One is a lightweight, focused data science utility—the other is a more robust data science platform. Which should you use for your data analytics?

Businessman pressing button on screen to demonstrate data science tools.
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Apache Spark and Apache Hadoop are both popular, open-source data science tools offered by the Apache Software Foundation. Developed by and supported by the community, they continue to grow in popularity and features.

Apache Spark is designed as an interface for large-scale processing, while Apache Hadoop provides a broader software framework for the distributed storage and processing of big data. Both can be used either together or as standalone services.

What is Apache Spark?

Apache Spark is an open-source data processing engine built for efficient, large-scale data analysis. A robust unified analytics engine, Apache Spark is frequently used by data scientists to support machine learning algorithms and complex data analytics. Apache Spark can be run either standalone or as a software package on top of Apache Hadoop.

What is Apache Hadoop?

Apache Hadoop is a collection of open-source modules and utilities intended to make the process of storing, managing and analyzing big data easier. Apache Hadoop’s modules include Hadoop YARN, Hadoop MapReduce and Hadoop Ozone, but it supports many optional data science software packages. Apache Hadoop may be used interchangeably to refer to Apache Spark and other data science tools.

Apache Spark vs. Apache Hadoop: Head-to-head

  Apache Spark Apache Hadoop
Batch Processing Yes Yes
Streaming Yes No
Easy to Use Yes No
Caching Yes No

Design and architecture

Apache Spark is a discrete, open-source data processing utility. Through Spark, developers gain access to a lightweight interface for the programming of data processing clusters, with built-in fault tolerance and data parallelism. Apache Spark was written in Scala and is used primarily for machine learning applications.

Apache Hadoop is a larger framework that includes utilities such as Apache Spark, Apache Pig, Apache Hive and Apache Phoenix. A more general-purpose solution, Apache Hadoop provides data scientists with a complete and robust software platform that they can then extend and customize to individual needs.


Apache Spark’s scope is limited to its own tools, which include Spark Core, Spark SQL and Spark Streaming. Spark Core provides the bulk of Apache Spark’s data processing. Spark SQL provides support for an additional layer of data abstraction, through which developers may build structured and semi-structured data. Spark Streaming leverages Spark Core’s scheduling services to perform streaming analytics.

Apache Hadoop’s scope is significantly broader. In addition to Apache Spark, Apache Hadoop’s open-source utilities include

  • Apache Phoenix. A massively parallel, relational database engine.
  • Apache Zookeeper. A coordinated, distributed server for cloud applications.
  • Apache Hive. A data warehouse for data querying and analysis.
  • Apache Flume. A warehousing solution for distributed log data.

However, for the purposes of data science, not all applications are this broad. Speed, latency, and sheer processing power are essential within the field of big data processing and analytics—something that a standalone installation of Apache Spark may more readily provide.


For most implementations, Apache Spark will be significantly faster than Apache Hadoop. Built for speed, Apache Spark may outcompete Apache Hadoop by nearly 100 times the speed. However, this is because Apache Spark is an order of magnitude simpler and more lightweight.

By default, Apache Hadoop will not be as fast as Apache Spark. However, its performance may vary depending on the software packages installed and the data storage, maintenance and analysis work involved.

Learning curve

Due to its comparatively narrow focus, Apache Spark is easier to learn. Apache Spark has a handful of core modules and provides a clean, simple interface for the manipulation and analysis of data. As Apache Spark is a fairly simple product, the learning curve is slight.

Apache Hadoop is far more complex. The difficulty of engagement will depend on how a developer installs and configures Apache Hadoop and which software packages the developer chooses to include. Regardless, Apache Hadoop has a far more significant learning curve even out of the box.

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Security and fault tolerance

When installed as a standalone product, Apache Spark has fewer out-of-the-box security and fault-tolerance features than Apache Hadoop. However, Apache Spark has access to many of the same security utilities as Apache Hadoop, such as Kerberos Authentication—they just need to be installed and configured.

Apache Hadoop has a broader native security model and is extensively fault-tolerant by design. Like Apache Spark, its security can be further improved through other Apache utilities.

Programming languages

Apache Spark supports Scala, Java, SQL, Python, R, C# and F#. It was initially developed in Scala. Apache Spark has support for nearly all the popular languages data scientists use.

Apache Hadoop is written in Java, with portions written in C. Apache Hadoop utilities support other languages, making it suitable for data scientists of all skill sets.

Choosing between Apache Spark vs. Hadoop

If you are a data scientist working primarily in machine learning algorithms and large-scale data processing, choose Apache Spark.

Apache Spark:

  • Runs as a standalone utility without Apache Hadoop.
  • Provides distributed task dispatching, I/O functions and scheduling.
  • Supports multiple languages, including Java, Python and Scala.
  • Offers implicit data parallelism and fault tolerance.

If you are a data scientist who requires a large array of data science utilities for the storage and processing of big data, choose Apache Hadoop.

Apache Hadoop:

  • Offers an extensive framework for the storage and processing of big data.
  • Provides an incredible array of packages, including Apache Spark.
  • Builds upon a distributed, scalable and portable file system.
  • Leverages additional applications for data warehousing, machine learning and parallel processing.