DRAFT - IN PROGRESS

A Guide for Data scientists, data engineers, data analysts, and Python developers seeking to leverage DuckDB for efficient data processing and analysis.

Table of Contents

Chapter 1: Introduction to DuckDB: The Embedded Analytical Revolution

To introduce DuckDB as a powerful embedded analytical database and explain its relevance in modern data engineering, highlighting its advantages over traditional systems.

• 1.1 The Evolution of Analytical Databases
  • From OLTP to OLAP: A historical perspective
  • Limitations of traditional data warehouses for real-time analytics
  • The rise of embedded analytical databases
• 1.2 DuckDB's Architecture and Design Principles
  • Columnar storage and vectorized query execution
  • Embedded architecture: Bringing the database to the data
  • Zero-copy data access and in-process analytics
  • Concurrency and multi-threading model
• 1.3 DuckDB vs. Traditional Database Systems
  • Comparing DuckDB to PostgreSQL, MySQL, and SQLite
  • Use cases where DuckDB excels: Local analytics, data science, and prototyping
  • Trade-offs and limitations of DuckDB
  • DuckDB's Role in Serverless Architectures
• 1.4 Installation and Setup in Python Environments
  • Installing the duckdb Python package
  • Setting up a basic DuckDB connection
  • Verifying the installation and exploring the DuckDB shell

Chapter 1 Draft

Chapter 2: Core Concepts: Connecting to DuckDB and Executing SQL

To establish a solid understanding of core DuckDB concepts, including the SQL interface, connection management, and basic querying.

• 2.1 Establishing Database Connections
  • Creating in-memory databases
  • Creating and managing persistent databases on disk
  • Choosing the right storage mode for your use case
• 2.2 Executing SQL Queries from Python
  • Using .sql() for query execution (preferred method)
  • When to use .execute() for parameter binding
  • SQL dialect and supported features
  • Understanding DuckDB's SQL extensions
• 2.3 Basic Data Definition Language (DDL)
  • Creating tables and defining schemas
  • Choosing appropriate data types
  • Basic schema design principles
• 2.4 Basic DuckDB API Usage
  • Fetching query results
  • Parameter binding and prepared statements
  • Error handling and exception management

Chapter 2: Core Concepts: Connecting to DuckDB and Executing SQL