Database v/s OLTP v/s DataWarehouse v/s OLAP

Abhilash Marichi
2 min readJan 31, 2021

I have seen many not having clarity on the difference between the differences. So here is a quick and easy explanation.

“Datawarehouse is a system thought could be built using a Database technology”

A database (DB) is an organized collection of data, generally stored and accessed electronically from a computer system.

A data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is considered a core component of business intelligence.

Database technologies like Oracle, MS SQL, My SQL could be used to build a Data Warehouse. But there are database technologies built to efficiently implement a Data Warehouse like Teradata, Amazon Redshift, etc.

“Traditional databases were built with a focus on OLTP”

A Relational Database is simply a place where data can be stored. It is stored as tables with multiple rows and columns.

It is up to the engineers to build either an OLTP system or a Data Warehouse on top of it.

Most people use OLTP (Online Transaction Processing) and Database as synonymous but they are not, they consider it so because most of the traditional databases were built to be efficient for OLTP. The focus of an OLTP system is to efficiently perform the operations such as Insert, Update, and Deletion of data in a table.

And most people think Data Warehouse and OLAP (Online Analytical Processing) are synonymous but they are not. A Data warehouse is a design implemented on a Database to make querying (selecting / reading ) and analyzing the data faster.

OLAP (Online Analytical Processing) is the set of operations that could be performed on the data stored in a data warehouse like slicing, dicing, pivoting and drilling of the data.

I hope I have given you the fundamental differences between these four terminologies. If you want to learn more on any of these topics then please let me know in the comments!

--

--

Abhilash Marichi

Data Engineer at Amazon. I write about Data, Product & Life.