Dimensional data modelling or dimensional modelling (DM) is a technique that has been structured by Ralph Kimball. Using this technique, the data structure is optimized to store it in the data warehouse.
Dimensional data modelling is used to ensure that data can be retrieved faster due to optimization. There are dimensions and facts tables in this concept.
Objectives of Dimensional Data Modelling
Ross and Kimball stated certain objectives that would be included in Dimensional Data Modelling best practices.
- To ensure that information can be accessed easily
- To provide consistent information
- To adapt and receive change
- To provide information within a deadline
- To protect the assets of information
- To provide a trustworthy foundation to business and enhancing their decision making
- To get acceptance of the system from VIPs
The ETL development process focuses on providing consistent information through different measures and in a time-bound manner. This information is provided by different ETL cycles. In addition to this, the design of the ETL also affects the adaptability to a great extent.
Why Do You Need Dimensional Data Modeling?
A data engineer like you is quite proficient in SQL and would be able to write different SQL queries. However, the end-user may not have the idea to do the same. That is why it is necessary to build up a data warehouse that can help analysts get the fast and in an efficient manner.
What Do You Want Your Analyst to Avoid Doing?
There are many things that even you, being a data engineer, would not want to do. Hence it is unnatural to expect your analyst to do those things. Hence, below we have mentioned some of the things that your analyst should avoid doing.
- Do queries with the help of ID
- Cascading the outer joins
- Conduct sub-queries both group as well as joined
- Recursive sub-queries
- Joins without the help of PK or FK
- Sub-query correlation
What Do You Want Your Analysts to Do?
As a data engineer, there are certain things that you would expect from your analyst. To ensure that the work is done smoothly you will have to ensure that your expectation reaches the analyst.
- Simple joins
- Simple aggregation
- DISTINCT
- Build columns which include comprehensive texts as well as names
- Functions which are analytically windowed
The system used by you should be capable of going through all of the queries that are mentioned above. Moreover, the objectives mentioned above will help in transforming the OLTP database into dimensions and facts.
Transition to Facts from the Transactional Database
The most important aspect of a business is its efficiency and their success. Both of these parameters in current times are measured using some data.
This data showcases the progress as well as the activities of the business. The data that is used to measure this is termed as facts.
Databases that are OLTP oriented will record transactions similar to an event streaming, but they will be more centralized. On the other hand, the database warehouse is quite part of this. There is no data recording at a transactional level when it comes to the data warehouse.
The major parameters for a data warehouse are facts that are related to different aspects of your business. Data warehouse would require the analyst to aggregate whatever information is necessary to improve the business. Therefore, redundancy is something that is similar to a crime when it comes to the data warehouse.
Knowing About Measures and Their Need in the Facts Table
In the context of a data warehouse, the measure is a parameter using which calculations are done. Facts that are derived from the data stores are available with certain additional data which are included in the analysis.
These areas of the derived facts help the analyst as well as the executive who is viewing this analysis to find the values in these facts.
Need for Maintaining Grain Consistently
It is necessary to maintain a very consistent grain so that you know that the system can correlate as well as aggregate all the facts legitimately. However, sometimes it becomes impossible to get data at an atomic level. In such cases, you will need to follow two methods which will help in bridging this gap.
- Accumulating Snapshot Fact Tables
- Periodic Snapshot Fact Tables
Advantages of Dimensional Data Modelling
There are various advantages of dimensional data modelling which is why they are used by any database development company. Some of the advantages are as follows:
- Standardizing the dimensions makes reporting quite easy for the business
- Dimensional information will be stored safely in the dimension tables
- The dimensional table is easier than a normalized model in terms of understanding
- Data querying becomes faster due to the optimization of data in dimensional models
- The schema that is created through dimension modelling optimizes the performance. There are fewer joins and data redundancy is minimized to a great extent.
Endnotes
Dimensional data modelling is a much faster and efficient way to optimize data. This is a great model for most businesses and is highly adapted in current times. The ease of use which is provided by this model is a major reason for its growth.
In addition to that, dimensional data modelling follows a very easy and accurate process which is not hard to understand.
