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Dimensional Data Modeling, or simply Dimensional Modeling (DM), consists of a unique set of techniques and concepts for designing a data warehouse. Dimensional Modeling primarily focuses on identifying and leveraging the key business process objectives to design and implement a fundamentally strong data model before adding more business processes. This is a sturdy bottom-up approach to data modeling.
The major objectives of dimensional data modeling are as below:
- It makes it easier to access the information in the database.
- Ensuring information is consistently present for access.
- Receptive to change and highly adaptable.
- Present information as and when needed, promptly.
- Protect all informational assets.
- Function as a trustworthy and authoritative foundation for better decision making.
On working with ETL systems, you may have noted that the conforming measures usually achieve consistency in delivering information. The timeliness offered by the ETL lifecycles also depends on the design of the same.
Dimensional modeling benefits
Here is a quick overview of some advantages of dimensions of data modeling.
- Standardization will enable easy and timely reporting across different areas of business.
- Dimension tables will help store the history of dimensional info.
- It will also help enable new dimensions without any major disruptions to the basic fact table.
- A Dimensional model will help store data in an easy-to-retrieve fashion once it is stored in the DB.
- In comparison with the normalized model, the dimensional data tables are much easy to understand.
- The data can be effectively grouped into various business categories.
Overall, the dimensional model is very flexible and understandable for any business. As this model is exactly on the business terms, so everyone knows the meaning of each dimension, fact, and attribute. The dimensional data models are optimized and deformalized for quick data querying, and relational DB platforms easily recognize this model to optimize the query execution and overall performance. Dimensional modeling of data warehouses will create a schema optimized for better performance, which means there are only fewer joins, which help minimize data redundancy. A Dimensional model will also help to boost query performance. It is also more denormalized and, therefore, query optimized. Above all, the dimensional model can also accommodate the changes easily. It is easy to add more columns to the dimension tables without affecting the existing business intelligence applications’ performance. For data warehousing support, providers like RemoteDBA can be of real help.
Why is modeling important?
The data engineers may be experts in querying a DB with SQL, but on the other hand, an end-user may not be having any knowledge of SQL. So, data modeling aims to build a data warehouse that is easy for all to fetch data quickly using simple and logical analytical queries.
Most of the businesses handling enterprise data may measure their operational efficiency by measuring different types of data. This data will help to capture the progress of real business activities. OLTP databases may record the transactions on time, like live streaming, but mostly centralized around the transactions. A data warehouse is different, however. It does not have to record the details at a transactional level. Instead, data warehouses need to have the facts across various criteria of the business. It should also aggregate the information needed to improve the business. In this case, redundancy can be ignored in the case of the warehouse.
Concept of multi-dimensional data modeling
The multidimensional data model is a unique model in data warehousing, in which data cubes represent the data. This will let the admins model the data in multiple dimensions and view it as defined by the facts and dimensions. Usually, multi-dimensional models are categorized around a central theme, which is represented by the fact table.
Need to maintain consistent grain
By ensuring consistent grain, you can ensure that the system correlates legitimately and can aggregate across the facts. However, it may not be possible all the time to have your data at an atomic level. To tackle this, experts use these two methods as below.
- Periodic Snapshot Fact Tables
Data is collected at regular intervals—for example, power consumption, inspection, and audit, etc. Periodic snapshots are taken and stored.
- Accumulating Snapshot Fact Tables
When the process is multi-step, you need to capture data in the given process’s entirety. Typically, the start, completion, and various milestones achieved are recorded. This is transaction-grained with many other measures too in between. Here, accumulating snapshot fact tables are used to get answers to complex questions.
Fact-location in a dimensional model
You must know how to query the data and filter the results to derive reliable business intelligence insight. This purpose is rightly served in the successful dimension model. Dimensions are made with a surrogate key, which is referenced by a foreign key. We can search the tables by searching for the dimensions which we look for. For example, all related data, the timestamps, store location, customer agents, product info, and customer details, are turned into various dimensions that we can search for.
The notable advantage of dimensional data modeling is that the tables’ facts are not defined by the primary keys or any other unique identifier. But on the other hand, these are defined by a combination of different dimensions. So, it is crucial to ensure the uniqueness of the dimensions we set. When we query across the data facts, any duplicates in the combination of dimensions may result in disaster.
There are various hierarchies to follow while setting dimensions as a single hierarchy, multiple hierarchies, etc. The time dimension is a difficult thing to get done perfectly. Even the time-series DBs may not help you well in setting hierarchies if you have a messed up ETL. There may also become occasions where one dimension is dependent on another. In those cases, the designers may have to put a foreign key from one to another. This constitutes a typical outrigger dimension, which is very common in calendar dimensions.
Now, you have a basic idea about dimensional data modeling, which needs to be further explored in light of your enterprise database requirements to get a more streamlined data warehouse design.
