🔗 Introducing DuckDB

The DuckDB database provides a seamless way to handle large datasets in Python with Online Analytical Processing (OLAP) optimization. You can create databases, verify data imports, and perform efficient data queries using both SQL and DuckDB’s Python API.

By the end of this tutorial, you’ll understand that:

• You can create a DuckDB database by reading data from files like Parquet, CSV, or JSON and saving it to a table.
• You query a DuckDB database using standard SQL syntax within Python by executing queries through a DuckDB connection object.
• You can also use DuckDB’s Python API, which uses method chaining for an object-oriented approach to database queries.
• Concurrent access in DuckDB allows multiple reads but restricts concurrent writes to ensure data integrity.
• DuckDB integrates with pandas and Polars by converting query results into DataFrames using the .df() or .pl() methods.

The tutorial will equip you with the practical knowledge necessary to get started with DuckDB, including its Online Analytical Processing (OLAP) features, which enable fast access to data through query optimization and buffering.

Ideally, you should already have a basic understanding of SQL, particularly how its SELECT keyword can be used to read data from a relational database. However, the SQL language is very user-friendly, and the examples used here are self-explanatory.

Now, it’s time for you to start learning why there’s a growing buzz surrounding DuckDB.