Data storage is a crucial aspect of any data-driven application or system. When dealing with large volumes of data, efficiency and performance become paramount. The Parquet file format has emerged as a popular choice for storing and managing structured data efficiently. In this comprehensive guide, we will explore the Parquet file format, its benefits, internals, and how it can be a game-changer in the world of data storage and processing.

What is Parquet?

Apache Parquet is an open-source columnar storage file format that was initially developed as a part of the Hadoop ecosystem. However, it has gained widespread adoption across various big data and analytics platforms, including Apache Spark, Apache Hive, and Apache Impala, among others.

Parquet files are designed to store and manage structured data efficiently. Unlike traditional row-based storage formats like CSV or JSON, Parquet stores data in a columnar fashion, which brings several advantages.

Benefits of Parquet

1. Columnar Storage

Parquet stores data in columns rather than rows, which means it groups similar data together. This columnar storage provides several benefits:

  • Compression Efficiency: Data in the same column often shares similar characteristics, making it highly compressible. This leads to reduced storage space requirements and faster data retrieval.
  • Column Pruning: When querying data, Parquet allows for column pruning, which means that only the columns required for a query are read from the storage. This minimizes data transfer and improves query performance.

2. Schema Evolution

Parquet files come with an embedded schema that includes data types and column names. This schema evolution feature allows you to add, remove, or modify columns without affecting the existing data. This flexibility is invaluable in evolving data models over time.

3. High Performance

Parquet’s columnar storage and compression efficiency translate into superior query performance. Reading only the necessary columns and utilizing modern hardware optimizations result in faster analytics and reduced I/O operations.

4. Cross-Platform Compatibility

Parquet is not tied to any specific programming language or platform. It has libraries and support for multiple programming languages, including Java, Python, C++, and more. This makes it a versatile choice for data storage and exchange between different systems.

5. Data Serialization

Parquet supports multiple serialization libraries like Avro, Thrift, and Protocol Buffers, allowing you to choose the best-fit serialization method for your data.

Parquet Internals:

To truly understand the power of Parquet, it’s essential to delve into its internals:


Each Parquet file contains metadata that describes the schema, compression algorithms used, and other essential information. This metadata enables quick schema discovery without the need to scan the entire file.

Row Groups

Parquet divides the data into row groups, each of which is a self-contained unit. Row groups are a critical feature for parallelism in processing and reading data. They allow for parallel execution of queries and reading data in smaller, manageable chunks.


Parquet employs various compression algorithms, such as Snappy, Gzip, and LZO, to reduce storage space and improve read performance. The choice of compression algorithm is configurable, allowing you to optimize for your specific use case.


Within each column, Parquet uses encodings like Run Length Encoding (RLE) and Delta Encoding to further enhance compression and efficiency.

Data Pages

Data in Parquet files is divided into data pages, which are the smallest unit of data compression. These pages can be individually read and decompressed, reducing I/O overhead.

Working with Parquet:

Working with Parquet files is relatively straightforward, thanks to the wide range of libraries and tools available. Here are some common operations:

Reading Parquet Files

You can read Parquet files using libraries like Apache Arrow, Apache Parquet, or specific language bindings such as PyArrow (for Python) and Arrow Rust (for Rust).

import pyarrow.parquet as pq

table = pq.read_table('data.parquet')

Writing Parquet Files

To create Parquet files, you can use the same libraries to write data to the columnar format.

import pyarrow as pa

table = pa.table({'column1': [1, 2, 3], 'column2': ['a', 'b', 'c']})
pq.write_table(table, 'output.parquet')

Schema Evolution

Modifying the schema of existing Parquet files is straightforward. You can add new columns or alter existing ones while maintaining backward compatibility.

import pyarrow as pa

# Open an existing Parquet file
table = pq.read_table('data.parquet')

# Create a new schema with additional column
new_schema = pa.schema([
    ('column1', pa.int32()),
    ('column2', pa.string()),
    ('new_column', pa.float64())])

# Add data to the new column
new_data = pa.array([1.1, 2.2, 3.3])

# Append the new schema and data to the existing table
table = table.add_column(2, 'new_column', new_data)

# Write the updated table to a new Parquet file
pq.write_table(table, 'updated_data.parquet')

The Parquet file format has emerged as a powerful solution for efficiently storing and managing structured data. Its columnar storage, schema evolution capabilities, and compatibility with various platforms make it a go-to choice for organizations dealing with large datasets. Understanding the internals of Parquet and how to work with it is essential for optimizing data storage and processing tasks. As the world of big data continues to grow, Parquet’s role in data storage and analytics is likely to become even more significant.

By Abhishek K.

Author is a Architect by profession. This blog is to share his experience and give back to the community what he learned throughout his career.