Parquet

What is Parquet?

Parquet is an open-source columnar storage format for efficient and high-performance data storage and processing. It is used in a wide range of big data applications, including Apache Hadoop and Apache Spark.

Parquet is a flexible and efficient way to store and process data in a columnar format. It allows for efficient compression and encoding of data, which can greatly reduce storage requirements and improve query performance.

How is Parquet used?

Parquet is used in a wide range of applications, including data warehousing, business intelligence, and machine learning. It is particularly useful for applications that require large amounts of data processing, as it allows developers to easily scale their applications to handle massive workloads:

  1. Data warehousing: Parquet is commonly used in data warehousing to store large amounts of structured data. It allows for efficient compression and encoding of data, which can greatly reduce storage requirements and improve query performance. Parquet is particularly useful for applications that require ad hoc queries and complex analytics.

  2. Business intelligence: Parquet is also used in business intelligence applications to store and analyze large amounts of data. It provides a flexible and efficient way to store data, which can improve query performance and reduce storage requirements. Parquet is particularly useful for applications that require complex reporting and data visualization.

  3. Machine learning: Parquet is used in machine learning applications to store and process large amounts of data. It allows for efficient compression and encoding of data, which can greatly reduce storage requirements and improve training performance. Parquet is particularly useful for applications that require training on large datasets or distributed training.

  4. Real-time data processing: Parquet is also used in real-time data processing applications to store and process large amounts of data in near real-time. It provides a flexible and efficient way to store and process data, which can improve query performance and reduce storage requirements. Parquet is particularly useful for applications that require low latency and high throughput.

What is the difference between Parquet and CSV?

Parquet:

  • Stores data in a columnar format, allowing for more efficient compression and encoding of data

  • Supports a wider range of data types, including numeric, boolean, and date/time data types

  • Designed to work well with distributed processing frameworks like Apache Hadoop and Apache Spark, making it easier to scale and process large datasets

CSV:

  • Stores data in a row-based format, with each row representing a single record and each column representing a field or attribute of that record

  • Often limited to storing data in text format, and converting between different data types can be time-consuming and error-prone

  • Can be more difficult to work with in distributed environments, as they require reading and processing entire files at once.

Some benefits of using Parquet include:

  • Efficient storage: Parquet allows for efficient compression and encoding of data, which can greatly reduce storage requirements and improve query performance.

  • High performance: Parquet is designed for high-performance data processing, making it perfect for applications that require large amounts of data processing.

  • Columnar storage: Parquet stores data in a columnar format, which allows for efficient querying and processing of specific columns.

  • Cross-platform support: Parquet is supported by a wide range of big data frameworks, including Apache Hadoop, Apache Spark, and Apache Hive.

If you want to learn more about Parquet, check out the following resources: