Though there are real advantages to optimizing and structuring data formats, some would prefer to store data as-is without jumping through the processing hoops associated with optimization or structuring. For these data storage needs, a new trend in data management has popped up recent years called “data lakes.” In this piece, we’re going to look at what a data lake is and how data lakes are best utilized.
Pooling Your Data Into a Data Lake
Instead of sorting different types of structured and unstructured data into types of data storage, wouldn’t it be nice just to have one big place to keep it? This is possible with data lakes. A data lake is nothing more than a massive storage location where data can be kept as it exists — raw, optimized, structured, unstructured, semi-structured, it doesn’t matter. Like a lake with its many tributaries in the form of rivers and streams, an organization can establish various conduits for sending data to these much larger data lakes.
What Are the Benefits of a Data Lake?
The perks of using a data lake are somewhat realized in its essence — data lakes being large containers for all manner of data. In addition to the simple benefits, there are some other benefits of using a data lake the many don’t realize. Just like if someone wanted to assess the contents of a physical lake, the same is true with a data lake via very advanced analytics. Using a variety of algorithms, data lakes can analyze their contents for users. Another benefit of data lakes is their elastic scalability — being able to change as needed. Due to their need to be able to support various file types and sizes, the adaptability of a data lake can be appealing for many organizations as they expand.
What the Shortcomings of Data Lakes?
For all of their advantages, data lakes have had their fair share of critics. Because data lakes require fewer specifications for the data they contain, they are prone to abuse by users. When you’re able to store anything you want, as much of it as you want, data lakes contribute to grossly inefficient data management habits. If data isn’t properly maintained within data lakes, they can quickly become “data swamps” or even “data graveyards.” Data lakes have also been compared to museums with no curator. Data lakes can create vastly wasteful data usage habits that may bleed over into other sectors of business. The users of data lakes would need to put data management protocols in place if data lakes are to be utilized responsibly.
