One is a centralized, lightweight data science utility — the other is a more robust data science platform. Which one should you use for your data analysis?
Apache Spark and Apache Hadoop are all popular, open source data science tools provided 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 storing and distributed processing of big data. Both can be used 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. As a powerful unified analytics engine, Apache Spark is often used by data scientists to support complex data analysis and machine learning algorithms. Apache Spark can be run standalone or as a software package on top of Apache Hadoop.
What is Apache Hadoop?
Apache Hadoop is a set of open source modules and utilities that make storing, managing, and analyzing big data easier. Apache Hadoop modules include Hadoop YARN, Hadoop MapReduce, and Hadoop Ozone, but it supports many optional data science software packages. Apache Hadoop can 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 | Right | It’s correct |
| Streaming online |
Right | No |
| Easy to use | Right | No |
| Caching | It’s correct | No |
Design and Architecture
Apache Spark is an open source, discrete data processing utility. Through Spark, developers have access to a lightweight interface for programming data processing clusters, with built-in fault tolerance and data parallelism. Apache Spark is written in Scala and is used mainly for machine learning applications.
Apache Hadoop is a larger framework that includes utilities like Apache Spark, Apache Pig, Apache Hive, and Apache Phoenix. A more general-purpose solution, Apache Hadoop provides data scientists with a complete and powerful software platform that they can then extend and customize to their individual needs.
Range
Apache Spark’s scope is limited to its own tools, which include Spark Core, Spark SQL, and Spark Streaming. Spark Core provides much of the data processing of Apache Spark. Spark SQL provides support for an additional layer of data abstraction through which developers can build structured and semi-structured data. Spark Streaming leverages Spark Core’s scheduling services to perform stream analysis.
The scope of Apache Hadoop is considerably broader. In addition to Apache Spark, Apache Hadoop open source utilities include
- Apache Phoenix. A large parallel relational database engine.
- Apache Zookeeper. A distributed, orchestrated server for cloud applications.
- Apache Hive. A data warehouse for querying and analyzing data.
- Apache Flume. An archiving solution for distributed log data.
However, for the purposes of data science, not all applications are as extensive. Absolute speed, latency, and processing power are essentials in big data analytics and processing — something a standalone installation of Apache Spark can more easily provide .
Speed, velocity
For most implementations, Apache Spark will be significantly faster than Apache Hadoop. Built for speed, Apache Spark can surpass Apache Hadoop by almost 100 times the speed. However, this is because Apache Spark is an order of magnitude simpler and lighter.
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 relatively narrow focus, Apache Spark is easier to learn. Apache Spark has several core modules and provides a clean, simple interface for data manipulation and analysis. Since Apache Spark is a fairly simple product, it is a bit difficult to learn.
Apache Hadoop is much more complex. The difficulty of the interaction will depend on how the developer installs and configures Apache Hadoop and the software package the developer chooses to include. Despite that, Apache Hadoop has a much more significant learning curve even at launch.
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Security and fault tolerance
When installed as a standalone product, Apache Spark has fewer security features and superior fault tolerance 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 widely fault-tolerant by design. Like Apache Spark, its security can be further improved through other Apache utilities.
Programming language
Apache Spark supports Scala, Java, SQL, Python, R, C#, and F#. It was originally developed in Scala. Apache Spark supports nearly all popular languages used by data scientists.
Apache Hadoop is written in Java, with parts written in C. Apache Hadoop utilities support other languages, making it suitable for data scientists of all skill sets.
Choosing between Apache Spark and Hadoop
If you are a data scientist working mainly in machine learning algorithms and large-scale data processing, choose Apache Spark.
Apache Spark:
- Runs as a standalone utility without Apache Hadoop.
- Provides task orchestration, I/O, and distributed scheduling functionality.
- Supports multiple languages, including Java, Python, and Scala.
- Provides implicit data parallelism and fault tolerance.
If you are a data scientist who requires a wide range of data science utilities to store and process big data, choose Apache Hadoop.
Apache Hadoop:
- Provides an extensible framework for storing and processing big data.
- Offers an incredible range of packages, including Apache Spark.
- Build on top of a distributed, extensible and portable file system.
- Leverage additional applications for data warehousing, machine learning, and parallelism.
