Parquet explained

Parquet: A Powerful Columnar Storage Format for AI/ML and Data Science

Table of contents

In the world of AI/ML and data science, handling and processing large volumes of data efficiently is crucial. One of the key components in data processing is the choice of storage format. Parquet has emerged as a popular columnar storage format that offers significant advantages for these domains. In this article, we will dive deep into Parquet, exploring its origins, features, use cases, industry relevance, and best practices.

What is Parquet?

Parquet is an open-source columnar storage format designed for big data processing frameworks like Apache Hadoop, Apache Spark, and Apache Arrow. It aims to provide efficient storage and processing of structured and semi-structured data. Unlike row-based storage formats such as CSV or JSON, Parquet organizes data by columns, enabling advanced optimizations for analytical workloads.

Columnar Storage

Traditional row-based storage formats store data in a row-wise manner, where all the values for a single row are stored together. On the other hand, columnar storage formats store data in a column-wise manner, where all the values for a single column are grouped together. This organization allows for better compression, as similar data types and values are stored contiguously.

Advantages of Parquet

Parquet offers several advantages that make it an ideal choice for AI/ML and data science applications:

1. Columnar Compression: Parquet's columnar storage allows for efficient compression techniques, such as run-length encoding, dictionary encoding, and bit-packing. These compression techniques can significantly reduce storage requirements, resulting in cost savings.

2. Predicate Pushdown: Parquet supports predicate pushdown, which means that query filters can be applied at the storage layer itself. This reduces the amount of data that needs to be read from disk, improving query performance.

3. Schema Evolution: Parquet supports schema evolution, allowing for easy addition, deletion, or modification of columns without rewriting the entire dataset. This flexibility is valuable in scenarios where data schemas evolve over time.

4. Column Projection: Parquet enables column projection, which means that only the required columns are read from disk during query execution. This reduces I/O overhead and improves query performance.

5. Type-aware Encoding: Parquet supports type-aware encoding, where different encoding techniques are applied based on the data type of each column. This results in efficient storage and improved query performance.

History and Background

Parquet was originally developed by Cloudera and Twitter as an open-source project in 2013. It was designed to address the limitations of existing storage formats, such as row-based formats like CSV or Avro, for big data analytics workloads. Parquet was later adopted by the Apache Software Foundation and became an Apache top-level project.

The design principles of Parquet were influenced by Google's Dremel paper, which introduced the concept of columnar storage and advanced query execution techniques. Parquet aimed to provide similar benefits while being compatible with the Apache Hadoop ecosystem.

Use Cases

Parquet has gained widespread adoption in AI/ML and data science applications due to its performance and compatibility with popular Big Data processing frameworks. Some common use cases of Parquet include:

1. Data Warehousing: Parquet is widely used in data warehousing scenarios, where large volumes of structured and semi-structured data need to be stored and analyzed efficiently. Its columnar storage and compression capabilities make it a suitable choice for data warehousing platforms.

2. Data Lakes: Parquet is often used as a storage format in data lakes, which are centralized repositories of raw and processed data. Data lakes leverage Parquet's performance optimizations to enable fast querying and analysis on diverse datasets.

3. Machine Learning: Parquet is well-suited for machine learning workflows, where large datasets need to be processed and transformed. Its efficient storage and columnar compression enable faster data access and reduce the time required for feature Engineering and model training.

4. Log Analytics: Parquet is commonly used in log analytics platforms, where large volumes of log data are stored and analyzed. Its ability to handle semi-structured data and perform predicate pushdown makes it an excellent choice for log analytics workloads.

Industry Relevance and Standards

Parquet has gained significant traction in the industry, becoming a de facto standard for columnar storage in the big data ecosystem. It is supported by major big data processing frameworks, including Apache Hadoop, Apache Spark, and Apache Arrow. Parquet's adoption by these frameworks ensures compatibility and seamless integration into existing data processing Pipelines.

Parquet is also supported by various cloud platforms, such as Amazon S3, Google Cloud Storage, and Microsoft Azure Data Lake Storage. This support further validates Parquet's industry relevance and highlights its importance in cloud-based data processing and analytics.

Best Practices and Recommendations

When working with Parquet, it is important to follow best practices to ensure optimal performance and compatibility. Here are some recommendations:

1. Schema Design: Design the Parquet schema carefully, considering the data types, encoding techniques, and compression options for each column. Choosing appropriate data types and encodings can significantly impact storage efficiency and query performance.

2. Partitioning: Utilize partitioning in Parquet to improve query performance. Partitioning involves organizing data into separate directories based on specific column values. This technique enables efficient filtering and reduces the amount of data that needs to be scanned during query execution.

3. Compression Options: Experiment with different compression algorithms and settings to find the optimal balance between storage size and query performance. Parquet supports various compression codecs such as Snappy, Gzip, and LZO.

4. Predicate Pushdown: Leverage predicate pushdown capabilities in Parquet-aware query engines to minimize data scanning. Pushing down filters to Parquet storage reduces the amount of data read from disk, resulting in faster query execution.

5. Data Serialization Libraries: Use efficient data serialization libraries like Apache Avro or Apache Arrow when writing Parquet files. These libraries provide optimized serialization and deserialization, improving overall performance.

Conclusion

Parquet has emerged as a powerful columnar storage format for AI/ML and data science applications. Its columnar storage, compression techniques, and compatibility with popular Big Data processing frameworks make it a suitable choice for storing and analyzing large datasets. By following best practices and leveraging Parquet's features, data scientists and AI/ML practitioners can unlock the full potential of their analytical workloads.

Parquet's industry adoption and support from major cloud platforms further solidify its relevance and importance in the data processing landscape. As AI/ML and data science continue to evolve, Parquet is expected to play a vital role in enabling efficient data storage, processing, and analysis.

References: - Apache Parquet Official Website - Parquet GitHub Repository - Parquet: Columnar Storage for the Apache Hadoop Ecosystem - Dremel: Interactive Analysis of Web-Scale Datasets