Using AVRO Files in PySpark
In the world of big data, efficiently storing and exchanging data is as critical as processing it. With a variety of file formats available, choosing the right one often depends on the specific needs of your data workflows. Among the many formats, AVRO stands out as a compact, schema-driven format designed for data serialization. It has become a popular choice for data engineering tasks, especially in Apache Spark and distributed data systems.
AVRO is widely used for its compact size, schema evolution capabilities, and support for data interoperability. In this article, we’ll explore how AVRO works in the context of PySpark, its benefits, and practical examples to demonstrate how to work with AVRO files effectively.
What Are AVRO Files?
AVRO is a row-based, binary file format designed primarily for data serialization. It is part of the Apache Hadoop ecosystem and supports schema evolution, which makes it particularly useful for storing structured data.
Key Features of AVRO:
- Compact Size:
- AVRO files are stored in binary format, making them smaller compared to text-based formats like JSON or CSV.
2. Schema-Driven:
- Each AVRO file includes a schema (written in JSON) that defines the structure of…
