Apache Parquet Datasets

Apache Parquet is a popular columnar storage format for data that is optimized for analytics workloads. It is designed to efficiently store and process large amounts of data, making it well-suited for big data processing frameworks like Apache Hadoop (more about Apache Hadoop HERE) and Apache Spark (more about Apache Spark HERE). Parquet datasets refer to organized collections of data stored in the Parquet format.

Key features of Apache Parquet datasets include:

  • Columnar Storage: Parquet stores data in a columnar format rather than a row-based format. This allows for better compression and encoding of data, as well as more efficient column-level operations, such as filtering and aggregation.
  • Compression: Parquet supports various compression algorithms, such as Snappy, Gzip, and LZO. This reduces storage space and improves data transfer performance.
  • Predicable Schema: Parquet datasets have a well-defined schema that specifies the data types of each column. This schema is stored with the data, enabling schema evolution and compatibility across different versions of the same dataset.
  • Efficient Encoding: Parquet uses efficient encodings, such as dictionary encoding and run-length encoding, to further reduce storage space and improve query performance.
  • Predicate Pushdown: Parquet supports predicate pushdown, which means that data filtering can be pushed down to the storage layer. This reduces the amount of data that needs to be read during query execution.
  • Compatibility: Parquet is designed to work well with a variety of data processing frameworks, including Hadoop, Spark, Impala, Hive, and more.
  • Performance: The columnar storage format of Parquet is particularly well-suited for analytics and reporting workloads. It allows for faster query execution and better performance for analytical queries.
  • Data Types: Parquet supports a wide range of data types, including primitive types (integer, float, boolean, etc.) and complex types (arrays, maps, structs).
  • Partitioning: Parquet datasets can be partitioned based on specific columns, which improves query performance by reducing the amount of data that needs to be scanned.
  • Schema Evolution: Parquet allows for schema evolution, meaning that changes to the schema can be made over time without requiring the entire dataset to be rewritten. This makes it more flexible when dealing with changing data requirements.

In summary, Apache Parquet datasets provide a highly efficient and optimized way to store, manage, and process large volumes of data for analytical purposes. They offer benefits in terms of storage efficiency, query performance, schema evolution, and compatibility with various data processing frameworks.


Apache Parquet Official Documentation: Website: https://parquet.apache.org/documentation/latest/

Apache Parquet GitHub Repository: GitHub: https://github.com/apache/parquet-format

Cloudera Blog – Introducing Parquet: Columnar Storage for Apache Hadoop: Blog Post: https://blog.cloudera.com/introducing-parquet-columnar-storage-for-apache-hadoop/

Databricks Blog – The Apache Parquet Format and Compression: Blog Post: https://databricks.com/blog/2015/04/13/parquet-and-compression.html

Hortonworks Community Connection – Why Apache Parquet? Article: https://community.cloudera.com/t5/Community-Articles/Why-Apache-Parquet/ba-p/247381

AWS Big Data Blog – How to Get the Best Performance from Amazon Athena: Blog Post: https://aws.amazon.com/blogs/big-data/how-to-get-the-best-performance-from-amazon-athena/

DataFlair – Parquet File Format in Hadoop: Tutorial: https://data-flair.training/blogs/parquet-file-format-in-hadoop/

Towards Data Science – Apache Parquet and Spark: Article: https://towardsdatascience.com/apache-parquet-and-spark-sequential-vs-concurrent-data-write-and-read-6cb10c1db3b2

TechTarget – Parquet File Format for Big Data and Data Lakes Explained: Article: https://searchdatamanagement.techtarget.com/definition/Parquet-file-format

Medium – Why Apache Parquet is becoming the de facto standard for big data processing: Article: https://medium.com/snaptravel/why-apache-parquet-is-becoming-the-de-facto-standard-for-big-data-processing-f3ac2f09a9eb

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