skip loading data altogether. Then you will put it to work — joining a live Postgres table against a Parquet archive sitting in S3, all in one query. You will embed DuckDB inside a Python service, a Go backend, a Rust application. You will build pipelines that run without complaint at 3 a.m., and you will learn to read a query plan the way a mechanic reads an engine, spotting exactly where the time goes.
By the final page, DuckDB will not feel like a new tool you picked up. It will feel like the one you reach for automatically.
What's Inside- Foundations: OLAP vs. OLTP, DuckDB's architecture, and where it fits beside Pandas, Postgres, Snowflake, and Spark
- Analytical SQL: window functions, CTEs, advanced aggregation, and nested/JSON data
- Data engineering: reading and writing CSV, Parquet, and JSON at scale; connecting to Postgres, MySQL, and SQLite
- Embedded applications: building with DuckDB in Python, Node.js, Java, Go, and Rust, including transactions and concurrency
- Cloud-native workflows: querying S3, Azure, and GCS directly; Iceberg and Delta Lake; dbt, Airflow, and Dagster pipelines; MotherDuck
- Performance tuning: profiling with EXPLAIN ANALYZE, data layout strategies, and real benchmark results
- Advanced extension: building custom extensions, UDFs, geospatial analytics, and machine learning feature engineering
This book is written for:
- Data engineers tired of spinning up heavy infrastructure for jobs that fit on one machine
- Backend developers who want a genuinely fast embedded database inside their applications
- Data analysts and scientists ready to move past Pandas' limits without learning a whole new stack
- Anyone comfortable with basic SQL, looking for a serious, practical upgrade
No prior DuckDB experience is assumed. A working knowledge of SQL is all you need to start.
Every day you keep provisioning infrastructure for problems a laptop could solve is a day you are working harder than you need to. The tool is free, open source, and already faster than what you are using now.
The only question left is whether you start today, or after your next deadline forces your hand.
Open Chapter 1. Find out what your data has been waiting to tell you.
"Synopsis" may belong to another edition of this title.
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