One is a lightweight, focused data science tool, the other is a more robust data science platform. Which one 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 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 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. Apache Spark, a robust unified analytics engine, is commonly used by data scientists to support machine learning algorithms and complex data analytics. Apache Spark can run as a 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 tools designed 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 can be used interchangeably to refer to Apache Spark and other data science tools.
Apache Spark vs. Apache Hadoop: Head to Head
|Easy to use
Design and architecture
Apache Spark is a discreet, open-source data processing utility. Spark gives developers 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 primarily used for machine learning applications.
Apache Hadoop is a larger framework that includes tools such as Apache Spark, Apache Pig, Apache Hive, and Apache Phoenix. Apache Hadoop, a more general solution, provides data scientists with a complete and robust software platform that they can then extend and adapt to individual needs.
The scope of Apache Spark is limited to its own tools, including Spark Core, Spark SQL, and Spark Streaming. Spark Core provides most of Apache Spark’s data processing. Spark SQL supports an additional layer of data abstraction, allowing developers to build structured and semi-structured data. Spark Streaming uses Spark Core’s scheduling services to perform streaming analytics.
The scope of Apache Hadoop is considerably wider. In addition to Apache Spark, Apache Hadoop’s open-source tools include:
- Apache Phoenix. A hugely parallel, relational database engine.
- Apache Zookeeper. A coordinated, distributed server for cloud applications.
- Apache hive. A data warehouse for retrieving and analyzing data.
- Apache Flume. A warehousing solution for distributed log data.
However, for the purposes of data science, not all applications are so broad. Speed, latency, and sheer processing power are essential when it comes to big data processing and analytics, something that a standalone installation of Apache Spark can provide more easily.
For most implementations, Apache Spark will be significantly faster than Apache Hadoop. Apache Spark is built for speed and can outperform Apache Hadoop nearly 100 times faster. However, this is because Apache Spark is an order of magnitude simpler and lighter.
Apache Hadoop is not as fast as Apache Spark by default. However, performance may vary depending on software packages installed and data storage, maintenance and analysis work.
Due to its relatively limited focus, Apache Spark is easier to learn. Apache Spark has a handful of core modules and provides a clean, simple interface for manipulating and analyzing data. Since Apache Spark is a fairly simple product, the learning curve is small.
Apache Hadoop is much more complex. The difficulty of involvement depends 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 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 tools 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, security can be further enhanced through other Apache tools.
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 abilities.
Choosing between Apache Spark vs Hadoop
If you’re a data scientist who primarily works with machine learning algorithms and large-scale data processing, choose Apache Spark.
- Runs as a standalone utility without Apache Hadoop.
- Provides distributed load balancing, 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 large number of data science tools for big data storage and processing, then choose Apache Hadoop.
- Provides a comprehensive framework for big data storage and processing.
- Offers an incredible array of packages, including Apache Spark.
- Builds on a distributed, scalable, and portable file system.
- Uses additional applications for data warehousing, machine learning, and parallel processing.