Data Warehouse Tutorial | Data Warehouse Design | Data Warehouse Design and Implementation

Data warehouse is a collection of data. It has the following properties:
Subject-oriented: A data warehouse should contain information about a few well-defined subjects rather than the entire enterprise. A data warehouse stores data for a specific objective. Integrated: A data warehouse is an integrated repository of data. It contains information from various systems within an organisation.
Non-volatile: The data value of a particular instance does not change over a period of time.
Time-variant: A data warehouse can store historical data for analysis.

Now let’s look into the overall objective of the data warehouse.
The main points regarding the objective of data warehousing can be summarised as follows:
- Data warehouses provide easy access to data.
- It stores clean and correct data in a single place.
- It helps in data-driven decision-making.
- It has a consistent data definition.
- It allows faster retrieval of data.

There are three terms we have been using frequently in this module: database, data warehouse, and data lake. They are not similar terms, and they cannot be used interchangeably.
- Database: It is generally used when you deal with a limited set of data.
- Data warehouse: It is used when you deal with a large volume of data, historical data, and structured data.
- Data lake: It is generally used when there is a huge volume of data, no fixed schema, and when the data is in unstructured form.

Challenges associated with designing a data warehouse.
The three V’s that are the major factors to be considered while designing a data warehouse are as follows:
Volume: It refers to the size of data that has been collected over a period of time.
Velocity: It refers to the speed at which a huge volume of data has been processed.
Variety: It refers to the different types of data such as text file, HTML file, image, and PDF.
Considering these three challenges, data warehouses are designed with the following features mentioned:
Data warehouses are integrated with cloud computing tools, which enable them to handle huge volumes of data.
Today, data warehouses have tighter integration with ETL and data processing tools, which makes data streamlined and easy.
Data warehouses integrate with data visualisation tools such as Tableau and PowerBI, which enables data to be directly linked with data storage to front-end reports.
Data warehouses provide features to run machine learning algorithms and analytics.

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