As a Data Science and big data analytics enthusiast I always wondered about the word “DATA WRANGLING” and then i decided to take a deep dive into the ocean of data wrangling….
As a Data science enthusiast I was aware about the term ETL, which mean Extract the data from one or several sources which are resembled form or may be in odd form and store on the desire system, where transforming the same mean cleaning the raw data and transform the data to the purpose oriented form and loading is simply the data insertion into required database or target system.
But here the question raised, If ETL is the solution since 1970 then what this Data Wrangling, If we go with the wikipedia then, wrangling means “Data Munging” and I am not going to talk on the munging right now. Wrangling is the process of transforming the RAW data into desired format. But the same is the ETL do.Then what is the difference between Data Wrangling and ETL.
After some research on the same i came to know that there are two major differences between the ETL and Data Wrangling…And That is People and Intension.
Data Wrangling is the Process of transforming the data with the intent of making it too useful as per the core purpose. Here the Data Wrangling is done by the people who are very near to the process or know the data very best like business analysts, Manager or Project Leader.
On the other hand ETL focused on Information Technology, Specially when the IT company get the order from the client then they start the work for workflow and pipeline project and uses ETL to give them the solution.
And here there is no verification of the data from ETL from the client(I will say Data Wrangler) but using Data Wrangling, he Client can also check the data.
Data Wrangling and ETL also works on different data. As we know big data is mostly deal with the unstructured data and ETL is best suited for the structured data where the Data wrangling tools can be used with the diverse and complex type of data and also it can used for the large scale too.
Where as the ETl is used from the 1970 and it is work fine with structured data like data from the traditional database.But if the Schema of the data is not defined then ETL will not work well.
In-short the process in which we clean, structure and enrich the data into the required format and data is prepared by the people who knows the data very well which gives us power to get the better decision within less time is called Data Wrangling.