topic Understanding Parquet File Storage for Large Datasets in Data Engineering

Understanding Parquet File Storage for Large Datasets

gowri_databrick — Mon, 07 Sep 2026 04:23:05 GMT

Hi everyone,

I’m learning about Parquet files and how they are used in Databricks for storing large datasets.

I’m trying to understand how column-based storage works in a practical situation.

For example, suppose an e-commerce company has 500 million order records containing customer details, product information, order dates, and payment information. If an analyst only needs order_date and order_amount to calculate daily sales, how does storing the data in Parquet help the system process this query efficiently?

I’d like to understand how Parquet storage works in this type of real-world scenario.

Thanks!

Re: Understanding Parquet File Storage for Large Datasets

Satyasai — Mon, 07 Sep 2026 04:40:42 GMT

Parquet's columnar architecture optimizes execution through three primary mechanisms: Column Projection, Page-Level Compression, and Metadata Pruning.
Row-Oriented vs. Column-Oriented Layout
To understand why Parquet is fast, compare how a traditional row-oriented format (like CSV or JSON) stores data on disk versus Parquet:
Row-Oriented Layout (CSV/JSON):
[Record 1: Customer, Product, Date, Amount, Payment] [Record 2: Customer, Product, Date, Amount, Payment] ...

Parquet Columnar Layout (Grouped into Row Groups):
[Row Group 1]
├── Column 1 (order_date): [2026-09-01, 2026-09-01, 2026-09-02, ...]
├── Column 2 (order_amount): [120.50, 45.00, 89.99, ...]
├── Column 3 (customer_id): [C102, C994, C102, ...]
└── Column 4 (payment_info): [Visa, Mastercard, Amex, ...]
The 3 Core Performance Advantages in Practice
1. Column Projection (Reading Only What You Need)
In a CSV or JSON file, to read order_date and order_amount for 500 million rows, the query engine must scan every single byte of the file from start to finish—including heavy text fields like customer addresses and payment tokens—just to discard them in memory.
In Parquet, data for each column is stored sequentially in contiguous disk blocks.
• When Databricks executes SELECT order_date, order_amount FROM orders, the query engine performs Column Projection.
• It completely skips the byte locations on disk where customer_id, product_info, and payment_info reside.
• If those unused columns represent 80% of your row width, your total I/O drops by ~80% instantly.
2. Homogeneous Compression Ratios
Compression algorithms (like Snappy or ZSTD) perform best when repeating, similar patterns of data are adjacent to one another.
• Row-oriented files mix numbers, timestamps, long strings, and booleans together in every byte block, making high-ratio compression difficult.
• Parquet columns group identical data types together. An entire column block contains only dates (2026-09-01), or only floating-point amounts.
Parquet applies specialized encodings directly to column data before compressing:
• Dictionary Encoding: Replaces repeating text values with short integer keys.
• Run-Length Encoding (RLE): Stores repeated values efficiently (e.g., storing "2026-09-01 repeated 50,000 times" as a compact tuple).
This drastically reduces the physical footprint on cloud storage, allowing Databricks to pull much smaller files across the network.
3. Row Group Metadata & Data Skipping
A Parquet file is divided into Row Groups (typically containing 100,000 to 1,000,000 rows each). Every Parquet file contains a Footer with rich metadata.
For every Row Group, the Parquet footer records:
• Minimum and maximum values (min_val, max_val) for each column.
• Total null counts and physical byte offsets.
Parquet File Footer Metadata:
Row Group 1: order_date [Min: 2026-01-01, Max: 2026-03-31]
Row Group 2: order_date [Min: 2026-04-01, Max: 2026-06-30]
Row Group 3: order_date [Min: 2026-07-01, Max: 2026-09-30]
If your daily sales query includes a filter like WHERE order_date >= '2026-09-01', the Databricks engine reads the file footer first. It sees that Row Groups 1 and 2 contain no data for September 2026 and skips reading those row groups entirely.
Databricks Delta Lake Layer
While standard Parquet provides these column-level benefits, Delta Lake (the default table format in Databricks) builds an additional transaction log layer (_delta_log) on top of Parquet files. Delta Lake tracks file-level statistics across millions of Parquet files, enabling Databricks to skip entire files without even opening their footers.

Re: Understanding Parquet File Storage for Large Datasets

balajij8 — Mon, 07 Sep 2026 05:27:48 GMT

@gowri_databrick

Parquet's columnar storage organizes data by column rather than by row - all values for order_date are stored together, all order_amount values are stored together and so on. In your commerce scenario with 500 million records, when the analyst queries only order_date and order_amount, Databricks reads only those two columns from disk completely skipping the customer details, product information and payment data. This is fundamentally different from row-based formats like CSV or JSON where the entire row must be read even if you need just two fields. With Parquet, if those two columns represent only 10% of the total data width you are reading 90% less data from storage.

This columnar approach delivers three major benefits on Databricks - dramatically faster queries (reading 100MB instead of 1GB is a massive speed improvement), lower compute costs (less data to process leads to less CPU and memory usage) and reduced storage costs through great compression (values in a single column tend to be similar so order_date values like 2024-01-15 compress much better when stored together than scattered across row records). Query optimizer automatically leverages Parquet's column pruning and predicate pushdown, so filtering on order_date > 2024-01-01 reads only the relevant row groups making analytics on massive datasets fast and cost efficient.