How to process small data using Hadoop ?

How to process small data using Hadoop

When it comes to Hadoop there surrounds a myth that you need terabytes or even petabytes of big data for processing.  However, it’s just a myth.

You can process small data using Hadoop and you know what it has got many advantages. Big data doesn’t always mean volume, the Hadoop assimilates different data types; it processes it quickly and saves money as it scales the data grow.

Below given are four ways to process your small data with Hadoop. Take a look-

Concatenating Text Files: Website logs, emails, or any other textual data can be concatenated it into large files. Hadoop processes data line by line, so the data will be processed just the same even after concatenation.

Hadoop Archives: Have you ever thought of the binary data, for example, videos or images? In that case here comes Hadoop Archives or HAR for rescue. HAR can be used to archive small files no matter in which format into a single file with the help of the command line. HAR files work as another file-system layer on top of the Hadoop data File System so that all the archived file systems can get accessed directly through har:// URLs.

Compress Files with Parquet: It’s a columnar storage format which is accessible for all Hadoop ecosystem projects for example Impala and Hive. It is based on Dremel paper of Google. Although it’s same as RC & ORC files, parquet claims to feature better performance with the help of efficient compression & encoding schemes. One needs to just put the data and see the magic!

The Hadoop runs under it and includes an automatic optimization for small data on the cloud. This works by copying data to the local HDFS of the cluster and optimizing them during the same process. These files are automatically deleted, once the processing of data gets finished. In order to use this feature, you’ll need to set the “pre-process-action” to “copy” in your cloud storage source component.

Now comes the question, why you need Hadoop for this, right? Well, there are so many factors that advocate its implementation. We have compiled some of the best reasons, let’s have a look-

Remember Big Data Doesn’t always Mean Big Volume: Did you remember the four V’s of big data? Yeah, Velocity, Volume, Variety & Veracity. Your data/file doesn’t need these all four together. Even if it has a great velocity and variety, it might be enough to congest MySQL and calls for something different. So, in that case, Hadoop takes care of all these four V’s.

It Integrates Different Data Types: If we talk about variety, then it’s clear that the data obtained from various sources has to be integrated at present- log files, web server, images, emails, videos, CRM, ERP and so on. Hadoop processes all of these formats.

It Processes Data with a High Speed: If you think small data doesn’t take too long to be processed, then think again. Even a bit of data may take a long time. The MapReduce of Hadoop system processes data in parallel and brings considerable advantages in conditions like a failure, redundancy as well as scalability for batch processing like ETL, data transformation, offloading and preparation for analytics. Hadoop allows you to execute more work in less time. What else you need?

Your Data is Growing: According to a recent research, the amount of data in the digital world is growing by 40\% every year. Whether you like it or not, you’re part of it. So, instead of constructing an infrastructure that may clogged next year, use a scalable technology that helps you start small but grow big!

It Saves Money: Yes money matters a lot to run the business smoothly. Apache Hadoop is famous to work with commodity servers that simply means you need not to own them or install personally. You just need to use Hadoop on the cloud. The Amazon’s EMR and such services work on the cloud & make it easier for you to use Hadoop that means scalability, affordability, and easiness.Now, no need to accumulate dust gathering hardware as the cloud allows you use the same source that you require at any point of time and discard them when you are done!

Now, you have seen why Hadoop is a winner when it comes to process small data.Madrid Software Trainings provides complete practical Big data Hadoop Training in Delhi.


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