Parquet file is a columnar storage file format that is optimized for use with big data processing frameworks like Apache Hadoop, Apache Spark, etc. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk. **What is Parquet?** - **Columnar Data Storage Format:** Parquet is an open-source file format designed for efficient storage and retrieval of large, complex datasets. Unlike row-oriented formats (like CSV), Parquet stores data column by column. - **Hadoop Ecosystem:** It's part of the Apache Hadoop ecosystem, making it widely compatible with big data tools like Spark, [[Hive]], and Impala. - **Efficiency and Performance:** Parquet's columnar storage and compression techniques make it highly optimized for analytical queries that typically focus on a subset of columns. **Why Use Parquet?** 1. **Fast Query Performance:** - Since Parquet stores data by column, you can fetch only the specific columns needed for a query, without reading the entire dataset. This saves a lot of I/O time, especially with large datasets. 2. **Efficient Compression:** Parquet supports various compression schemes (like Snappy, Gzip), reducing file sizes significantly. This means less storage space and faster data transfer. 3. **Metadata-rich:** Each Parquet file stores metadata about the data types, encoding schemes, and statistics within each column. This helps query engines optimize their execution plans. 4. **Wide Ecosystem Support:** Parquet works seamlessly with popular big data tools like: - Apache Spark - Apache Hive - Apache Impala - AWS Athena - Google BigQuery **Use Cases** - **Data Warehousing and Data Lakes:** Parquet is ideal for storing vast amounts of structured data. - **Analytical Workloads (OLAP):** Parquet's columnar nature makes it ideal for queries that aggregate, filter, and analyze data across a few selected columns. **Example** Imagine a dataset like this in CSV: ``` Name, Age, City, Country Alice, 25, New York, USA Bob, 32, Chicago, USA Eve, 40, London, UK ``` The same data in Parquet would be stored roughly like this: ``` Column: Name Column: Age Column: City Column: Country Alice 25 New York USA Bob 32 Chicago USA Eve 40 London UK ``` # Why is it called Parquet? The name "Parquet" is a reference to the columnar nature of the data storage. In architecture, a "parquet" is a type of floor made from small pieces of wood arranged in a geometric pattern. Similarly, in Parquet files, data is stored in columns, each of which can be processed individually - much like how you might walk on individual parts of a parquet floor. This makes it an ideal format for storing large datasets where you might need to access specific columns frequently but not necessarily all columns at once. Parquet's columnar storage design allows high degree of compression and encoding optimizations. It also supports complex nested structures making it suitable for handling big data use cases. # References ```dataview Table title as Title, authors as Authors where contains(subject, "Parquet" ) sort modified desc, authors, title ```