One of the key elements of success in big business is the proper handling and manipulation of big data – the voluminous amounts of digital information that inundates an enterprise in its day-to-day operations. By handling big data correctly, a business can not only determine the root causes of failures in their business model, processes, or products and services, they can also determine the steps they need to take in order to perform better and to grow. One such way of handling big data that is currently proving to be popular and revolutionary is the use of data lakes. But just what is a data lake, and how does it benefit a company?
A data lake, explained in the simplest way, is an ever-growing repository of vast amounts of raw data. Everything and anything that a business deals with is stored in that data lake. All forms of data, whether internal or external, whether from your employees, partners, customers, or competitors—all of them can be stored in that data lake, structured just enough so that it can be queried for a specific piece of information stored inside and it can then return that data in its raw, unprocessed form.
If this sounds like a bit of a messier type of data management—like a disorganized data warehouse—that’s not a completely wrong take. However, a data warehouse approach to big data means that all data within it is already pared down to fit its rigid structure and criteria – a process that could have removed from the incoming data other pieces of information that a business owner may not need now, but may do so in the future.
Through a data lake approach, on the other hand, all data is preserved in their raw, unstructured form, allowing data analysts or processors to examine them and link them to other sets of data within the data lake through their intact attributes. This helps them find unique solutions to a business’ problems or issues that they would not have otherwise been able to in a data warehouse setup.
Some other benefits of a data lake to a company include:
- Affordable scalability. Data lakes are far cheaper to maintain and expand than structured and organized data warehouses, due to the fact that the data going in do not need to be processed and organized extensively before they is stored.
- Data hoarding. In a data lake, all data is preserved in their natural state, which means that even though a particular data set may not be relevant to a business use now, They can be stored for the future, in the event that they do actually become relevant.
- Data adaptability to new tech. Because the data in the data lake is raw and unprocessed, they can be adapted to new formats and structures quickly, as opposed to data already structured and processed, such as in a data warehouse.
Of course, data lakes need to be handled properly so that that they benefit a company rather than become a detriment for it. For one, all data inside a data lake must be tagged in some way or another, in order to allow efficient querying and prevent hours upon hours of data-digging. Second, due to the inflated size a data lake may inevitably balloon up to, processing changes made within the data lake and ensuring that those changes are processed without straining company resources is essential – so a prudent, resource-efficient change data capture method, such as log-based change data capture, should be utilized. Log-based change data capture, as the name implies, only deals with the change logs of the modified data rather than the data itself, which minimizes server load and thus speeds up performance.
Finally, as a data lake may have all sorts of data relevant to a company, it needs to have airtight data security—something that cannot be taken for granted, especially in this day and age of damaging data breaches.