What Is Apache Spark? The Distributed Data Engine Explained

Apache Spark is an open-source engine for processing large datasets in parallel across a cluster, using in-memory computation for speed.

Apache Spark is the dominant engine for big-data processing. When a dataset is too large for one machine, Spark spreads the work across a cluster, keeps intermediate results in memory, and exposes friendly APIs in Python, Scala, SQL, and more.

What Spark is

Spark is a distributed compute engine. It splits a large dataset into partitions, processes them in parallel across worker nodes, and combines the results. Its in-memory model makes it far faster than older disk-based frameworks for many workloads, especially iterative ones.

How it works

A Spark application is coordinated by a driver that builds a DAG of transformations and hands tasks to executors running on the cluster. Spark is lazy: transformations build a plan, and only an action (like writing output) triggers execution, letting Spark optimize the whole DAG.

What people use it for

Spark powers large-scale ETL, SQL analytics (Spark SQL), streaming (Structured Streaming), and ML (MLlib). Managed offerings like Databricks and EMR run Spark clusters so teams do not have to operate the infrastructure themselves.

Spark and CI

Spark jobs are code, so CI runs them against a small local Spark session on sample data to catch logic errors before they hit a real, expensive cluster.

Testing Spark jobs locally in CI

steps:
  - run: pip install pyspark
  - run: pytest tests/spark   # uses a local[*] SparkSession

Latchkey note

A local Spark session in CI can use real CPU and memory. On Latchkey you can run these tests on larger runners so a local[*] session has room, cache the heavy PySpark and JVM dependencies between runs, and auto-retry transient sample-data fetches.

Key takeaways

Apache Spark processes large datasets in parallel across a cluster using in-memory computation.
A driver builds a DAG of lazy transformations that executors run when an action triggers them.
CI tests Spark jobs against a small local session on sample data before they reach a real cluster.