Data Science, MGADS,

How to Avoid these 5 Hadoop Mistakes?

Data storage was nothing less than a huge headache for businesses just a few years ago. Thankfully, new technologies such as Hadoop have clearly eased many of these problems. Hadoop is an open source, Java-based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment. It is easy-to-use, inexpensive, flexible, and powerful enough to help process large volumes of data.

Hadoop, for all its strengths, is not free from imperfections. Businesses need specialized skills, data integration, and budget to factor into planning and implementation of Hadoop. Even when this happens, a large percentage of Hadoop implementations fail. This is because of the common Hadoop mistakes made by data scientists. What are these mistakes and how can you avoid them? Here’s a look:

1. Security breaches

High-profile data breaches have motivated the IT teams of most enterprises to prioritize the protection of sensitive data. Businesses may accidentally share the card and bank details, and personally identifiable information about their clients, customers or employees. If a business considers using big data, it is important to keep the security structure in mind while processing sensitive data about the customers and partners.

How to avoid:

  • Addressing each of the security solutions before deploying a big data project.
  • Planning ahead to decide who will be benefited from the investment and how it is going to impact the infrastructure.

2. Migrating everything before a plan:

Migrating everything to Hadoop without a clear strategy can result in long-term issues and expensive ongoing maintenance. With first-time Hadoop implementations, you can expect a lot of error messages and a steep learning curve.

How to avoid:

  • By considering every phase of the process (from data ingestion to transformation) beforehand.
  • Following a holistic approach and starting with smaller test cases.

3. Treating Hadoop as a regular database

One of the worst mistakes is to treat the data lake on Hadoop as a regular database like in Oracle, HP Vertica, or a Teradata database. The structure of Hadoop is totally different and wasn’t designed to store anything you’d normally put on Dropbox or Google Drive.

How to avoid:

  • By following a simple rule of thumb: if it can fit on your desktop or laptop, it probably doesn’t belong on Hadoop.
  • Taking proper steps up front to ingest data to get a working data lake, thereby avoiding a data swamp.

4. Buying cheap server hardware:

Hadoop is often talked about as being low cost due to the free open-source framework. Some businesses think that buying inexpensive hardware will do the trick. This is far from the truth as it always results in frequent node failures and other time-related losses.

How to avoid:

  • By buying quality server hardware, even if it is a bit expensive
  • With consistent monitoring and maintenance of the quality of the server hardware.

5. To assume that rational database skill sets are transferable to Hadoop

Hadoop is a distributed file system, not a traditional relational database (RDBMS). Thinking that you can migrate all your relational data and manage it in Hadoop the same way will result in nothing, but problems. If your current team lacks Hadoop skills, it would be best to provide big data Hadoop training to your existing employees rather than hiring new talent.

How to avoid:

  • By using new software, along with the right combination of people, agility, and functionality to make big data Hadoop successful.
  • By automating some of the more routine and repetitive aspects of data ingestion and preparation using tools available in the market.

The effective use of Hadoop has proven beneficial to a number of industries. Several premium institutions like Manipal Global Academy of Data Science are offering big data Hadoop training opportunities. A big data Hadoop certification is not only a skill set boost, but also a career boost.

What are some common Hadoop mistakes you’ve come across? Tell us in the comments section!


Manipal Global Academy of Data Science offers cutting-edge learning solutions in the field of data science. MGADS faculty comprises of academicians, data science experts, and IT professionals to guide you in today’s competitive environment. If you are keen on becoming a data scientist, MGADS will equip you to do the same.



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Nishant Desai

Nice blog. Thank you sharing such a nice blog on avoiding 5 hadoop mistakes. It was helpful & well written blog.



Thank you, Nishant!


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