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Apache Spark: The Engine Behind Big Data's Heavy Lifting
Aman Mundra · 2026-07-13 · 4 min read
Contents
TL;DR - Apache Spark is the leading distributed data processing and analytics engine - batch and streaming, SQL and machine learning, at a scale a single machine could never touch. Its in-memory execution and multi-language APIs (Scala, Python/PySpark, SQL) made it the workhorse of big-data ETL and ML feature pipelines. Spark 4.0 (May 2025) matured Spark Connect into a true client-server architecture and made strict ANSI SQL the default. It is the batch-compute and feature-engineering stage of a cloud-native AI pipeline.
Before Spark, working with data too big for one machine meant writing MapReduce jobs - verbose, slow, and painful. Spark's founding insight was that most of that work could be expressed as ordinary transformations on distributed collections, held in memory across a cluster, and it could be an order of magnitude faster. That combination - familiar programming model, serious speed - is why Spark became the default engine for large-scale data work.
What Spark does
Spark takes a computation you express over a large dataset and transparently splits it across a cluster: partitioning the data, scheduling the work, moving as little of it over the network as possible, and recovering from node failures without losing the job. You write what looks like single-machine code; Spark makes it distributed.
Its reach comes from a unified set of APIs over one engine:
- Spark SQL - query structured data with SQL or DataFrames; the most-used entry point.
- Structured Streaming - the same DataFrame model applied to unbounded, real-time data.
- MLlib - distributed machine learning.
- PySpark - the Python API that made Spark accessible to the entire data-science world, and today the dominant way people use it.
That breadth is why Spark is the processing layer of so many data platforms: the same engine handles the batch ETL, the streaming, the feature engineering, and the ML prep, instead of stitching four different systems together.
Spark 4.0: the client leaves the cluster
Spark 4.0, released May 2025, resolved over 5,100 tickets from more than 390 contributors - and its defining theme, once again shared across the Apache data stack this cycle, was decoupling.
- Spark Connect matured. Historically, a Spark application was tightly bound to the cluster: your client code effectively ran inside it. Spark Connect splits them into a thin client and a remote server communicating over a stable protocol, and in 4.0 it reached high feature parity with classic Spark. The payoff is concrete: a tiny 1.5 MB Python client, new official clients for Go, Swift, and Rust, and the ability to embed Spark in applications without dragging the whole cluster runtime along. It is the same "cut the bolted-on dependency" instinct that removed ZooKeeper from Kafka and decoupled workers in Airflow.
- ANSI mode is now the default. Spark now defaults to strict, standard SQL semantics - for example, raising an error on invalid operations instead of silently returning null. This is a meaningful behavioural change that catches real data-quality bugs earlier, and it is worth flagging before you upgrade.
- Richer SQL and Python. A new
VARIANTtype for semi-structured data, SQL user-defined functions, session variables, pipe syntax, string collation, native Plotly-based plotting on PySpark DataFrames, a Python Data Source API, and Java 21 support. ML now runs natively on Spark Connect. (SparkR is deprecated - plan an exit if you use it.)
Where Spark fits in an AI stack
Spark is the heavy lifting. In a data or ML platform it does the batch compute: the ETL that shapes raw events into usable tables, the distributed feature engineering that turns those tables into model inputs, and the large-scale preparation of training data. It sits naturally between Kafka (streaming ingestion) and Airflow (orchestration) as the processing engine, and together the three form the data-infrastructure backbone under many cloud-native AI systems.
For ML teams, Spark's value is that feature engineering at scale is a distributed-systems problem in disguise, and Spark is the engine that hides that complexity - you write DataFrame transformations; it handles the cluster.
Getting involved
Spark's contribution surface spans PySpark, Spark SQL, Structured Streaming, and MLlib, plus connectors and data sources, and the usual accessible on-ramps of documentation, examples, and tests. Larger changes go through SPIPs (Spark Project Improvement Proposals). It is one of the ASF's largest and most active projects, with a deep bench of maintainers and a well-trodden path from first PR to committer.
