Getting Started with DuckDB

Analyze and transform data efficiently with DuckDB, a versatile, modern, in-process SQL database

Key Features

• Use DuckDB to rapidly load, transform, and query data across a range of sources and formats
• Gain practical experience using SQL, Python, and R to effectively analyze data
• Learn how open source tools and cloud services in the broader data ecosystem complement DuckDB’s versatile capabilities
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

DuckDB is a fast in-process analytical database. Getting Started with DuckDB offers a practical overview of its usage. You'll learn to load, transform, and query various data formats, including CSV, JSON, and Parquet. The book covers DuckDB's optimizations, SQL enhancements, and extensions for specialized applications. Working with examples in SQL, Python, and R, you'll explore analyzing public datasets and discover tools enhancing DuckDB workflows. This guide suits both experienced and new data practitioners, quickly equipping you to apply DuckDB's capabilities in analytical projects. You'll gain proficiency in using DuckDB for diverse tasks, enabling effective integration into your data workflows.

What you will learn

• Understand the properties and applications of a columnar in-process database
• Use SQL to load, transform, and query a range of data formats
• Discover DuckDB's rich extensions and learn how to apply them
• Use nested data types to model semi-structured data and extract and model JSON data
• Integrate DuckDB into your Python and R analytical workflows
• Effectively leverage DuckDB's convenient SQL enhancements
• Explore the wider ecosystem and pathways for building DuckDB-powered data applications

Who this book is for

If you’re interested in expanding your analytical toolkit, this book is for you. It will be particularly valuable for data analysts wanting to rapidly explore and query complex data, data and software engineers looking for a lean and versatile data processing tool, along with data scientists needing a scalable data manipulation library that integrates seamlessly with Python and R. You will get the most from this book if you have some familiarity with SQL and foundational database concepts, as well as exposure to a programming language such as Python or R.

Table of Contents

1. An Introduction to DuckDB
2. Loading Data into DuckDB
3. Data Manipulation with DuckDB
4. DuckDB Operations and Performance
5. DuckDB Extensions
6. Semi-Structured Data Manipulation
7. Setting up the DuckDB Python Client
8. Exploring DuckDB's Python API
9. Exploring DuckDB's R API
10. Using DuckDB Effectively
11. Hands-On Exploratory Data Analysis with DuckDB
12. DuckDB – The Wider Pond

Simon Aubury has been working in the IT industry since 2000 as a data engineering specialist. He has an extensive background in building large, flexible, highly available distributed data systems. Simon has delivered critical data systems for finance, transport, healthcare, insurance, and telecommunications clients in Australia, Europe, and Asia Pacific. In 2019, Simon joined Thoughtworks as a principal data engineer and today is associate director of data platforms at Simple Machines in Sydney, Australia. Simon is active in the data community, a regular conference speaker, and the organizer of local and international meetups and data engineering conferences.

Ned Letcher has worked as a data science and software engineering consultant since completing his PhD in computational linguistics in 2018 and currently works at Thoughtworks. He has designed and developed data-powered products and services across a range of industries and helped organizations and teams improve the effectiveness of their data processes and workflows. Ned has also worked as a Python trainer, supporting both tertiary students and data professionals across various organizations. He is active in the data community, speaking at and helping organize meetups and conferences, as well as contributing to a range of open source projects.