Introduction

Start here to learn what DuckDB is, why it shines for analytics, and how this course is structured.

DuckDB for Analytics — Introduction

DuckDB is an in-process, columnar SQL database that runs anywhere you can run your app. It excels at fast analytical queries on local files like Parquet and CSV, without needing a server, a cluster, or cloud credits.

New to terms like Parquet, CTE, or materialize? See the Beginner glossary.

This course is part of the Databases learning pathway, focused on practical analytics: querying files, creating small analytical datasets, and integrating with Python.

What you’ll learn

• Install and run DuckDB (CLI and Python)
• Query CSV/Parquet directly with SQL
• Create and persist a local analytics database
• Work with DuckDB from Python and Pandas/Polars
• Read data from data lakes (local, HTTP/S3) and tune performance
• Use extensions and best practices for analytical workloads

Who this is for

• Makers, analysts, and developers who want fast local analytics
• Beginners to databases and data lakes who prefer practical examples

Prerequisites

• Basic command line and Python familiarity
• macOS, Linux, or Windows
• Optional: Python 3.10+ and VS Code for notebook-style work

Why DuckDB

• No server: embed it in your scripts and apps
• Fast analytical SQL: vectorized, columnar execution
• File-first: query Parquet/CSV directly, no ETL required
• Great with Python: query DataFrames and return results as DataFrames
• Portable: a single .duckdb file you can version and ship

When to choose it:

• You need quick, local analytics (OLAP) on files
• You want to prototype or build repeatable data workflows without infra
• You want SQL over DataFrames, or to complement Pandas/Polars

Quick start (macOS)

• CLI: brew install duckdb
• Python: python -m pip install duckdb

Try the CLI:

duckdb

Then run a tiny analytics query directly on a CSV from the web:

-- Create a table from a remote CSV
CREATE TABLE tips AS
SELECT * FROM read_csv_auto('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv');

-- Simple aggregate
SELECT day, ROUND(SUM(total_bill), 2) AS revenue, COUNT(*) AS orders
FROM tips
GROUP BY day
ORDER BY revenue DESC;

Exit with .quit.

Or from Python:

import duckdb, pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv')

# Query a DataFrame directly
result = duckdb.query("""
  SELECT day, AVG(total_bill) AS avg_bill
  FROM df
  GROUP BY day
  ORDER BY avg_bill DESC
""").df()

print(result)

What we’ll build in this course

• A small, local analytics workspace using DuckDB
• Reproducible queries over CSV/Parquet (local and remote)
• A persisted .duckdb database and lightweight “data mart”
• Python integrations for analysis and notebooks (like Jupyter notebooks)
• Practical performance tips (statistics, Parquet predicate pushdown, and more)

Course structure

• 00 intro: what DuckDB is and why use it
• 01 installing: CLI, Python, VS Code setup
• 02 basic queries: selecting, filtering, aggregations, joins
• 03 advanced features: views, CTEs, extensions, persistence
• 04 python integration: DataFrames, results, notebooks
• 05 data analysis: end-to-end mini analysis
• 06 data lakes: reading from local/remote object storage
• 07 parquet: formats, partitioning, predicates
• 08 performance tuning: tips for speed and memory
• 09 conclusion: recap and patterns
• 10 additional resources: docs, tools, datasets

Next up

If you want a one-page setup and first queries, see the Quickstart. Otherwise, head to 01 installing to set up DuckDB and the Python environment.

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