How well do you know a data lake and its main business benefits?

When I tackled my first data lake project it was difficult for me to understand what it actually is. However, I finally grasped the concept by understanding that there are three types of data: structured, semi-structured and unstructured. In layman’s terms, a data lake is a data management solution that stores and processes raw data at scale, including data that does not fit well into SQL databases and data warehouses, which suit structured data. What sets it apart from other data management solutions is that storing data economically lets you answer business questions in future that you would otherwise have had no data for.

A well-implemented data lake brings many benefits. Analytics from new sources such as log files, click-streams, social media and industrial IoT can increase revenue by uncovering valuable insights, support better business decisions faster and bring budget savings. When data is all in one place, with storage cost optimised (AWS delivers automatic storage savings when access patterns change), catalogued and democratised (available to everyone), you can expect lower OpEx too. The benefits do not stop there. Cloud providers typically focus on security and governance features that reduce risk and help you meet demanding compliance standards.

Now that you understand what a data lake is and what the main benefits are, you can start preparing your business for reliable data analytics. While there are many use cases for data stored in data lakes, I am particularly excited about machine learning and AI.

In my next article, I will briefly overview data lake architecture on AWS.

For any data platform or AI needs, contact DataPhoenix at info@dataphoenix.io.