Last Updated on 2022-07-14 by AlexHales
An organization can store enormous amounts of data that has been obtained from various sources and placed in a data warehouse, which serves as a central location. Meaning, data warehouses hold all of the crucial information that companies require to conduct analyses and obtain important business insights from that information. The data that underpins business intelligence (BI) activities ultimately ends up in the data warehouse.
Users may utilize data warehouses to execute logical queries, create precise forecasting models and spot important trends throughout their organization.
But how exactly is a data warehouse designed? Here’s the answer.
1. Outlining Business Needs
A data warehouse’s design involves the entire organization as it affects every aspect of the business. Since the data in the warehouse determines how effective it is, it is crucial to its success to match departmental demands and goals with the project’s overall objectives. Therefore, the overall query results will be incomplete if you can’t connect all of the sales figures with the marketing data. Marketing data is necessary in order to identify the most valuable leads.
2. Creating the Physical Setting
Development, testing and production are the three main physical settings that make up a data warehouse. These three settings will live on entirely different physical servers, simulating industry-standard best practices for software development.
3. Data modelling
It is the process of visualizing the distribution of data in the warehouse. It’s like a blueprint that helps build linkages between data sets, regulates naming conventions and establishes security procedures that support the broader IT goals. It also aids in the visualization of the relationships between data.
4. Selecting an ETL Solution
The technique users will employ to extract data from the present tech stack or existing storage solutions and load it into the warehouse is called ETL, also known as Extract, Transfer, Load. The choice of an ETL solution deserves close consideration. Since ETL handles the majority of the in-between work, selecting or creating a weak ETL process can ruin your warehouse as a whole.
5. Online Analytic Processing (OLAP) Cube
A user may come across OLAP if they are starting to build the database architecture from scratch or need to maintain their own OLAP cube, which is normally done by a data analyst. Users can easily analyze the data in their warehouse with the use of OLAP. These cubes are frequently used for reporting, but they also have a wide range of other applications.
6. The Front End’s Development
The aforementioned stages are the backend processes. Now it’s time to start the development of the front end. Front-end visualization is required so that consumers may quickly understand and make use of the outcomes of data queries. There are several products such as Tableau and Power BI available on the market that support visualization. One can also create a unique solution, but it may take a lot of work.
7. Optimization of Query
Optimizing the queries is a difficult procedure that is quite tailored to the particular requirements. However, there are a few guiding principles to go over. For example, one should make sure that the resources in the development, testing and production environments are all mirrored so that projects from one environment to another can be pushed without the server stalling.
8. Setting Up a Rollout
Before the warehouse goes live, it is important to consider some important elements like use cases, education and training. In many cases, it may take a week or two before the end users begin to use the warehouse features. Make sure that before the rollout is finished, they receive proper training.
These are the fundamental elements of a warehouse design. However, one must keep in mind that the company might have additional actions not covered in the list. Each data warehouse is unique. The aforementioned ways, precisely, should make it easier for users to comprehend some of the fundamental needs and procedures involved in building a useful data warehouse that offers practical benefits throughout all phases of the company’s operations.
Now get started as you are now prepared to create a data warehouse!