Parquet vs. Avro: A Detailed Comparison of Big Data File Formats

Aditi Prakash
August 18, 2023
12 min read
TL;DR: Parquet and Avro are popular file formats for storing large datasets, especially in the Hadoop ecosystem. While Parquet is a columnar storage format, Avro is row-based.

Each format has its strengths and use cases, making the choice between them context-dependent. This guide will unpack the nuances of both formats, compare their advantages, and provide insights on when to use each.

In the world of Big Data, where large-scale datasets are processed to gain valuable insights, the format we use to store and handle data matters. Parquet and Avro are two commonly used data formats.

Parquet is a columnar storage format that is great for data analytics, while Avro is a row-oriented format and system used for data serialization.

In this article, we will delve into Parquet and Avro and their key features. We will provide an in-depth comparison of their main differences. We have also listed the factors that can help you decide which format suits your use case.

Understanding Parquet

Apache Parquet is an open-source column-oriented format designed for efficient data storage and processing in big data environments. 

It was developed as part of the Apache Hadoop ecosystem and is supported by various data processing frameworks like Apache Hive

Parquet’s columnar approach offers several advantages over traditional row-oriented storage formats like CSV or JSON.

Features and Benefits of Parquet

This data format has six main characteristics:

  • Columnar Storage: Parquet stores data in a columnar format, where the values of each column are stored together, allowing for better compression and improved query performance. This design is particularly well-suited for analytics and reporting workloads.
  • Compression: Parquet employs various compression schemes, such as dictionary and run-length encoding, that take advantage of the similarity in values within a column. This results in reduced storage requirements and faster data access.
  • Predicate Pushdown: Parquet files support predicate pushdown, meaning query engines can skip reading irrelevant data blocks when executing a query. This leads to significant performance improvements by reducing the amount of data read from storage.
  • Schema Evolution: This file format enables flexible changes to data schemas, allowing you to add or modify columns without breaking compatibility with existing data. This feature is crucial for managing changing data requirements.
  • Efficient Encoding: The file format uses efficient encoding techniques, like bit-packing, to minimize the storage space required for data. This further contributes to lower storage costs and faster query performance.
  • Compatibility:  Parquet accommodates big data file formats and supports complex nested data structures. This streamlines integrations with big data processing and analytics frameworks.

Use Cases for Parquet

Here are five common use cases for this file format:

  • Big Data Analytics: Parquet is ideal for analytical queries involving aggregations and filtering of large datasets. It accelerates query performance by minimizing the data read from the disk.
  • Data Warehousing: Parquet is used in data warehousing environments where large volumes of structured and semi-structured data must be stored efficiently and queried quickly.
  • ETL Pipelines: Parquet can be used as an intermediate storage format in ETL (Extract, Transform, Load) pipelines, allowing data to be transformed and processed more efficiently before loading it into data warehouses.
  • Log Analytics: Parquet is well-suited for analyzing log files and event data. It enables faster analysis of logs from various sources.
  • Data Archiving: Parquet is a good choice for long-term data archiving, helping organizations store historical data in a cost-effective manner.

Understanding Avro

Apache Avro is an open-source data serialization framework that was also developed as part of the Hadoop framework. The binary row-oriented format provides an efficient way to serialize and deserialize data. 

This makes it suitable for data interchange across different systems and programming languages. Avro’s design focuses on simplicity, performance, and compatibility.

Features and Benefits of Avro

This data format has five main features:

  • Schema-Based Serialization: Avro uses a schema to define the data structure, including data types and field names. In an Avro data file, the schema and the serialized data are stored in the same file, enabling seamless deserialization even when the schema evolves.
  • Compact Binary Format: Avro uses compact binary encoding to reduce the size of the serialized data. This efficiency is particularly valuable when transmitting data over networks or storing it in a space-constrained environment.
  • Schema Evolution: Avro allows for forward and backward compatibility. You can add new fields and mark existing fields as optional with default values, ensuring that newer and older schema versions can interoperate.
  • Dynamic Typing: It supports dynamic typing, which means data can be serialized and deserialized without generating and sharing specific code for each data type. This flexibility simplifies integration between different systems.
  • Interoperability: The data format supports multiple programming languages, making it an excellent choice for systems built with diverse technologies. Avro’s schema is defined in the JSON format, allowing easy readability and manual editing if needed.

Typical Use Cases for Avro

Here are the standard use cases for this file format:

  • Data Interchange: Avro is commonly used for data interchange between applications, services, and languages. Its features ensure compatibility and efficient transmission of data.
  • Streaming Analytics: Avro is used in streaming data pipelines, where data is continuously generated and processed in real time. Its efficient serialization and compatibility with streaming platforms enable rapid analysis of incoming data.
  • Messaging Systems: Avro is well-suited for use in message queues. It is a preferred format for passing messages between distributed systems.
  • Big Data Processing: The file format is often used with big data frameworks like Apache Kafka. It allows data to be ingested, processed and analyzed efficiently across different stages of a data pipeline.
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Avro and Parquet: A Side-by-Side Comparison

Here is a table highlighting the key differences between the file formats:

The main differences between Parquet and Avro

Let’s take a deeper look at some of the aspects where Avro and Parquet differ:

Schema Evolution


Parquet supports schema changes through a concept called “schema evolution rules.” It allows you to add new columns, rename columns, and change the data type while maintaining backward and forward compatibility. This means you can read older Parquet files with a newer schema and vice versa.


Avro also supports schema changes by allowing you to add optional fields and provide default values. This ensures compatibility with older readers when new fields are added. However, deleting or changing the type of an existing field can lead to compatibility issues.



