Hadoop is fundamentally meant to distribute storage & processing of Big Data sets on clusters of computer systems created on commodity hardware and it is an open source software framework. While designing the modules for Hadoop, one basic assumption is made that failures occurring in the hardware are common and framework would handle them all automatically. It comprises a storage part known as Hadoop Distributed File System (HDFS) along with MapReduce, which is the processing part. What Hadoop does with large data is that it splits down a large block & distribute the small blocks evenly across the cluster. With the assistance of data locality, it transfers packaged codes for nodes in the cluster for data to be processed. This helps the data to be processed swiftly & efficiently. The biggest challenge in utilizing Hadoop to its full potential is the wisdom of knowing that where it can be used and where not.

5 reasons Hadoop should be used:

For humungous data sets:

Every organization feels that their data is huge enough to utilize Hadoop for it. The truth is this is not the case always. Along with huge data handling capacities, Hadoop also comprises limitations on the programming of applications and to the pace the results are obtained. Therefore, organizations having data in MBs or GBs are recommended to use Excel, SQL or BI tool (Postgres) to get faster results. Whereas, when data gets bigger to Terabytes or even Petabytes, then Hadoop is the most efficient technology to be applied as its immense scalability will save time & cost.

Data Mixing:

Hadoop is best to be applied when an organization is having data diversity to be processed. The most significant advantage HDFS (Hadoop Distributed File System) have is that it is very flexible when it comes to data types. It does not matter whether the raw data is structured as in ERP system, semi-structured as in XML or log files or completely unstructured i.e. videos, audios, Hadoop can handle it in the best way possible.

Specialized programming skills

Hadoop is been driven to be converted into a general purpose computing framework, but as of now, all the Hadoop applications are developed in Java. Therefore, if programmers have mastered the skill of Java coding then it is best to utilize Hadoop. This is also the reason that if a professional is having skills of Java coding along with data science, he/she will be high in demand by organizations.

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Future vision of Hadoop utilization:

If an organization is not having a data huge enough to utilize Hadoop yet it visualizes itself using Hadoop in near future, it will be beneficial for it to start experimenting with Hadoop and prepare working IT professionals to work with it comfortably.

Optimum data utilization:

In some cases, it happens that some potential data has to be thrown as it costs a fortune to archive it. To retain this data and utilize it in the best way possible, Hadoop can be used as it can handle data as huge as in Petabytes.

5 reasons Why Hadoop should not be used:

Trade-off for Time:

Hadoop is without a doubt the best to be utilized to handle huge data. The only improvement needed is the time taken to produce outcomes. It is hence recommended to utilize Excel or SQL or any other tool to process smaller data up to Gigabytes.

Intense optimization for queries:

To get the best out of Hadoop, a substantial investment is required, in order to optimize the queries. If we carry out the same process with software-based optimizers in combination with conventional data warehouse platforms, better & economical results can be obtained.

Inability to interactive access to random data

Hadoop having endless pros for data handling, also possess few disadvantages. One of the most significant ones is that it has limitations in its batch-oriented MapReduce, which restricts it to access & serve interactive queries for random data. A competitors like SQL is in a process for enhancing its capability to perform the same and outperform Hadoop.

Crucial data storage:

One of the most notable limitation of Hadoop is that is it not efficient in storing sensitive and crucial data. Hadoop comprises of basic data and access security, hence, there is a risk of accidentally losing crucial identifiable information

Data warehouse replacement:

There has been a notion building in the market that Hadoop can totally replace the traditional data warehouse platforms. This is not the complete truth as Hadoop can complement data warehouse platforms but cannot replace it.

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