Here’s How You Can Purge Big Data From Unstructured Data Lakes
Data in unattended data lakes eventually becomes murky with irrelevant and low-quality data. Use these tips to purge your big data or unstructured data.
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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. Sadly, the data in unattended data lakes eventually becomes murky with the data that has been degraded in quality or that is no longer relevant even after diligent upfront data cleaning.
So what do you do then at that point?
Let’s flick through some compelling tips you can use to purge your big data or unstructured data.
Utilize Data Cleaning Techniques Specifically Designed For Big Data
Unlike typical databases that store data for the same structure and format, the data lake repository stores different types of structured as well as unstructured data. The format and size of the file are not fixed when it comes to stirring data in data lakes. Every element of data is assigned a unique identifier and is attached to metadata that provides details about the data.
Developers working in an IT infrastructure can use tools such as Hadoop storage repositories to eliminate duplicates. Not only this, but they can also use other effective ways to monitor incoming data which is being ingested into the data repository for the assurance that no partial or full duplication of the existing data encounters. Data managers can use tools that suit their requirements for the assurance of data lake integrity.
Run Data Cleaning Operations in Your Data Lake Regularly
This can be as effortless as eliminating any spaces between running text-based data that probably have originated from social media, for example, Liver Pool and Liverpool both are the same. It is called the data trim function. Why? Because as the name suggests, you are trimming away unnecessary spaces to distill the data into the most compact form. It becomes simple to find and remove duplicated data once the trimming operation is performed.
Revisit Data Retention Policies and Governance Periodically
Indeed, regulatory requirements and businesses are constantly changing in this ever-evolving world. IT experts and developers should meet with their outside auditors and with the end business at least annually for the identification of changes. Also, the meetings will help them to identify how they influence data, and how hanging rules can affect big data retention policies.
Look for Duplicate Images
Images are not stored in databases. In fact, they are stored in files that can be cross-compared by converting every image file into the numerical format and then cross-checking between images. There is a duplicate file that needs to be removed if the exact match occurs between the numerical values of the respective contents of two image files.
All the tips mentioned above are effective and compelling to carry out data purging activities for big data. There would be no wrong to say that there are several reasons for doing data purging activity. Some of the reasons are as follow:
- For the assurance of agility in case of disaster activity.
- Through data storage is not very expensive, up-gradation of hardware is a cost-intensive activity
- Storage and retrieval is a problem as data is growing at an unpredictable and uncontrollable rate.
- Ever-expanding data leaves an adverse impact on the performance and efficiency of the business.
At the heart of it all, data purging is the most crucial activity to reduce DB maintenance by decreasing downtime, bringing down IT costs, increasing user productivity which results in a quicker database reporting process.
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