Data Warehousing: A Summary

Data warehousing is an important part of data management and analytics. This article gives a brief overview of what a data warehouse is, how it works, and some key terminologies associated with it. By the end of this article, you will have a foundational understanding of data warehouses and their importance in business intelligence.

What is a Data Warehouse?

A data warehouse is a centralized storage that is used to store, manage, and analyze large volumes of structured data from various sources. However, it usually does not store semi-structured and unstructured data. Structured data has a pre-defined structure, like a table having columns and rows. This structured data is then further used for analysis purposes to uncover valuable insights and trends in order to help the business with decision-making.

Important Terminologies associated with Data Warehousing

Before learning how a data warehouse works, we should familiarize ourselves with some of the key terminologies related to data warehouses.

1. ETL (Extract, Transform, Load): ETL is a process that is followed to store data in a data warehouse. It involves extracting data from various sources, then transforming this extracted data into a clean & desirable format, and finally loading this transformed data into the data warehouse.
   
2. OLAP (Online Analytical Processing): OLAP is a data processing system used in databases. It is used where the purpose is to perform complex analysis of data and help in decision-making.
   
3. Data Mart: A subset of a data warehouse that is focused on a specific business department or region, which is used to provide analysis & insights focused on that specific department/region.
   
4. Schema: It is used to define the structure and organization of data. It also defines the relationship between different tables in the data warehouse.
   
5. Fact Table: A fact table is a central table surrounded by various dimensional tables in a star or snow schema. It is used to store quantitative data like measures, numeric values, etc.
   
6. Dimension Table: A table that is used to store descriptive attributes or dimensions related to the facts table and provides the context to users for analysis.
   

Steps Involved in Data Warehousing

The process of data warehousing includes the following steps.

1. Data Extraction: In this step, raw data is collected from different sources such as databases, CRM systems, etc. using different tools and technologies. This can be considered as the Extract in the ETL process.
   
2. Data Transformation: In this step, the extracted data is transformed before loading it into the data warehouse. This usually involves data cleaning, converting it into correct formats, removing duplicates and invalid data, etc. This is the Transformation phase in the ETL process.
   
3. Data Loading: The transformed data is then loaded into the data warehouse. This may involve creating tables, indexes, and other structures to improve data querying and retrieval. This can be considered as the Load phase in the ETL process.
   
4. Data Analysis and Reporting: Once the data is loaded, users can access the data warehouse through various tools such as SSMS, Teradata Assistant, etc., allowing users to run complex queries to fetch the data generate reports, and perform data analysis.
   

Conclusion

Data warehousing plays a very important role in modern data analytics and business intelligence. By creating a central repository of data from various sources, organizations extract valuable insights and make informed business decisions. Understanding the basics of data warehousing is essential for anyone looking to explore the realm of data analysis.