Building Your Modern Data Analytics Stack with Python, Parquet, and DuckDB

2025-12-22 · Source: KDnuggets · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, long

Summary

This article details building a modern data analytics stack using Python, Parquet, and DuckDB for analytical workloads. It explains how Parquet, a columnar storage format, offers significant performance and compression benefits over row-based formats like CSV, achieving up to 58.5% storage savings with Snappy compression. DuckDB, an embedded analytical database, is highlighted for its ability to query Parquet files directly without prior data loading, enabling "query in place" capabilities. The author demonstrates this stack with an e-commerce dataset, performing complex joins and aggregations, and showcasing DuckDB's performance, which was approximately 17x faster than pandas for a customer purchase analysis task. The content also covers building reusable, parameterized SQL queries using Common Table Expressions (CTEs) for flexible analysis.

Key takeaway

For data scientists and analysts performing batch analytical workloads on structured data, adopting a Python, Parquet, and DuckDB stack can significantly boost query performance and simplify data management. You should consider this approach for tasks involving aggregations, filtering, and joins on large datasets, especially when data updates are periodic rather than real-time. This stack reduces overhead by eliminating the need for separate database servers and explicit data import steps, allowing you to focus on analysis.

Key insights

Combining Parquet for columnar storage and DuckDB for direct querying offers a fast, efficient data analytics stack.

Principles

• Columnar storage improves query performance and compression.
• Embedded analytical databases simplify data stack management.
• "Query in place" eliminates data loading overhead.

Method

Store data in Parquet files, connect with DuckDB in Python, and execute SQL queries directly on the files. Use pandas for data manipulation and the broader Python ecosystem for ML/visualization.

In practice

• Use `to_parquet()` with `snappy` compression for data storage.
• Connect DuckDB with `duckdb.connect(database=':memory:')` for in-process querying.
• Employ CTEs and dynamic SQL for reusable analytical functions.

Topics

• Python Data Analytics
• Parquet File Format
• DuckDB Database
• Columnar Storage
• Query Performance

Code references

• balapriyac/data-science-tutorials
• google/snappy
• facebook/zstd

Best for: Data Scientist, Data Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by KDnuggets.