Parquet File Format

Parquet is an open-source columnar storage format for Hadoop. It is designed to bring efficient columnar storage of data compared to row-based files like CSV. By providing columnar compression and encoding schemes, Parquet significantly reduces the disk I/O, storage space, and enhances the processing speed of data querying tasks.

History

Parquet was created to make the advantages of compressed, efficient columnar data representation available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language. It was first released by Apache in 2013.

Functionality and Features

Parquet is built to support complex nested data structures. It uses the record shredding and assembly algorithm described in the Dremel paper to represent nested records. Parquet also provides flexible compression options and efficient encoding schemes.

Architecture

Parquet organizes data by column, enabling better compression, and is optimized for queries via complex nested data structures. Parquet uses various compression techniques to shrink disk space including dictionary encoding, run-length encoding and bit packing. The layout of Parquet data files is optimized for queries that process large volumes of data, making it a top choice for performance in a Data Lake environment.

Benefits and Use Cases

Parquet's columnar storage brings efficiency compared to row-based files like CSV. When querying, the columnar storage allows you to skip non-relevant data very quickly. As a result, the I/O for the query will be minimized, which leads to faster execution times. This makes Parquet an excellent format for large scale data analytics.

Challenges and Limitations

The main limitation of Parquet is that it does not support real-time data processing. It is best used for batch jobs over large sets of appended-only data (like logs).

Integration with Data Lakehouse

Parquet is a popular format for many Data Lake solutions - it's also compatible with most of the data processing frameworks in the Hadoop environment. Parquet also provides a binary data format option that enables users to avoid serialization and deserialization cost, making it suitable for a data lakehouse setup.

Security Aspects

In terms of security, Parquet provides encryption options for sensitive data. It is also built for interoperability between programming languages, allowing it to easily integrate with various data processing frameworks in a secure manner.

Performance

Parquet is optimized for the write-once-read-many-times paradigm. Because it is columnar, it can enable very fast query times - assuming correct storage and querying methods.

FAQs

Is Parquet better than CSV? Yes, Parquet saves storage, and also improves query times. It is optimized for the complex nested data structures.

What type of data does Parquet best support? It best supports large data files because it reads data by column and not by row, optimizing for data analytics.

Is Parquet suitable for real-time processing? No, Parquet is not ideal for real-time data processing. It is best used for batch processing over large datasets.

Glossary

Columnar Storage: Data storage method optimized for fast retrieval of columns of data.

Data Lakehouse: A new type of platform that combines the best elements of data warehouses and data lakes.

Dictionary Encoding: A type of lossless data compression in which a dictionary is used to replace raw data with numerical representations.

Hadoop: An open-source, Java-based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment.

Dremel: A scalable, interactive ad-hoc query system for analysis of read-only nested data.