Let’s understand what is Data Warehousing. Data Warehouse is a collection of software tools that help analyze large volumes of unrelated data. The goal is to derive profitable insights from the data. This course covers advanced topics like Data Marts, Data Lakes, and Schema amongst others.

There are various topics you going to learn in this course. Those topics are Database Vs Data Warehouse: Key Differences, Data Warehouse Concepts, Architecture and Components, ETL (Extract, Transform, and Load) Process, Must Know Differences between ETL & ELT, what is Data Modelling? What are Conceptual, Logical, & Physical Data Models, what is OLAP (Online Analytical Processing): Cube, Operations & Types, MOLAP (Multidimensional Online Analytical Processing), What's the Difference between OLTP & OLAP? What is Dimensional Model in Data Warehouse, what is Star and Snowflake Schema in Data Warehousing, what is Data Mart, Types & Example of Data Mart, Know the Difference between Data Warehouse & Data Mart, what is Business Intelligence? Definition & Example, Process, Techniques, Tools & Examples of Data Mining, Difference between Data Mining and Data Warehouse, Difference Between Fact Table and Dimension Table, Difference between Information and Data.
A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.

In addition to a relational database, a data warehouse environment includes an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users.