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Getting Started with DuckDB

By : Simon Aubury, Ned Letcher

5 (1)

Getting Started with DuckDB

5 (1)

By: Simon Aubury, Ned Letcher

Overview of this book

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.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

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Conventions used

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Chapter 1: An Introduction to DuckDB

Chapter 1: An Introduction to DuckDB

Technical requirements

What is DuckDB?

Why use DuckDB?

DuckDB deployment options and installation

A short SQL primer

Summary

Chapter 2: Loading Data into DuckDB

Chapter 2: Loading Data into DuckDB

Technical requirements

Loading CSV files

Loading JSON files

Working with Parquet files

Exploring public datasets

Exporting data

Summary

Chapter 3: Data Manipulation with DuckDB

Chapter 3: Data Manipulation with DuckDB

Technical requirements

Data wrangling with DuckDB – cleaning and reshaping data

Data import and manipulation

Altering tables and creating views

Aggregate functions and common table expressions

Joining data from multiple tables

Summary

Chapter 4: DuckDB Operations and Performance

Chapter 4: DuckDB Operations and Performance

Technical requirements

Exploring DuckDB indexes

Optimizing the file read performance of DuckDB

Working with timestamps

Time and window functions

Summary

Chapter 5: DuckDB Extensions

Chapter 5: DuckDB Extensions

Technical requirements

Expanding DuckDB capabilities with extensions

Working with geospatial data

Summary

Chapter 6: Semi-Structured Data Manipulation

Chapter 6: Semi-Structured Data Manipulation

Technical requirements

Exploring nested data types

Working with lists

Working with JSON

Querying JSON data from an API

Summary

Chapter 7: Setting up the DuckDB Python Client

Chapter 7: Setting up the DuckDB Python Client

Technical requirements

Connecting to DuckDB in Python

Summary

Chapter 8: Exploring DuckDB’s Python API

Chapter 8: Exploring DuckDB’s Python API

Technical requirements

Working with the Relational API

Working with the Python DB-API

Integration with Python packages and language features

Summary

Chapter 9: Exploring DuckDB’s R API

Chapter 9: Exploring DuckDB’s R API

Technical requirements

Working with DuckDB using R’s DBI

Registering R objects as virtual tables

Querying DuckDB with dplyr

Summary

Chapter 10: Using DuckDB Effectively

Chapter 10: Using DuckDB Effectively

Technical requirements

Selecting columns effectively

Applying function chaining

Using INSERT effectively

Leveraging positional joins

Using temporal joins with ASOF

Recursive queries and macros

Summary

Chapter 11: Hands-On Exploratory Data Analysis with DuckDB

Chapter 11: Hands-On Exploratory Data Analysis with DuckDB

Technical requirements

Preparing the pedestrian traffic dataset for analysis

Effective data analysis using Jupyter Notebooks

Analyzing pedestrian traffic through Melbourne CBD

Summary

Chapter 12: DuckDB – The Wider Pond

Chapter 12: DuckDB – The Wider Pond

Tools and services for working with DuckDB effectively

DuckDB integration

Alternative DuckDB query interfaces

DuckDB-powered data apps

Keeping up to speed with the DuckDB ecosystem

Chapter and book summary

Index

Index

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Applying function chaining

In this section, we’ll introduce function chaining, an elegant programming style for calling multiple scalar functions sequentially. Function chaining (sometimes termed method chaining) is a familiar technique in object-orientated programming languages – and DuckDB supports this elegant style to increase the readability of code. This will be familiar to users of Python. So, let’s learn how to use function chaining in DuckDB SQL.

We will be using our skiers table from the previous section. Let’s familiarize ourselves with the data available by querying the two name columns:

SELECT skier_first_name, skier_last_name
FROM skiers;

This returns the string fields shown in the following screenshot:

Figure 10.7 – The name fields of the skiers table

Figure 10.7 – The name fields of the skiers table

Let’s say we want to format the names of our skiers by performing the following steps:

Concatenate skier_first_name and skier_last_name...

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