Apache Spark vs PySpark vs Databricks — What’s the Difference in Data Engineering?

Published 2026-08-18 in PySpark

As I move from Azure Data Factory into the transformation side of Data Engineering, I wanted to first understand three terms that are often mentioned together: Apache Spark. PySpark. Databricks. They are closely related, but they are not the same thing . Understanding the difference makes the overall Data Engineering architecture much easier to visualize. Apache Spark — The Processing Engine Apache Spark is an open-source, distributed data processing engine. Instead of processing a large dataset on a single machine, Spark distributes the workload across multiple machines and processes the data in parallel. In Data Engineering, Spark is commonly used for: The key idea is: Spark provides the distributed processing capability. PySpark — Python API for Spark PySpark is the Python API for Apache Spark. It allows Data Engineers to use Python to work with Spark features such as: DataFrames Transformations Joins Aggregations Window functions Spark SQL Data cleansing The important point is that PySpark code is not simply processing everything on my local machine. The operations are translated into Spark jobs that can execute across the distributed Spark environment. So, a simple way to…

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