Data lakes and data warehouses are critical technologies for business analysis, but the differences between the two can be confusing. How are they different? Is one more stable than the other? Which one is going to help your business the most? This article seeks to demystify these two systems for handling your data.
What Is a Data Lake?
A data lake is a centralized repository designed to store all your structured and unstructured data. Further, a data lake can store any type of data using its native format, without size limits. Data lakes were developed primarily to handle the volumes of big data, and thus they excel at handling unstructured data. You typically move all the data into a data lake without transforming it. Each data element in a lake is assigned a unique identifier, and it is extensively tagged so that you can later find the element via a query. The benefit of this is that you never lose data, it can be available for extensive periods of time, and your data is very flexible because it does not need to adhere to a particular schema before it is stored.