Apache Spark vs. Apache Hadoop
One is a lightweight and focused data science utility, the other is a more robust data science platform. Which should you use for your data analysis?
Apache Spark and Apache Hadoop are both popular open-source data science tools offered by the Apache Software Foundation. Developed and supported by the community, they continue to grow in popularity and functionality.
Apache Spark is designed as an interface for large-scale processing, while Apache Hadoop provides a larger software framework for distributed storage and 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 designed 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 analysis. Apache Spark can run 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 ease the process of storing, managing, and analyzing big data. Apache Hadoop modules include Hadoop YARN, Hadoop MapReduce, and Hadoop Ozone, but it supports many optional data science 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|
|Easy to use||Yes||Nope|
Design and Architecture
Apache Spark is a discrete, open-source data processing utility. Using Spark, developers have access to a lightweight interface for programming data processing clusters, with built-in fault tolerance and data parallelism. Apache Spark was written in Scala and is primarily used 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 versatile solution, Apache Hadoop provides data scientists with a comprehensive and robust software platform that they can then extend and customize to suit 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 core data processing of Apache Spark. Spark SQL supports an additional layer of data abstraction, through which developers can create structured and semi-structured data. Spark Streaming leverages Spark Core scheduling services to perform streaming analytics.
The scope of Apache Hadoop is significantly wider. In addition to Apache Spark, Apache Hadoop open source utilities include
- Apache Phoenix. A massively parallel relational database engine.
- Apache Zookeeper. A coordinated and distributed server for cloud applications.
- Apache hive. A data warehouse for querying and analyzing data.
- Apache Channel. A warehousing solution for distributed log data.
However, for data science purposes, not all applications are so broad. Speed, latency, and processing power are essential in the realm of big data processing and analysis, which a standalone installation of Apache Spark can more easily provide.
For most implementations, Apache Spark will be significantly faster than Apache Hadoop. Built for speed, Apache Spark can outperform Apache Hadoop by almost 100 times the speed. However, that’s 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.
Due to its relatively narrow focus, Apache Spark is easier to learn. Apache Spark has a handful of core modules and provides a clean and simple interface for manipulating and analyzing data. As Apache Spark is a fairly simple product, the learning curve is slight.
Apache Hadoop is much more complex. The difficulty of engagement will depend on how a developer installs and configures Apache Hadoop and what software packages the developer chooses to include. Either way, Apache Hadoop has a much steeper 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 largely fault tolerant by design. Like Apache Spark, its security can be further enhanced through other Apache utilities.
Apache Spark supports Scala, Java, SQL, Python, R, C#, and F#. It was originally developed in Scala. Apache Spark supports almost 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’re a data scientist working primarily in machine learning algorithms and large-scale data processing, choose Apache Spark.
- Works as a standalone utility without Apache Hadoop.
- Provides distributed task dispatching, I/O functions, and scheduling.
- Supports multiple languages including Java, Python, and Scala.
- Provides implicit data parallelism and fault tolerance.
If you are a data scientist who needs a wide range of data science utilities for storing and processing big data, choose Apache Hadoop.
- Offers an extended framework for storing and processing big data.
- Provides an amazing range of packages including Apache Spark.
- Relies on a distributed, scalable and portable file system.
- Leverages additional applications for data warehousing, machine learning, and parallel processing.