Apache Spark is a fast processing engine for batch jobs, streaming, SQL and machine learning; Apache Hadoop is a framework for storing and processing huge data sets across clusters, made up of HDFS storage, YARN scheduling and MapReduce processing. They're not strict rivals: Spark can run on a Hadoop cluster through YARN and read data straight from HDFS. The real question is whether you need Hadoop's storage and cluster layer or just a processing engine.
I read the Apache Spark home page, FAQ and documentation overview, and the Apache Hadoop home page and docs. Both are Apache Software Foundation projects, and their own pages explain how they fit together better than most comparisons do.
Spark and Hadoop side by side
| Apache Spark | Apache Hadoop | |
|---|---|---|
| Type | Multi-language engine for data engineering, data science and machine learning | Framework for distributed storage and processing of large data sets |
| Main parts | Spark SQL, Structured Streaming, MLlib, GraphX | Hadoop Common, HDFS, YARN, MapReduce |
| Languages | Python, SQL, Scala, Java, R | Simple programming models, per the project page |
| Storage | Doesn't include its own; reads HDFS, HBase, Cassandra, Hive and more | HDFS distributed file system |
| Cluster managers | Standalone, YARN or Kubernetes | YARN |
| Free option (as of October 2026) | Yes, open source | Yes, open source |
What Hadoop is made of
Hadoop's home page describes it as a software library for distributed processing of large data sets across clusters of computers using simple programming models, scaling from single servers to thousands of machines that each offer local computation and storage. Instead of relying on hardware to deliver high availability, Hadoop is designed to detect and handle failures at the application layer.
The project lists four modules. Hadoop Common holds shared utilities. HDFS is a distributed file system for high-throughput access to data. YARN handles job scheduling and cluster resource management. MapReduce is a YARN-based system for parallel processing of large data sets. Around it sit related Apache projects such as Hive, HBase, Ozone and, notably, Spark itself.
What Spark does differently
Spark describes itself as a multi-language engine for data engineering, data science and machine learning on single machines or clusters. It unifies batch and real-time streaming, runs distributed ANSI SQL, and lets you train machine learning code on a laptop and scale the same code to clusters of thousands of machines. Installing it can be as simple as pip install pyspark.
Spark's FAQ answers the comparison question directly. It calls Spark a fast and general processing engine compatible with Hadoop data that can run in Hadoop clusters through YARN or in its standalone mode, processing data in HDFS, HBase, Cassandra, Hive and any Hadoop InputFormat. It's designed for batch processing similar to MapReduce plus newer workloads like streaming, interactive queries and machine learning.
Two facts people get wrong
First, Spark doesn't need everything to fit in memory. Its FAQ says Spark's operators spill data to disk when it doesn't fit, so it runs well on any sized data. Second, you don't need Hadoop to run Spark. The FAQ says a cluster needs some form of shared file system, but that doesn't have to be HDFS.
On performance, the Spark project points to the 2014 Daytona GraySort benchmark, where it sorted 100 TB of data 3X faster than Hadoop MapReduce on one tenth of the machines. The largest Spark cluster the project knows of has 8000 nodes.
How to choose
- Do you need to store very large data sets across your own servers? Hadoop's HDFS is built for that.
- Do you need fast batch jobs, SQL, streaming or machine learning? Spark is the engine for that work.
- Already running a Hadoop cluster? Run Spark on YARN and keep your data in HDFS.
- Starting fresh in the cloud? Spark runs on Kubernetes or standalone without a Hadoop cluster.
- Writing in Python or SQL? Spark supports both directly.
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My advice on Spark or Hadoop
For most new projects, start with Spark: it covers batch, streaming, SQL and machine learning in one engine and doesn't require a Hadoop cluster. Keep or add Hadoop when you need its distributed storage on your own hardware, and run Spark on top of it through YARN. Thinking of them as layers, storage and cluster management underneath and processing on top, makes the choice much simpler.
Do you make data engineering tools? Tool makers can ask to be considered for a featured place in comparisons like this one.
Frequently asked questions
Does Spark need Hadoop?
No. Spark's FAQ says you don't need Hadoop, but a cluster needs some form of shared file system. Spark can also run on YARN and read HDFS if you do have Hadoop.
Is Spark faster than Hadoop MapReduce?
The Spark project says it sorted 100 TB of data 3X faster than Hadoop MapReduce on one tenth of the machines in the 2014 Daytona GraySort benchmark.
What are Hadoop's main modules?
Hadoop lists Hadoop Common, HDFS (a distributed file system), YARN (job scheduling and resource management) and MapReduce (parallel processing).
Does data have to fit in memory for Spark?
No. Spark's operators spill data to disk if it doesn't fit in memory, so it can run on any sized data.
Which languages does Spark support?
Spark supports Python, SQL, Scala, Java and R.
