Columnar Storage in Hadoop: How Apache Parquet Improves Query Performance
As the volume of data grows exponentially, big data technologies have evolved to keep up with the demands of fast, efficient data processing. One of the key challenges in big data analytics is optimising storage formats to improve query performance. In the Hadoop ecosystem, Apache Parquet , a popular columnar storage format, has emerged as a game-changer for performance-oriented data analytics. This blog will explore the benefits of columnar storage in Hadoop and explain how Apache Parquet enhances query performance, particularly in the context of data science applications. What is Columnar Storage? Columnar storage is a method of storing data tables by columns rather than rows. Unlike traditional row-based storage, which saves entire rows of data together, columnar storage saves data from each column sequentially. This structure makes it ideal for analytical queries that access a subset of columns across many rows. When running queries that only need a few columns, columnar stor...