Purge Big Data From Unstructured Data Lakes
Without a doubt, big data is becoming the biggest data with the passage of time. It’s going above and beyond. Here are some pieces of evidence as to why I said it.
According to the big data and business analytics report from Statista, the global cloud data IP traffic will reach approximately 19.5 zettabytes in 2021. Moreover, the big data market will strike a figure of 274.3 billion US dollars with a five-year compound annual growth rate (CAGR) of 13.2% by 2022. Plus, Forbes predicted that over 150 trillion gigabytes or 150 zettabytes of real-time data will be required by the year 2025. Also, Forbes found that more than 95% of the companies need some assistance for the management of unstructured data, while 40% of the organizations affirmed that they need to deal with big data more habitually.
Well, any organization would like to preserve its entire historical data accumulated over a time span for data analysis and mining. The performance of an IT infrastructure begins to deteriorate when data purging activity is not carried out periodically. This leads to the fact that purging activity is the most crucial aspect for infrastructures for the sake of performance tuning.
To run data purge against database records is relatively straightforward because the record stored in the form of a database is structured. Their data keys are easy to find and they have fixed record lengths. For example, the duplicate record will be discarded if there are two customer records for Ryan Jason. Similarly, one of the records will be discarded if the algorithm identifies that Ryan Jason and R. Jason are the same people.
However, data purge operations become more complex and complicated when it comes to big data or unstructured data. Why? Because of several data types such as voice records, images, text, etc. different types of data neither have the same formats nor lengths. such data do not share a standard set of record keys. On top of that, data has to be maintained for a long time span in some stances, for example keeping documents on file for legal discovery.
Several IT departments have decided to give up as they get overwhelmed with the complexity of coming up with sound data-purging decisions for data lakes that possess unstirred data. They maintain their entire unstructured data for an undetermined time span that ignites their storage cost and data maintenance in the cloud and on-premises.
Organizations have adopted data cleaning tools on the front end of data importation. These tools get rid of chunks of data which is incomplete, inaccurate, or duplicated prior to storing them in a data lake.
