How to purge big data from unstructured data lakes


A recent study by TechRepublic revealed four steps to purge big data from unstructured data lakes.

Indeed, unstructured and big data are making data purge decisions and processed much more complex as they are so many types of data stored. However, data protection has now become vital for IT teams.

So as to purge your big data, the story suggests running data cleaning operations in the data lake by removing any spaces between running text-based data that might have originated from social media. Once this is done, it will be easier to find and eliminate data duplicates.

It also recommends checking for duplicate image files such as photos that are stored in files and not databases. By converting each file image into a numerical format, you can cross-check between images and if there is an exact match between two image files, then the duplicate file can be removed.

It was reported to use data cleaning techniques that are specifically designed for big data in order to remove duplicates in Hadoop storage repositories and monitor incoming data to make sure that no full or partial duplication of existing data occurs. Hence, data managers can use these tools to ensure the integrity of their data lakes.

Finally, the story suggests revisiting governance and data retention policies regularly as requirements for data constantly change. Having an annual meeting with IT to identify the changes and how data is impacted is necessary.