Parquet supports various compression algorithms such as Snappy, Gzip, and LZO. It offers efficient compression on columnar data, leading to reduced storage space and improved query performance. Parquet’s compression is especially effective when dealing with wide tables with many columns.


While Avro also supports compression, with options like Snappy and Deflate, it might not achieve the same level of compression as Parquet for specific data types due to its row-oriented architecture.



Parquet is widely adopted in the Hadoop ecosystem and works well with tools like Apache Hive and Impala. It’s suitable for use cases where performance and compatibility with these tools are important.


Avro is known for its simplicity and is used as a serialization framework. It can be integrated easily with many programming languages and is a good choice when data interchange between systems is a priority.

Read/Write Speed

Parquet’s columnar format is designed for analytical queries, which can lead to improved read performance when querying specific columns. It is well-suited for OLAP (Online Analytical Processing) workloads where aggregations and analysis are common.

Avro’s row-based storage might offer better performance for certain OLTP (Online Transaction Processing) scenarios or use cases that require frequent updates, inserts, or deletes.

Advantages of Parquet

There are three main benefits of using Parquet:

Columnar Storage for Analytical Tasks

In a columnar layout, the values of a single column are stored together, which allows for more efficient data access and processing. 

Analytical queries often involve selecting specific columns and performing operations across those columns, and Parquet’s columnar organization significantly reduces the I/O overhead associated with fetching irrelevant data.

This results in faster query execution times and improved overall performance.

Integration with Big Data Frameworks

Parquet is seamlessly integrated with popular Big Data processing frameworks like Apache Spark. These frameworks are designed to take advantage of Parquet’s columnar format, allowing for optimized execution of queries and transformations. 

Parquet files can be read and processed efficiently, reducing the need for data movement and conversion. This enhances the overall speed and efficiency of data pipelines and analytics workflows.

Space Efficiency

Parquet employs various compression techniques that capitalize on the similarity between values within a column. This leads to significantly reduced storage requirements compared to traditional row-based storage formats. 

The compression techniques used by Parque are especially effective for data with repeating patterns. The combination of columnar storage and efficient data compression contributes to lower storage costs, as less disk space is needed to store the same amount of data.

Advantages of Avro

Avro has three main strengths:

Schema Evolution

Avro’s support for schema evolution is a major advantage. As data formats and requirements change over time, Avro allows you to modify or extend schemas without breaking compatibility with existing data. 

This is crucial for systems that need to evolve without disrupting data pipelines or causing data migration challenges.

Integration with Streaming Platforms

Avro is preferred for integrating with streaming platforms like Apache Kafka due to its compact binary format, schema change capabilities, and support for multiple programming languages.

Avro-serialized data can be efficiently produced, consumed, and processed by various components in a streaming architecture. Avro and Kafka combine to enable real-time data streaming and analytics with minimal overhead.

Efficiency in Row-Wise Operations

While Parquet excels in analytics, Avro’s strength lies in efficient row-wise operations. Avro is well-suited for frequent read-and-write operations, so it’s the best choice when quick data manipulation and updates are required.

Real-world Scenarios: When to use Parquet or Avro

Here are two real-world scenarios showcasing when to use Parquet or Avro:

Scenario 1: Analytics-Intensive Data Warehouse

Format: Parquet

Use Case: A large e-commerce company is building a data warehouse to store and analyze customer behavior, sales transactions, and product performance data.

Reasoning: The company can use Parquet’s compatibility with analytics to quickly perform complex analytics, aggregations, and reporting queries. The columnar layout also enhances query performance.

Scenario 2: Streaming Data Pipeline

Format: Avro

Use Case: A social media platform is building a real-time analytics pipeline to process and analyze user-generated content from different sources.

Reasoning: Avro’s support for schema changes is valuable here, as the platform’s data schema may evolve as new content types are introduced. 

Avro files enable seamless real-time data streaming by integrating with Kafka, while Avro’s dynamic typing accommodates varying content structures.

Making the Choice

Here are five factors to consider when choosing between Avro and Parquet:

  • Data Characteristics: Consider the nature of your data – its volume, structure, and variability.
  • Query Performance: Parquet is the way to go if your use case involves heavy analytics and reporting. Conversely, Avro is more suitable for real-time processing and scenarios with frequent data manipulations.
  • Integration with Ecosystem: Choose the format that integrates well with your existing data processing ecosystem. Parquet is tightly integrated with Hadoop-based frameworks like Apache Spark, while Avro is compatible with streaming platforms.
  • Interoperability: Avro’s multi-language support makes it the best choice if you need to exchange data between systems built using multiple languages.
  • Operational Needs: Consider the operational aspects of your use case. If you need to process large volumes of data in an analytics pipeline, Parquet files are more suitable. For microservices communication or real-time data processing, Avro could be beneficial.

Hybrid Approaches

In some scenarios, a hybrid approach using both Parquet and Avro can offer benefits by leveraging the strengths of each format. 

For example, you could use Avro for initial data ingestion, real-time processing, and data interchange and then convert the relevant portions to Parquet for long-term storage and analytical purposes.

Similarly, you could use Avro for intermediate data representations during data transformations or ETL processes. Once the data is transformed, it could be stored or queried in Parquet.


The choice between Avro and Parquet is a pivotal decision that impacts the efficiency, performance, and flexibility of your data workflows.

Parquet is best for analytical workloads. By storing data in columns and implementing compression schemes, it optimizes performance and minimizes storage costs. On the other hand, Avro is a row-based format that offers a versatile solution for data interchange and real-time processing.

Choosing which big data file format to use depends on understanding your data’s intricacies and your organization’s unique needs.

Check out our blog to learn more about data file formats and integration and get expert tips.

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