After its inception, Hadoop took over the IT world like a storm and everyone was raving about it. However, with time and after years of use, it still hasn’t reached its apotheosis. One of the biggest reasons for its alleged failure has been the inability of companies to fully utilize it. No one has been able to look beyond the horizon where technology meets present day utility, and hence, Hadoop seems like it is failing. But looking at Hadoop with the future in mind, there is a strong feeling amongst IT professionals that it will gain ground on the other contemporary technologies and reach its full potential in the coming years. It is high time for IT aspirants to look at this amazing technology with seriousness and learn Hadoop for a fulfilling career.
A Hadoop data scientist earns an average salary of $99,333, which makes learning Hadoop an attractive proposition for IT career aspirants.
Why Hadoop Seems To Be Falling?
There are many reasons why people believe Hadoop is falling. Below are some of these reasons:
1. Difficult To Harness For Profits
After a surge in the adoption of Hadoop upon its launch, many organizations shrugged it because they found it too big to be useful. A lot of companies discovered that Hadoop as a Big Data technology was difficult to harness in a way for it to be profitable in the long run.
2. Availability Of Other Cost-Effective Solutions
Platforms like Apache Spark, Ceph, Disco, and Hydra are similar to Hadoop, however, these solutions are more economical and hence, profitable for organizations.
3. Companies Are Unable To Decipher Hadoop’s True Value
It is hard to estimate the true value of Hadoop in the current state of IT industry. Though it has immense potential, people are unable to ascertain when it will help them leverage their businesses and make a decent profit using Hadoop.
4. Hard To Anticipate Completion Of Big Data Processing
Hadoop platform can guarantee that your data processing will complete, however, it does not always tell you when it will complete. This is a major drawback of Hadoop, as it puts doubts in the mind of users who want their crucial tasks to be completed in time.
5. Lack Of Visibility And Control
Hadoop system lacks the visibility and controls required by system admins for effectively diagnosing and resolve the issues in the system. This is one of the many reasons why organizations are hesitant in adopting Hadoop as their Big Data solution.
Why Hadoop Is Better In The Long Run?
Despite the aforementioned disadvantages, Hadoop is still one of the most powerful Big Data frameworks in the IT world.
1. Massive Processing Capabilities
Hadoop is the most effective platform when it comes to processing massive amounts of data in the minimum amount of time. With this amazing framework at your disposal, you can future proof your business for the times when Big Data would expand in size and all other solutions will be rendered ineffective for handling such large amount of data.
2. Highly Scalable
Hadoop can be easily scaled and hence, it is the perfect solution for domains where the quantity of Big Data is expected to grow with time. The scalability of this framework is another desirable characteristic which makes it future safe.
3. Versatility
The flexibility of Hadoop allows you to work on different types of data sets. This cuts down on the need for bringing in new solutions for handling different type of tasks. The versatility of Hadoop can be a major asset for any organization that is striving to reduce complexity in their operations.
Future Of Hadoop
It is not right to take Hadoop lightly because of its present status. The incredible potential that it has makes it a worthwhile tool for the future. With its revolutionary data warehousing techniques, Hadoop is able to self-optimize itself and has the ability to work flawlessly without any need for human intervention. In the quest for technological singularity, Hadoop can be an amazing tool that can leverage AI and bring about unimaginable changes in human perspective towards technology.
Hadoop Database Training At QuickStart
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