What is Apache Parquet?

Organizations use different file formats to meet other data needs. Many traditional formats organize data in rows, optimizing for simple data transfers and readability.

Parquet takes a fundamentally different approach. It groups similar data types in columns. This columnar structure has helped transform how organizations handle large-scale analytics, enabling superior compression and targeted data access.

For instance, when analyzing customer transactions, a retail database that uses Parquet can access specific columns such as purchase dates and amounts without loading entire customer records. This ability to access specific columns can reduce both processing time and storage costs.

The Parquet format is valuable in 3 key areas:

• Analytics workloads that process complex queries across billions of records.
• Data lakes and data warehouses requiring efficient storage and rapid data retrieval.
• Machine learning (ML) pipelines that analyze specific attributes across large training datasets.

Another reason for Parquet's widespread adoption is its compatibility with distributed systems and data tools, such as Apache Spark, Apache Hive and Apache Hadoop.

Compared to other file formats, Parquet transforms data storage and access through 3 key capabilities:

• Columnar storage format: While traditional file formats store data in rows much like spreadsheet entries, Parquet uses a columnar storage format. It organizes similar types of data together in columns, allowing Parquet to read only the relevant column values. This targeted approach helps reduce query times from hours to minutes while significantly lowering storage costs.

• Schema and metadata management: Every Parquet file includes a self-describing schema—effectively a blueprint of its data structure. This metadata helps with efficient querying and allows data models to evolve as business needs change. For example, a company database that uses Parquet can add new customer attributes without rebuilding existing records.

• Efficient compression: Parquet automatically optimizes data storage through sophisticated compression techniques—meaning that it makes files smaller by removing unnecessary duplicate information and storing data in a more space-efficient format. This intelligent approach to data storage helps ensure optimal storage efficiency and compute performance across different types of workloads.

Apache Parquet systematically transforms raw data into an optimized columnar format, significantly improving both storage efficiency and query performance.

Here's how Parquet processes data:

Apache Parquet can deliver significant advantages for organizations managing large-scale data operations.

Some of its benefits include:

Parquet’s data structure can make running analytical queries much faster. When applications need specific data, they access only relevant columns, reducing query times from hours to minutes. This targeted access makes Parquet valuable for organizations running complex analytics at scale.

Unlike simpler formats, Parquet can efficiently manage nested data structures and arrays common in modern applications. This capability makes it useful for organizations dealing with complex data types, such as JSON-like structures in web analytics or nested arrays in sensor data from Internet of Things (IoT) devices.

Parquet's columnar format fundamentally changes how data is stored and compressed. By grouping similar data types together, Parquet can apply different encoding algorithms to each type of data, achieving better compression ratios than formats such as CSV or JSON.

For example, a dataset containing millions of customer transactions might require terabytes of storage in CSV format but only a fraction of that space when stored as Parquet files.

Modern data architectures often require seamless tool integration, which Parquet delivers through native support for major frameworks. Whether teams use Python with pandas for analysis, Java for application development or Apache Spark for data processing, Parquet can help ensure consistent data access across the enterprise.

Parquet's native integration with Hadoop makes it particularly effective for big data processing. Because Parquet was built for the Hadoop Distributed File System (HDFS), it generally performs better than traditional file formats in Hadoop environments. When using Parquet with Hadoop, organizations can run queries faster and store their data more efficiently, often by using a fraction of the storage space they needed before.

Apache Parquet can address a range of data engineering needs across industries and applications.

Some of its most impactful implementations include:

Organizations building data lakes and data warehouses often choose Parquet as their primary storage format. Its efficient compression and query performance make it ideal for storing large volumes of data while maintaining quick access to business intelligence tools and structured query language (SQL) queries.

For example, a retail chain that uses Parquet to store transaction data can analyze sales patterns across thousands of stores while using less storage space than traditional formats.

Data scientists and analysts who work with frameworks such as Apache Spark or Python's pandas library benefit from Parquet's optimized performance for analytical queries. While formats such as Avro often excel at record-level processing, many find the Parquet file format particularly effective for complex analytics.

For instance, a financial services company might use Parquet to store market data, enabling analysts to process millions of trading events and calculate risk metrics in near real-time.

Modern data pipelines frequently use Parquet as an intermediate or target format during extract, transform and load (ETL) processes. Its compatibility with popular frameworks such as Apache Spark and support for schema evolution makes it valuable for data engineering workflows that need to handle changing data structures.

For example, healthcare organizations might use Parquet to efficiently transform patient records from multiple systems into a unified format, with schema evolution capabilities automatically handling new data fields without disrupting existing processes.

Here's how the Parquet file format compares to other common storage formats:

Traditional formats such as CSV and JSON store data in rows, making them ideal for simple data transfer and human readability. However, when dealing with large-scale analytics, reading Parquet files offers significant advantages.

While a CSV must scan entire rows even when querying single columns, Parquet's columnar storage enables direct access to specific data elements. For instance, analyzing a single column in a petabyte-scale dataset might require reading the entire CSV file, while Parquet would access only the relevant column chunks.

Avro and Parquet serve different use cases in the data ecosystem. Avro's row-based format excels at serialization and streaming scenarios, making it ideal for recording individual events or transactions.

The Parquet file format, by contrast, optimizes for analytical workloads where organizations need to analyze specific columns across millions of records.

For example, an e-commerce platform might use Avro to capture real-time order events but convert this data to Parquet for long-term storage and analysis.

The strength of Apache Parquet lies not only in its format specifications but also in its strong ecosystem of supporting tools and frameworks.

Some of the most significant technologies in the Parquet ecosystem include:

Parquet integrates seamlessly with major data processing frameworks. Apache Spark provides high-performance analytics capabilities, while Hadoop enables distributed processing across large clusters.

Apache Arrow can further enhance this processing ecosystem by enabling fast, efficient data sharing between systems and direct data access—features that speed up performance when using frameworks such as Spark and Hadoop.

Organizations can combine these frameworks with Parquet to build efficient data pipelines ranging from gigabytes to petabytes.

Data engineers can work with Parquet through multiple programming interfaces. Python developers typically use pandas for data manipulation, while Java applications use native Parquet libraries.

Major cloud providers, including Amazon Web Services, Google Cloud Platform, Microsoft Azure and IBM Cloud®, offer native Parquet support.

Parquet is also compatible with cloud-based data warehouses and query engines such as Amazon Athena, Google BigQuery and IBM® Db2® Warehouse.