Data Lakes vs. Data Centers: More Than Just a Drop in the Ocean

With the rise of AI and big data, terms like “data lake” and “data center” are often used in overlapping discussions – but they refer to entirely different concepts. A data center could host a data lake, but beyond that, the two have little in common.

So why the confusion? Both play a role in managing and storing vast amounts of information, and as organizations scale their AI and analytics capabilities, the infrastructure and data management strategies behind them become increasingly intertwined.

Here’s a closer look at what a data lake is, how it differs from a data center, and why the distinction matters.

What is a Data Lake?

A data lake is a software platform that servers as a central repository for data. Typically, the purpose of data lakes is to host the various types of data that a business needs to manage. Data lakes can serve as a site for hosting structured data (like databases) as well as unstructured data (like videos or emails).

Data lakes became popular starting about a decade ago. At the time, most businesses that needed to manage or process data on a large scale relied on so-called data warehouses, which are less flexible because they can usually only support structured data. By offering a centralized place to store almost any type of data, data lakes facilitated diverse data management and analytics use cases.

Data lakes have evolved over the years, with some data lake platforms adding features designed to enhance data governance and security or streamline data processing. Still, the core purpose of data lakes – centrally storing data of varying types – remains unchanged.