Debunking the Top 5 Myths about Big Data & Hadoop

The era of big data Hadoop has arrived. Today, companies both large and small are discovering the benefits of analyzing vast pools of unstructured data for new insights and competitive advantage. Big data analytics is one of the major trends every company is told it must jump on for competitive advantage, even survival.Because of which, there’s a lot of mythology around big data.There is a number of lingering misconceptions about big data that companies looking to successfully implement. Given below are the top 5 myths about Big Data Hadoop that you should not believe-

1. Only big companies need big data Hadoop– One of the common myth about Big Data Hadoop Training is that only big companies need it. However, this is not true as whether you are a small, medium or big enterprise, leveraging big analytics to better manage your business is essential. It also gives you the competitive advantage.

2. Hadoop is batch only– Perhaps the most prevalent myth is that Hadoop isonly useful for batch processing. However, the reality is that Hadoop also works well with other real-time big data solutions, including Spark, and is capable of handling immediate processing jobs like online customer transactions where there is no room for errors or latency.

3. Big Data Implementation is Expensive– Another myth is that Big Data is expensive and comes with a price tag. In actuality, implementing Hadoop is very affordable. But, this is relative and depends on what kind of solution a company decides to go for, but budget spend on big data can lead to huge ROI.

4. Hadoop lacks in security– The reality is that security was built into Hadoop gradually. In the beginning, it was hard to achieve enterprise level security in Hadoop, but over the past few years, as many large businesses took on big data initiatives, Hadoop has come a long, long way in terms of security.

5. Big data analytics is too complicated to comprehend – The problem most people face with Big Data Analytics is that it is too difficult to comprehend. Not all big data solutions are equal. When looking at a big data analytics environment, mostlythe need to store, process, analyze and visualize data is there.But, there are specialized tools that are designed to meet a company’s environment – whether on- premise or in the cloud.

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