Apache Parquet

What is Apache Parquet?

Apache Parquet is an open-source file format for Hadoop. Parquet stores nested data structures in a flat columnar format. Compared to traditional row-based formats, Parquet is more efficient in terms of storage and processing time. It is engineered with complex structures, schemas, and attributes that work together to optimize performance in large-scale data processing systems.


Apache Parquet was created to offer a more efficient and comprehensive storage solution to the Hadoop ecosystem. The format was developed by Twitter and Cloudera to deal with the limitations of storage efficiency and query performance of row-based files like CSV and TSV. It became an Apache Software Foundation top-level project in 2015.

Functionality and Features

Parquet is designed to bring efficiency to big data processing. It offers various features:

  • Columnar storage format: Parquet stores data by columns, which allows it to compress data more efficiently and perform queries faster than row-based storage formats.
  • Schema evolution: It supports changes to the data schema over time.
  • Compression: Parquet is highly efficient at data compression, which reduces the storage space required.
  • Interoperability: Being part of the Hadoop ecosystem, Parquet is compatible with a variety of data processing tools in this ecosystem.

Benefits and Use Cases

Parquet proves beneficial in various scenarios:

  • Analytics: Apache Parquet's columnar storage format makes it a great choice for performing analytics, as it allows more efficient data aggregation and IO.
  • Storage efficiency: Due to its ability to compress data effectively, Parquet is fit for storing massive datasets.
  • Real-time querying: Since only the necessary columns are read during a query, Parquet offers faster data retrieval.

Challenges and Limitations

Despite its advantages, Apache Parquet does have some limitations:

  • Not ideal for small datasets: While Parquet is excellent for big data scenarios, it may underperform on small datasets or for queries requiring the entire row of data.
  • Limited support for row-based operations: Parquet is not well-suited for row-based operations due to its columnar format.

Integration with Data Lakehouse

In a data lakehouse environment, Apache Parquet plays a crucial role as a robust columnar storage format. Its columnar nature ensures optimized query performance, essential for analytics workloads in a data lakehouse. Furthermore, Parquet's compatibility with a wide range of data processing tools contributes to its flexible integration in data lakehouse architecture.

Security Aspects

Apache Parquet itself doesn't provide any explicit security features. However, since it integrates with Hadoop ecosystems, it can benefit from the security measures in place for those systems, such as Kerberos for authentication and Apache Ranger for authorization.


Apache Parquet offers excellent performance in big data processing scenarios. Its columnar format enables efficient data compression and faster queries, which is essential for analytical workloads. Despite this, it might not be the best choice for row-based operations or small datasets.


  • What is the main advantage of Apache Parquet? Its columnar storage format is the main advantage, which allows for efficient data compression and faster query performance, especially in analytical workloads.
  • What types of operations is Parquet not ideal for? Apache Parquet is not well-suited for row-based operations due to its columnar storage format.
  • How does Apache Parquet fit in a data lakehouse environment? Parquet's optimized query performance and compatibility with many data processing tools allow its flexible integration in a data lakehouse environment.
  • Does Apache Parquet provide any security measures? Parquet itself does not offer any explicit security features. However, it can benefit from the security measures of the Hadoop systems it integrates with.

Glossary of Terms

  • Columnar Storage Format: A format that stores data by columns rather than rows. Ideal for analytical and business intelligence queries which typically aggregate over a range of row entries.
  • Schema Evolution: The ability to modify the schema of a database over time in response to changing business requirements without requiring a redesign of your data models.
  • Data Lakehouse: A new kind of data platform that combines the features of data warehouses and data lakes. It allows for structured and unstructured data to coexist with support for a wide range of analytics, from dashboards and reports to machine learning.
  • Kerberos: A network authentication protocol that provides strong authentication for client/server applications by using secret-key cryptography.
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