In this blog post, we are going to introduce the differences between the database vs the data warehouse, and the data lake. The most familiar term we used to hear is DATABASE. We rather hearing about the data warehouse or data lake. That's why in this article we are going to explain the most fundamental of these 3 terms and show how it's connected with one another.
It typically refers to a relational database such as PostgreSQL, Oracle, MySQL, etc. A database is designed to capture and record data in OLTP (Online Transactional Process). For example, when a business completes a transaction by selling an item it will save it in the database and the data has the ability to be live and real-time. Data in the database is stored in tables with rows and columns highly detailed. The database is also a flexible schema which means you can change the data to make it works as you need.
Data Warehouse is also a database but it's used for analytical processing OLAP (Online Analytical Process) which enable the ability to analyze huge amount of data. The most common process of getting the data and put in the data warehouse is by using the ETL (Extract Load Transform) mechanism. The data in the data warehouse is not getting direct from sources but whenever the ETL is executed. The data warehouse always has the historical data which is refreshed from the source systems. The data is summarised which enables the analytical process much faster, but you have to plan ahead how the data you want to be which is not as flexible as the database did.
There are some differences between Database and Data Warehouse as below
- Databases are designed for transactions while data warehouses are designed for analytics and reporting
- Databases data is fresh and detailed while data warehouses data is refreshed periodically and is summarized
- Databases perform slowly for querying large amounts of data and can slow down transactional processes. Data warehouses don't interfere with any processes and are generally faster.
The data lake is designed to capture raw data such as structured, semi-structured, and unstructured data. It can be any type of data pictures, images, documents, etc. AI and Machine learning are usually the use cases of how the data lake is being used. Mostly you will have extra work to make the data useful for modeling the model of your prediction/ analytics.
Between the database, data warehouse, and data lake, no one is better than the other it depends on what kind of data you have and what you want to do with it. Businesses and companies can have only one or all options.