Mastering DuckDB: The Hands on Guide
High-Performance SQL with DuckDB Course: Fast, Local, Cloud and Efficient Analytics
Mastering DuckDB: The Hands on Guide free download
High-Performance SQL with DuckDB Course: Fast, Local, Cloud and Efficient Analytics
Description: Mastering DuckDB – Fast, Lightweight Analytics for Modern Data Workflows
DuckDB is a modern, high-performance SQL OLAP database designed for lightning-fast analytics, yet lightweight enough to run entirely within your application, Jupyter notebook, or Python script. With zero setup, zero servers, and near-instant performance, DuckDB is revolutionizing how we interact with local data.
Whether you're a data analyst exploring CSV files, a data engineer building ETL pipelines, or a data scientist running experiments on structured data — DuckDB will save you time, effort, and frustration. This course is your complete guide to mastering DuckDB from scratch, with hands-on exercises, real-world projects, and expert insights.
What You Will Learn
This course is designed to take you from the basics to advanced use cases with DuckDB. Here’s a detailed overview of what you’ll gain:
Introduction to DuckDB
What is DuckDB and why is it gaining popularity?
OLAP vs OLTP – and where DuckDB fits in
How DuckDB compares to SQLite, Pandas, Postgres, and big data tools
Installing DuckDB across platforms (Windows, Mac, Linux)
Using DuckDB via CLI, Python, Jupyter, and SQL
Getting Started with SQL in DuckDB
Creating databases and running queries
Filtering, aggregations, group by, joins, and subqueries
Window functions, CTEs (Common Table Expressions), and date/time functions
Creating views and temporary tables
Using SQL for data exploration, profiling, and reporting
Querying Data Files Directly (No Import Required!)
Querying CSV files directly from disk with SQL
Working with large Parquet files — efficiently and fast
Integrating with Apache Arrow
Using DuckDB to read/write JSON, Excel, and other formats
Combining multiple files into a single virtual table using wildcards
DuckDB + Python Integration
Setting up DuckDB in a Python environment
Running SQL queries on DataFrames without conversion
Writing SQL queries as part of your Python data pipeline
Efficient data transformations without loops or apply()
DuckDB in Jupyter Notebooks
Magic commands for fast SQL in notebooks
Exploring datasets directly in notebooks using SQL + Python together
Ideal workflow for data science projects
Performance, Best Practices & Optimization
Vectorized execution and columnar storage explained
When to use DuckDB vs Pandas or SQL databases
Performance tuning: batching, lazy evaluation, efficient file access
Memory management and handling large datasets
Advanced Capabilities:
Implement DuckLake for enterprise-grade data management
Perform time travel queries for historical analysis
Build robust error handling with TRY expressions
Use lambda functions for complex data transformations
Optimize memory usage and query performance
Enterprise Features:
Set up cloud-based data lakes with AWS S3 integration
Manage data versioning and snapshots
Implement ACID transactions across multiple tables
Monitor and debug using metadata tables
Design scalable data architectures
Who This Course is For
This course is for anyone who works with data and is looking for a better, faster, and simpler tool for analytics:
Data Analysts: Tired of slow CSV loads or limited Excel capabilities? DuckDB will transform the way you explore and analyze data.
Data Scientists: Quickly explore, clean, and process data with SQL directly in your notebook.
Python Developers: Use SQL without a full database backend, right inside your script or application.
Data Engineers: Simplify your pipelines by removing unnecessary database dependencies and using DuckDB to process raw files.
Students/Learners: If you’re new to databases or SQL, this is a great entry point with modern tooling and hands-on projects.
No prior experience with DuckDB is required. Basic familiarity with SQL or Python will be helpful, but we start from the ground up.
Tools & Technologies Covered
DuckDB CLI and embedded usage
DuckDB with Python & Pandas
DuckDB in Jupyter Notebook
CSV, Parquet, Arrow, JSON handling
SQL (basic to advanced)
Optional: Integration with Streamlit for dashboards
Why Learn DuckDB?
DuckDB is rapidly becoming a must-have tool in the modern data stack. Here's why:
Zero Setup: No server, no deployment, just run it and go.
High Performance: Easily handle millions of rows locally.
Embedded & Portable: Run inside notebooks, scripts, or even desktop apps.
SQL-Powered: Ideal for analysts and anyone who loves SQL.
File-Native: Work directly with Parquet, CSV, and more — no database needed.
Open Source & Evolving: Constantly improving and growing with the community.
Learning DuckDB now puts you ahead of the curve, as more companies and teams start to adopt it for local-first, scalable analytics.
What You'll Get
6+ hours of video lectures
Downloadable notebooks and datasets
Hands-on projects and exercises
Quizzes to test your understanding
Certificate of completion
Ready to Master DuckDB?
By the end of this course, you'll be confident using DuckDB in your data projects — whether you're exploring data files, building ETL pipelines, or combining SQL with Python for fast analytics.
Join us and learn how DuckDB can make your data work faster, easier, and more fun.
Let’s dive in and make analytics delightful again — with DuckDB!
