Getting Started with DuckDB: A practical guide for accelerating your data science, data analytics, and data engineering workflows
Author: by Simon Aubury (Author), Ned Letcher (Author)
Publisher finelybook 出版社: Packt Publishing – ebooks Account
Publication Date 出版日期: 2024-undefined-Jun.
Language 语言: English
Print Length 页数: 382 pages
ISBN-10: 1803241004
ISBN-13: 9781803241005
Book Description
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
By finelybook
DuckDB is a fast in-process analytical database. Its ease of use, versatile feature set, and powerful analytical capabilities make DuckDB a valuable addition to the data practitioner’s toolkit.
Getting Started with DuckDB offers a practical overview of DuckDB’s fundamentals and guidance for effectively using its powerful capabilities. Through extensive hands-on examples, you’ll learn how to use DuckDB to load, transform, and query a variety of data sources and formats, including CSV, JSON, and Parquet files, semi-structured data, remotely-hosted files, and external databases. You’ll also find out how to leverage DuckDB’s performance optimizations and friendly SQL enhancements. You’ll explore how to use DuckDB’s extensions for specialized applications, such as geospatial analysis and text search over document collections. In addition to working through examples in SQL, Python, and R, you’ll also dive into using DuckDB for analyzing public datasets and discover the wider ecosystem of open-source tools and cloud services that supercharge DuckDB-powered workflows and applications.
Whether you’re a seasoned data practitioner or new to working with analytical data, this book will rapidly get you up to speed with DuckDB’s versatile and powerful capabilities, enabling you to apply them in your analytical workflows and projects.
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
- An Introduction to DuckDB
- Loading Data into DuckDB
- Data Manipulation with DuckDB
- DuckDB Operations and Performance
- DuckDB Extensions
- Semi-Structured Data Manipulation
- Setting up the DuckDB Python Client
- Exploring DuckDB’s Python API
- Exploring DuckDB’s R API
- Using DuckDB Effectively
- Hands-On Exploratory Data Analysis with DuckDB
- DuckDB – The Wider Pond
Review
“In this excellent book, Simon and Ned have combined the practicalities of what you need to know now with a wealth of hints and tips for getting the most out of DuckDB. Tips for doing more, much more easily.
The chapter on DuckDB’s extensions is particularly fruitful if you’re looking to perform minor data miracles. You will learn how to pull raw data off S3, chew through it in seconds, and export it into an Excel spreadsheet, instantly becoming the favourite data guru of an entire marketing department.”
Kris Jenkins
Host of Developer Voices and Co-Founder of BullionVault
About the Author
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.
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