A recent study done on around 100 data scientists’ states that only 48% of them are using Hadoop and others have found that working with Hadoop is slow and more time is required on basic data preparation with Hadoop. On contrary to this, an analysis done by CrowdFlower says that more than 3500 jobs for data science on LinkedIn ranked Hadoop to be the second most important skill every data scientist must possess. With so many stats and figures coming out the data scientists always get confused with what data science is and whether Hadoop stands as an important part of it or not. Here, you will get to know if learning Hadoop is required to become a data scientist or not.
Why data scientist should go for Hadoop?
Let’s say there is a job based on data analysis that would take approx 20 to 25 minutes to get completed. For the same job, if we double up the computers performing the operation, the same job will get completed in half time. This is where Hadoop comes into the picture. We can achieve linear scalability with Hadoop via hardware. The data can be first loaded into the Hadoop system and then whatever questions are there on data set can be asked.
Another important part of working with Hadoop is that the data scientist need not do mastery in the distributed systems as there is transparent parallelism offered by Hadoop. The data scientist just needs to write the code based on Java with Pig and Hive etc.
Hadoop as an important tool for a data scientist
When the data volume exceeds the memory of the system or in case business requires the distribution of data among many servers then Hadoop plays a major and important role. With Hadoop, data can be easily transported to different system nodes at a very faster rate thus providing higher efficiency to the data scientist.
Hadoop for the task of data exploration
More than 80% of the time of a data scientist is gone in preparation of data and data exploration is a very important and crucial part of it. Hadoop works really well with data exploration as it helps in figuring out all the data related complexities which are difficult to be understood by the data scientist. With Hadoop, they can store data as it is without understanding it and going deep into data exploration.
Hadoop as a data filtering tool
Based on the business requirements, the data needs to be filtered. With Hadoop, the data scientists can easily filter data subset and business problems can be easily solved.
Hadoop for data sampling
The way data is available is very complex; many times, similar type of data is grouped together, so it is not always possible for the data scientist to just deal with first 1000 records. Therefore, proper data sampling is required to be done to get a proper data view. Using Hadoop to sample data helps for data modeling and helps in trimming down the number of records.
Conclusion
Hadoop is widely used by data scientist to make the working with data easy and effective. So, it is always important to learn Hadoop to become a good data scientist. There are many online and offline institutes, who give effective Hadoop Training to the beginner as well as experienced candidates.
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