HDFS Parquet

HDFS Parquet refers to the use of the Apache Parquet file format for storing data in the Hadoop Distributed File System (HDFS). Apache Parquet is a columnar storage file format that is highly optimized for analytics and big data processing. When data is stored in Parquet format within HDFS, it offers several advantages:

1. Columnar Storage: Parquet stores data column-wise rather than row-wise. This allows for more efficient compression and encoding, which results in reduced storage space and improved query performance. Columnar storage is especially beneficial for analytical workloads that often involve reading a subset of columns from a large dataset.

2. Compression: Parquet uses various compression algorithms, such as Snappy and Gzip, to further reduce the storage footprint. Efficient compression not only saves disk space but also speeds up data reading because less data needs to be transferred over the network.

3. Schema Evolution: Parquet supports schema evolution, which means you can add, remove, or modify columns in your data schema without needing to rewrite the entire dataset. This flexibility is essential in data warehouses and data lakes where schemas can change over time.

4. Predominantly Used with Hive and Impala: Parquet is commonly used with query engines like Apache Hive and Apache Impala. These query engines can efficiently read and process Parquet files, making them a preferred choice for interactive querying and analytics.

5. Predominantly Used with Apache Spark: Apache Spark, a popular big data processing framework, also supports Parquet as one of its primary data sources. Spark can efficiently read and write Parquet files, making it an excellent choice for ETL (Extract, Transform, Load) processes and data analysis.

6. Cross-Compatibility: Parquet is designed to be a cross-compatible file format, meaning it can be used with various Hadoop ecosystem tools and other data processing frameworks.

7. Predominantly Used for Analytical Workloads: Parquet is particularly well-suited for analytical workloads where fast querying and analytics on large datasets are essential. It is a popular choice for data warehousing, data lakes, and business intelligence applications.