Parquet

Definition

Parquet is a columnar storage file format optimized for use with big data processing frameworks like Apache Hadoop. Its design allows for efficient data compression and encoding schemes, making it ideal for analytics and query performance. By storing data in a columnar manner, Parquet enables faster retrieval of specific columns and reduces the amount of I/O operations needed, which is crucial in environments handling large datasets.

5 Must Know Facts For Your Next Test

1. Parquet was developed by Cloudera and Twitter and is designed to work with various data processing frameworks, providing interoperability across tools in the Hadoop ecosystem.
2. The columnar storage format of Parquet allows for better compression than row-oriented formats because similar types of data are stored together, which leads to more efficient storage utilization.
3. Parquet supports complex nested data structures, enabling it to handle a variety of data types, including arrays and maps, which is valuable for modern applications.
4. Using Parquet can significantly reduce the amount of disk space needed compared to traditional formats like CSV or JSON, making it cost-effective for storing large volumes of data.
5. Parquet files can be read by multiple engines like Apache Spark, Apache Drill, and Apache Impala, making it a versatile choice for different big data processing tasks.

Review Questions

• How does the columnar storage feature of Parquet enhance performance in big data environments?
  • The columnar storage feature of Parquet enhances performance by allowing specific columns to be accessed directly without needing to read the entire dataset. This reduces the I/O operations significantly since only the relevant data is retrieved during queries. The ability to compress similar types of data together also contributes to faster read times and reduced disk usage, making it an efficient format for analytics.
• In what ways does Parquet’s support for nested data structures improve its usability compared to traditional flat file formats?
  • Parquet’s support for nested data structures allows it to store complex data types like arrays and maps, which traditional flat file formats struggle to accommodate. This flexibility enables users to model real-world entities more accurately and retrieve them efficiently during analysis. As modern applications often require handling such complex datasets, Parquet's capability provides a significant advantage in structuring data for effective querying.
• Evaluate the impact of using Parquet on overall data processing costs in an organization leveraging big data solutions.
  • Using Parquet can have a substantial impact on overall data processing costs by reducing storage requirements due to its efficient columnar format and compression capabilities. This leads to lower expenses related to cloud storage or on-premise servers. Additionally, faster query performance translates to reduced processing time and resource consumption, allowing organizations to derive insights quicker and potentially leading to cost savings in computational power while improving decision-making efficiency.

Related terms

Hadoop:

An open-source framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.

Columnar Storage: A storage method that stores data tables by columns rather than rows, facilitating faster access and better compression for analytical queries.

Hive: A data warehouse infrastructure built on top of Hadoop that provides data summarization, query, and analysis capabilities using a SQL-like language.