A data lake is a place to store your structured and unstructured data, as well as a method for organising large volumes of highly diverse data from diverse sources.
The data lake tends to ingest data very quickly and prepare it later, on the fly, as people access it.
A data warehouse collects data from various sources, whether internal or external, and optimizes the data for retrieval for business purposes. The data is primarily structured, often from relational databases, but it can be unstructured too.
Primarily, the data warehouse is designed to gather business insights and allows businesses to integrate their data, manage it, and analyze it at many levels.
The Differences Between Data Lakes, and Data Warehouses
Data lakes, data warehouses and databases are all designed to store data. So why are there different ways to store data, and what’s significant about them? In this section, we’ll cover the significant differences, with each definition building on the last.
The Data Warehouse
But the data warehouse is a model to support the flow of data from operational systems to decision systems. What this means, essentially, is that businesses were finding that their data was coming in from multiple places—and they needed a different place to analyze it all. Hence the growth of the data warehouse.
The Data Lake
Although databases and data warehouses can handle unstructured data, they don’t do so in the most efficient manner. With so much data out there, it can get expensive to store all of your data in a database or a data warehouse.
In addition, there’s the time-and-effort constraint. Data that goes into databases and data warehouses needs to be cleansed and prepared before it gets stored. And with today’s unstructured data, that can be a long and arduous process when you’re not even completely sure that the data is going to be used.
That’s why data lakes have risen to the forefront. The data lake is primarily designed to handle unstructured data in the most cost-effective manner possible. As a reminder, unstructured data can be anything from text to social media data to machine data such as log files and sensor data from IoT devices.