Data warehouses and data lakes are both data repositories designed for housing vast amounts of data that traditional relational databases can't handle, but they differ in five main areas.
In this blog post, we'll explain the differences, but which one best fits your needs?
Let's find out together!
1. Data Types
Data warehouses store structured process data froma few specific sources, like transactional systems, operational databases and applications. Data lakes store both structured and unstructureddata from more sources, including sensors, websites, business apps, and mobile apps.
2. Purpose
Data warehouses store data ready for analysis, like in business intelligence, batch reporting and data visualization. Well suited for users with limited technical knowledge. Data lakes store big data analytics for machine learning, predictive analytics and data discovery, a good fit for data scientists and analytics experts.
3. Data Capture
Warehouses capture data from multiple relational sources, while lakes capture data from multiple sources that contain various forms of data.
4. Data normalization
Both data warehouses and lakes use denormalized schemas. However warehouses use schema on right while lakes use schema on read. Schema on write is their traditional one size fits all approach, but data being shared more and more between people with different roles and interests. More emphasis is being placed on the more flexible schema on read.
5. Benefits
Data warehouses store historical data from many sources in one place, and data is classified with the user in mind for accessibility ease. Data lakes retain data in its native format, which gives data scientists flexibility in data analysis and model development.
