Prior knowledge of a warehouse is that it is used for storage. A data warehouse is not any different. This piece would enlighten you on what a data warehouse is and what category of people need it.

What is a data warehouse?

A data warehouse is a particular type of database, which focuses on a very specific application: storing, filtering, retrieving and analyzing huge volumes of information.

The data warehouse is a big database, where you take all your data from different systems and files and put them into one place.

It aggregates structured data from across an entire organization. It pulls together data from multiple sources and then selects, organizes and aggregates data for efficient comparison and analysis.

Uses of data warehouse

Most organisations employ the use of a data warehouse for mainly 3 purposes:

  1. Validation: Community users of data could use a data warehouse to confirm the credibility of the data in use.
  2. Tactical report: Tactical reporting is where the user community uses the data for purposes to reach a specific end.
  3. Exploration: Exploration is where you search for ideas or knowledge that you did not know before.

How is data analyzed using a data warehouse?

From a data analyst's point of view, a data warehouse is just an organized database to pull data ready (or near) for analysis. Once you do your query and retrieve your data you use another tool such as Python, R, Excel, or Tableau to do the actual analysis.

Who needs a data warehouse?

A data warehouse, although important, would only be relevant if the need arises. This segment discusses the category of people that would need a data warehouse.

  1. If you analyse data from different sources.
    Usually, organisations are segmented into departments. If you need to analyse data obtained from different departments, you will need a data warehouse to serve as a unified storage platform for easy access and analysis.

2. If you need to segment a large chunk of data:
A data warehouse would be important for you to correctly segment your data in an organised way.


3. If your original data source is not suitable for querying:
If you have large raw data ingested from different sources, they may be difficult to use. A data warehouse would help to process your data for further use.

Types of data warehouse

Data warehouse is segmented into 3 types

  1. Enterprise Data Warehouse (EDW)
  2. Operational Data Store (ODS)
  3. Data Mart

Enterprise data warehouse (EDW) is a centralized warehouse that offers a unified approach for organizing data and classifying data according to the subject.

Operational data store (ODS) is a warehouse used for reporting data from the Enterprise data warehouse. It is refreshed in real-time, making it preferable for routine activities like employee records. It is a complementary warehouse to EDW.

Data Mart is a subset of a data warehouse- it is streamlined to a specific subject which makes it easier for one to find something without going through the entire data warehouse.

FAQs about Data Warehouse

Q: What is the difference between a database and a data warehouse?

A: A database is a management system for your data. It could be an excel file. A data warehouse is a type of database where your data is specifically organised for analysis, queries or any other type of use. It is safe to say then that a data warehouse is built on a database and could be a subset of a database.

Q: What is the difference between a data warehouse and a data lake?

A: Data Lakes embrace and retain all types of data, regardless of whether they are texts, images, sensor data, relevant or irrelevant, structured or unstructured. Unlike a data lake, data warehouses are quite picky and only store structured processed data. Data lakes are useful for those users who are looking for data to access the report and quickly analyze it for developing actionable insights. A data warehouse, on the contrary, supports only a few business professionals who can use it as a source and then access the source system for data analysis.

Q: What is the difference between data mining and data warehousing?

A: Data warehousing generally refers to the combination of many different databases across an entire enterprise. Data mining on the other hand refers to the analysis of data. It involves an automated process of analysing large sets of data to extract meaning from it by finding hidden patterns and the relationship between the datasets.`

Conclusion

  • A data warehouse is a unified type of database that has data ingested from different sources for further use.
  • Data warehouses are generally classified into 3; Enterprise Data Warehouse (EDW), Operational Data Store (ODS) and Data Mart.
  • A data lake accepts every type of data-processed or raw whilst a data warehouse accepts processed data.