Performing read-write operations to a Parquet file using Spark in Synapse

Apache Parquet is a columnar file format that is supported by many big data processing systems and is the most efficient file format for storing data. Most of the Hadoop and big data world uses Parquet to a large extent. The advantage is the efficient data compression support, which enhances the performance of complex data.

Spark supports both reading and writing Parquet files because it reduces the underlying data storage. Since it occupies less storage, it actually reduces I/O operations and consumes less memory.

In this section, we will learn about reading Parquet files and writing to Parquet files. Reading and writing to a Parquet file with PySpark code is straightforward.

Getting ready

We will be using a public dataset for our scenario. This dataset will consist of New York yellow taxi trip data; this includes attributes such as trip distances, itemized fares, rate types, payment types, pick...