What is Parquet?
Apache Parquet is an open-source, columnar storage file format optimized for use with big data processing frameworks. Conceived to be hardware-friendly, it provides efficient, compressed data storage that allows rapid data retrieval.
History
Parquet was first created by Twitter and Cloudera with the aim of creating a columnar storage layout that was efficient and scalable. It was later moved to the Apache Software Foundation where it continues to be actively developed.
Functionality and Features
Parquet's primary feature is its columnar storage method, which allows for efficient reading and writing of data. This is particularly advantageous for analytical queries on large datasets, as only the necessary columns need to be read.
Key features of Parquet include:
- Schema evolution: Parquet supports changes to the schema over time.
- Compression: Parquet provides efficient compression and encoding schemes.
- Language independence: Parquet can be used with any language that supports Hadoop InputFormat.
Architecture
Parquet stores binary data in a column-wise way, where the values of each column are organized consecutively, enabling better compression. It has a modular design and integrates with many data processing tools in the Hadoop ecosystem and beyond.
Benefits and Use Cases
Parquet stands out with its efficient columnar storage, schema evolution capabilities, and compatibility with many data processing tools. This makes it ideal for use cases such as data warehousing, where analytics is an integral part.
Challenges and Limitations
As beneficial as Parquet may be, it's not without limitations. For example, while its columnar nature is beneficial for analytical processes, it might not be optimal for transactional systems that update records frequently.
Integration with Data Lakehouse
In a data lakehouse setup, Parquet plays a pivotal role in structuring and storing data efficiently. Data lakehouses retain the benefits of data lakes, such as schema-on-read, while also providing capabilities of a traditional data warehouse like ACID transactions and data versioning. Parquet's columnar format enables efficient querying and supports these attributes in a data lakehouse environment.
Security Aspects
As part of the Apache Hadoop ecosystem, Parquet can leverage all Hadoop's security features, including Kerberos authentication, data encryption, and access controls.
Performance
Parquet's columnar format greatly improves performance of I/O operations and analytical queries. Additionally, its efficient compression and encoding schemes save storage space and speed up data processing workflows.
FAQs
What is Apache Parquet? Apache Parquet is an open-source, columnar storage file format designed for efficient and compact data storage.
What is the advantage of Parquet's columnar storage format? Parquet's columnar storage format allows for efficient reading and writing of data, which can greatly speed up analytical queries.
How does Apache Parquet integrate with a data lakehouse environment? Parquet plays a key role in a data lakehouse environment by enabling efficient data querying and supporting the schema-on-read, ACID transactions, and data versioning attributes of a data lakehouse.
Glossary
Columnar Storage: A method of storing data by column, rather than by row. This improves performance for analytical queries and data storage.
Data Lakehouse: A technology that combines the best elements of a traditional data warehouse with a data lake to provide an optimized architecture for data analytics.
Schema-on-read: A data handling strategy whereby the schema is applied as you read the data, rather than as it's ingested.
ACID transactions: A set of properties that guarantee data transactions are processed reliably in a database system.
Hadoop Ecosystem: A suite of tools and technologies designed for efficiently storing, processing, managing, and analyzing big data.
