Columnar storage means similar data (all values from one column) is physically stored together, instead of storing each full row together as in CSV. That’s what lets analytical engines read only the columns a query actually needs.
Think of a Parquet file as a very well-organized goods warehouse. The key idea is simple: a traditional row-based file stores everything about one item together. Parquet stores similar attributes together in columns. That makes Parquet especially fast when you only need a few columns from a very large dataset.
A normal row-oriented format such as CSV roughly stores data with all product information on one line. Parquet organizes differently by placing similar information together in separate sections, called columnar storage.
When a manager asks for average pricing, systems using CSV must read all columns including Product_ID, Product_Name, Category, Quantity, Price, and Warehouse. Parquet can walk directly to the Price column, resulting in less data read, less network traffic, lower CPU usage, faster queries, and reduced cloud costs.
Parquet’s internal structure includes row groups (large warehouse sections) and column chunks (storage areas within row groups). The footer metadata contains schema information, row-group locations, minimum/maximum values, null counts, and encoding details.
A particularly powerful feature is metadata-driven data skipping, or predicate pushdown. When metadata indicates a row group’s maximum price is $900, queries seeking prices above $1,500 can skip that entire section without reading it.
Compression is significantly better in Parquet because similar data sits together. Dictionary encoding, run-length encoding, bit packing, and compression techniques allow huge datasets to shrink substantially—sometimes from 10 GB to 3 GB.
For analytics involving billions of transactions with multiple columns, Parquet’s ability to read only necessary columns provides major advantages. A query needing just country and amount data skips the other nine columns entirely.
Parquet is a file format, not a database. Spark, Databricks, Trino, Snowflake, Fabric, and other engines read Parquet files stored in cloud locations like ADLS Gen2.
Delta Lake adds a transaction layer around Parquet, functioning as a warehouse management system with inventory history, transaction logs, change tracking, and ACID guarantees. While Parquet stores actual data, the Delta Log tracks what happened to that data.
The critical distinction is: Parquet stores data by column, enabling analytical engines to read only necessary data, compress efficiently, and skip sections matching query criteria. In Azure Databricks architecture, Parquet represents the efficient storage format while Delta Lake makes those files behave like reliable enterprise tables.