Mastering Hadoop – Pros and Cons of Using Hadoop Technology

Hadoop is fundamentally meant to distribute storage and processing of Big Data sets on clusters of computer systems created on commodity hardware and it is an open-source software framework. While designing its modules, one basic assumption is made that failures occurring in the hardware are common and the 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.

In large data, it splits down a large block and distributes 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 and 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.

Check out the top Hadoop Courses

5 Reasons Why 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, it 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 and cost.

Explore popular Big Data Courses

Data Mixing

Hadoop is best to be applied when an organization is having data diversity to be processed. The most significant advantage HDFS has 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 logs files, or completely unstructured i.e. videos, audios, it 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 it. This is also the reason that if a professional is having skills in Java coding along with data science, he/she will be high in demand by organizations.

Also Read: Career Advantages of Hadoop Certification

Future Vision of Hadoop Utilization

Some organizations do not have data huge enough to utilize Hadoop yet visualize themselves using it in near future. It will be beneficial for them to start experimenting with Hadoop and prepare working IT professionals to work with it comfortably.

Also Read: Top 8 Highest Paying IT Certifications

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 Petabytes.

5 Reasons Why Hadoop Should Not be Used

The 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.

Explore popular Databases Courses

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 and 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 and serve interactive queries for random data. A competitor like SQL is in the process of enhancing its capability to perform the same and outperform it.

Crucial Data Storage

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

Check out the Top Online IT Courses

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 it can complement data warehouse platforms but cannot replace it.

If you have recently completed a professional course/certification, click here to submit a review.