What is Parquet Format?
Parquet is a columnar storage file format optimized for use with big data processing frameworks. It's utilized in the Hadoop ecosystem, particularly for robust and efficient data analytics. The format is open-source and particularly popular for its compactness, efficiency, and speed.
History
Developed and open-sourced by Twitter and Cloudera in 2013, Parquet was built to be integrated with multiple data processing tools such as Hadoop, Presto, and Apache Arrow.
Functionality and Features
Parquet organizes data in columns, dramatically reducing the I/O needed for specific queries. The file format supports complex nested data structures and utilizes highly efficient compression and encoding schemes. Schema evolution, which allows the format of the data to change over time, is another notable feature of Parquet.
Architecture
Parquet's architecture is based on a columnar format. It writes data by column, not by row, which is ideal for analytical queries that fetch specific columns. Files consist of row groups, columns, and pages, allowing efficient disk I/O and quick data retrieval.
Benefits and Use Cases
The primary advantages of Parquet include efficient storage and faster query execution which can be crucial in a wide range of applications, from business intelligence (BI) systems to data mining and machine learning. Its columnar nature makes it a perfect choice for OLAP-style applications.
Challenges and Limitations
Whilst Parquet offers numerous advantages, it's not ideal for every scenario. It falls short in environments that need to write or read records individually or where row-based storage is more suitable.
Comparisons
Compared to row-based file formats like CSV or TSV, Parquet uses less space and provides faster lookup times. However, writing data can be a slow process, especially compared to formats like Avro, which are designed for efficient data serialization.
Integration with Data Lakehouse
Parquet is a fundamental component in many data lakehouse architectures due to its efficient columnar storage design. It enables faster and more efficient querying, making data more accessible and useful for analytics.
Security Aspects
As a file format, Parquet doesn't inherently include any security features. However, security measures get implemented at the file system or data processing framework level.
Performance
Parquet demonstrates excellent performance in analytic operations, partly due to its columnar format and efficient compression mechanisms. It effectively minimizes disk I/O operations, which contribute significantly to processing times.
FAQs
What is Parquet file format? Parquet is an efficient, columnar storage file format available within the Hadoop ecosystem. It is optimized for complex data processing and analytics tasks.
What are the benefits of Parquet format? Benefits include efficient and performant data storage and analysis, optimized disk space usage, and compatibility with a wide array of data processing tools.
How does Parquet format compare to CSV or JSON? Due to its columnar storage approach, Parquet often uses less space and provides better performance for read-heavy operations than CSV or JSON.
Glossary
Columnar Storage: A data storage method where data is stored by columns, which can provide significant speed and efficiency benefits for read-heavy applications.
Data lakehouse: A hybrid data management platform that combines features of data lakes and data warehouses.
Encoding: The process of converting data from one form to another.
Compression: The reduction in size of data in order to save space or improve data transmission speed.
Kerberos: A network authentication protocol designed to provide strong authentication for client/server applications.
Dremio and Parquet Format
Dremio uses Parquet as its underlying storage format, leveraging its powerful columnar storage and compression capabilities. This means data queried with Dremio can realize the significant benefits of this format, such as fast execution times and reduced storage space.
