Hadoop Use Cases

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The Hadoop Distributed File System (HDFS) is a file system with an implementation based on Java. This translates into scalability, reliability, and efficiency in regards to computing distribution. HDFS is also open source.

Hadoop’s popularity has soared due to its large tolerance to errors and highly compatible architecture that was made for inexpensive hardware. Well-known global iconic businesses that use Hadoop include Amazon, eBay, Alibaba, Facebook, and LinkedIn.

To help you determine if HDFS is right for your business, let us examine some Hadoop use cases that illustrate its flexibility. Afterward, we will touch on its limitations.

Operating Sytems that work with Hadoop

Many different types of operating systems are compatible with Hadoop. These include Windows, Linux, OpenSolaris, Mac OS/X, BSD.

>NOTE: Windows and Linux are supported by Hadoop. Other operating systems tend to work with Hadoop, although the application does not offer support for them.

Three main elements of Hadoop are:

  • The HDFS – Hadoop Distributed File System is the heart of Hadoop.

  • Hadoop MapReduce algorithm- It handles large data sets processing by breaking up a file, reducing it to small splits to complete the job.

  • YARN – Hadoop uses Yet Another Resource Negotiator (YARN) to allow you to manage and monitor clusters, stop applications, submit applications too. A key benefit of YARN is its stability effect for cluster nodes. If a node ceases to run, jobs are moved to another node where they will continue to run.

Main benefits of Hadoop compared to relational databases

Hadoop has many benefits over relational databases. With Hadoop:

  • Users bypass the step of reformating the stored data that they want to retrieve later.

  • Automatic replication of data copies takes place over the cluster.

  • Setting the configuration for replication amounts for individual files is possible and at any time, modifications can be made.

Examples of Hadoop use cases

Businesses enjoy using Hadoop to conduct data analysis, searching data, reporting of data, and indexing web crawlers or log file data and other types on a large scale data. Many of these tasks are made possible with MapReduce, a Hadoop core component, which is also great for big data processing projects.

Processing data in petabytes or terabytes

When it concerns big data, Hadoop is perfect for processing data in petabytes or at least terabytes. A gigabytes size is not large enough to warrant the need for Hadoop. Therefore, large enterprises with lots of data suit it well.

It could be that although your business is not yet handling big enough data, you estimate that your data will grow substantially. You may even see the trajectory leading to bigger data in the near future. It is a good idea to review your goals of how long you want to maintain the data. For example, it might be helpful to keep raw data around continually so you can process it in different ways.

Storing data in different formats

Processing and storing multiple file formats are two more Hadoop use cases that businesses appreciate. Over the course of a day, it’s common to process and store a variety of files such as images, plain text, as well as different versions of the same file. With HDFS, handling data on a large scale is much simpler and faster compared to dealing with the complexities of the traditional, slow migration data process.

Data processing parallelizing

As touched on briefly earlier, one main Hadoop element is the algorithm MapReduce. This is suitable among the list of Hadoop use cases if you conduct one-at-a-time variable processing such as for aggregation or counting. Parallelizing data is mandatory in order to utilize MapReduce. Unfortunately, when you have several variable correlations, it becomes too complex for the algorithm. The good news is that, depending on your data requirements, you may not need to run joint variable processing.

To boost the functionality of MapReduce, use the Apache Tez execution framework which utilizes Yet Another Resource Negotiator (YARN) cluster management OS to process data more robustly.

Situations where Hadoop isn’t a good fit

Hadoop, like all applications, has some limitations, but the positives outweigh those overall. Here is a shortlist of things of which you should be aware:

  • Not suitable for conducting data analysis in real time – Relational databases will process the data faster in this case because Hadoop doesn’t process every bit of data at the same time. Instead, it works in batches.

  • Does not replace a relational database – Since Hadoop processes batches of data as opposed to everything at once, it is a bit slower than a relational database.

  • General network file system non-suitability – HDFS wasn’t designed to be a general network file system because it does not contain POSIX standard features. For example, there are certain things that general network systems must be able to do such as arbitrary point updating.


The flexibility of processing huge amounts of data in different formats is what draws businesses to Hadoop. It was made to run on hardware that is inexpensive, and this also adds sweetness to the number of advantages of Hadoop use cases. Large enterprises, rather enterprises with big data benefit most from using Hadoop. Now you’ve learned some basics of the open-source file system. Take a few moments to consider if it’s right for your business.

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