What is Parquet and why should you use it?

By Dan McCarey

Over the years, I’ve worked with a myriad of data formats and tools, but Parquet remains one of my favorites for dealing with large, columnar datasets. Whether you're building dashboards, processing large-scale analytics, or simply storing data efficiently, Parquet’s versatility and performance are unmatched.

Why Parquet?

Parquet is a columnar storage file format that excels in read efficiency and compression. Its design is perfect for analytical workloads because it allows you to scan only the columns you need—a huge time saver for querying large datasets. Additionally, its tight integration with popular data tools and frameworks makes it a no-brainer for anyone working in data engineering or analysis.

Enter DuckDB: A Game-Changer for Local Analytics

DuckDB is an in-process SQL OLAP database that’s perfect for analytical queries on the fly. What makes DuckDB shine is its seamless support for Parquet files. You can query Parquet data directly without needing to load it into a separate database. This direct access, combined with DuckDB’s blazing speed, creates an incredibly powerful toolchain for local data analysis.

Using Parquet with DuckDB and Python

I frequently use Python for orchestrating workflows, and integrating DuckDB into my Python scripts has been a delight. Here’s a simple example of how I work with Parquet files:

import duckdb

# Query a Parquet file directly
query = """
    SELECT column1, SUM(column2) as total
    FROM 'data.parquet'
    WHERE column3 = 'some_value'
    GROUP BY column1
"""

# Execute the query
result = duckdb.query(query).to_df()
print(result)

This setup lets me perform SQL-like queries on Parquet files with minimal overhead. Whether I’m exploring a dataset or building a pipeline, the combination of DuckDB and Parquet is efficient and elegant.

Viewing Parquet Files in VSCode

A hidden gem that’s enhanced my Parquet workflow is the Parquet Explorer extension by Adam Viola in Visual Studio Code. This simple yet powerful tool allows you to open and inspect Parquet files directly within your editor. It’s been a lifesaver for quick checks and exploratory analysis. If you’re using VSCode, I highly recommend installing this extension to streamline your work.

Tips for Optimizing Your Parquet Workflow

1. Partition Your Data: If you’re working with large datasets, partitioning can drastically speed up query times by reducing the amount of data scanned.
2. Leverage Compression: Parquet supports multiple compression formats like Snappy and ZSTD. Experiment to find the best balance between speed and file size for your use case.
3. Use DuckDB’s Extensions: DuckDB’s flexibility extends beyond Parquet. Look into its extensions, such as those for reading CSV or JSON files, for even greater functionality.

Parquet and DuckDB has transformed how I work with data. The combination performance, ease of use, and flexibility allow me to tackle complex analytics without being bogged down by infrastructure or tooling constraints.