Hadoop, Spark or Both?

Spark or Hadoop? This question has recently sparked various discussions throughout the online communities. Even though these two work on different principles, they can be applied in a same way for various uses. While Hadoop is a household name in the world of big data processing, Spark is still building a name for itself and it’s doing so with “style”.

We will go through some of the basic functional elements of these frameworks, and what are the common practices in the world of big data when it comes to using Hadoop and Spark.

Hadoop

Hadoop is quite commonly used and is known for being a secure big data framework based on a lot of, mostly open source programs and algorithms. This means that the majority of its contents are directly editable and almost all of those contents are free. These elements can then be used practically as a foundation for your own big data analysis. Hadoop is based on four core “modules,” specific parts of framework that carry out different essential tasks for computer systems meant for big data analysis.

• Distributed File systems – One of the most important Hadoop modules is the file system. This allows data to be stored in accessible formats across quite a number of devices and platforms. Hadoop has its own file system that has higher priority over OS file systems, which allows it to be accessed from any device or platform, regardless of its own operating system.
• MapReduce – This module is basic for poking around and investigating your data. Its primary functions are to read the database, translate the gathered data into a suitable format or a map, and apply various math functions in order to get a specific analysis (demographics, age, gender and similar).
• YARN – This module combines a central resource manager that reconciles the way applications use Hadoop system resources with node manager agents that monitor the processing operations of individual cluster nodes.
• Hadoop Common – A very basic module that provides users with tools in JavaScript that allow them to work in any environment. This is a module that helps users read and analyze the collected data on various different systems, like UNIX, Linux, Windows and many more.

Besides these four core modules, there is a plethora of others, but for full deployment these, four are necessary.

Hadoop represents a very solid big data framework which is quite flexible. It can be easily modified for specific needs for a reasonable price. It is one of the most used data storing and processing systems and is used by some of the corporate giants in various different markets. Its applicability, flexibility and availability make it a great tool when it comes to specific big data analysis systems.

Spark

While Spark is listed as yet another module on Hadoop’s page, it also has its own standalone version. Basically, the main difference between the two of them is that Spark processes data through logical memory, RAM, while Hadoop works with disks. Practically, you can look at Spark as a layer that is placed above data stores from which it can load data into memory with parallel analysis. Much like Hadoop, Spark has some major components that make it what it is.

• Spark Core – Spark’s foundation which distributes tasks and works with scheduling.
• Spark Streaming – A real-time streaming data analysis module. Basically helps you analyze sea of data as it comes in real time.
• Spark Machine Learning Library (MLlib) – An extensive library of analytic algorithms for Spark cluster, adaptable to all other clusters Spark can work with.

There are other important components of Spark, like GraphX, an engine for graph analysis, and Spark SQL that work from Java, Python, R and Scala based Spark analytics, but Spark’s main utilization is similar to MapReduce. It helps process streams of data in extremely little time, and due to its nature, it can be adjusted to serve any needs.

Overview

General consensus is that what makes Spark stand out when it gets compared to Hadoop is its speed. While Hadoop focuses on switching and transferring data through hard disks, Spark runs its operations through memory. Working through logical RAM increases the speed quite significantly, so Spark can handle data analysis quite faster than Hadoop. Having said that, we arrive to the reason why Spark is listed as a module to Hadoop. Namely, Spark lacks a file system and is as such is useless as a standalone. To get Spark to work without Hadoop, one would need to opt for a third party file organizing system.

So, which should you go with? Spark, Hadoop or both? First and the most important thing to know when it comes to these frameworks is that you have to know what you will use the framework for. If you’re handling a business that is mostly based around collecting large datasets that need to be processed in batch and stored, Spark’s machine learning capabilities and streaming analytics may be too much for you. On the other hand, the ever-expanding sea of Spark users find new applications and uses for it – machine learning, marketing campaigns, security analytics, IT services, IoT sensors, social media sites and log monitoring. Great thing about open source principle is that similar products with different functions can freely coexist in the market, helping business owners maximize their potential as a result.

The definite answer is ­– you can go either way. As presented above, both frameworks have their specific uses and as such can be exclusive. However, setting Spark up with a third party file system solution can prove to be complicating, and since both Hadoop and Spark are maintained by Apache Software Foundation, it is implied that using Spark on top of Hadoop is the best long term solution in regards to compatibility and streamlined experience. Also, when talking about long term, it is predicted that by 2022 Spark will overcome its shortcomings and dominate the big data landscape, thanks to its superiority when compared to some Hadoop features.

About the Author

Blake Davies is an IT writer and support professional who has contributed to a number of  online media outlets.

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