Spark
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
An open-source engine that processes large datasets across a cluster of machines, holding data in memory to run analytics and machine learning at scale.
What is Spark?
Apache Spark is an open-source engine that distributes data processing across a cluster of machines and keeps working data in memory rather than on disk.
That in-memory design is why it displaced the older MapReduce approach for most big data work: jobs that reread data many times, like training a machine learning model, run far faster when the data stays in RAM.
How Spark Works
A Spark job starts in a driver program, which breaks the work into tasks and hands them to executor processes spread across the cluster. Each executor computes on its share of the data; if a machine dies mid-job, Spark recomputes the lost pieces instead of restarting everything.
Developers write against high-level APIs in Python, Scala, Java, or R, and the same engine serves several workloads. Spark SQL handles queries over structured data, Structured Streaming processes live data feeds, and MLlib provides machine learning algorithms that train across the whole cluster.
MLlib is the tie-in that matters for AI work: when the training data no longer fits on one machine, its implementations of regression, clustering, and recommendation algorithms are the standard way to keep training without sampling the data down.
Example of Spark
An online retailer wants product recommendations from a year of clickstream logs – far too much data for a single machine.
The pipeline runs on Spark end to end. First, executors read the raw logs in parallel from distributed storage. Second, Spark SQL cleans the events and joins them against the product catalog. Third, MLlib trains a collaborative filtering model on the full dataset, each executor computing on its partition.
The finished model scores which products each customer is likely to want next. The job that would have taken a single machine days, if it ran at all, completes across the cluster in hours.
Related AI terms: Big Data · Machine Learning · Data Analytics · Ray · Pipeline
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Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About