Treasure Box
The Treasure Box
The Treasure Box is a garbage godown for some but for others, it’s a treasure box full of information. The Treasure Box of information is popularly known as Database.
Just like creating a plan for a house we need to design and create a design from that Database. We often use databases for storing and retrieving data, ETL processing, reporting and analytics, and many such functions. To do all these activities in a better and optimized manner, the underlying database must be designed and created in a suitable fashion.
As a part of data modeling, the key factor one should keep in mind is the primary purpose of the database which will be created. Then comes the other key elements like the granularity of the data, data volume, how large the userbase is, read/write ratio of the data, etc.
The general data modeling techniques involve the Hierarchical model, Relational model, Network model, and Object-oriented model and Entity-Relationship model. Among these, the Entity-Relationship model is the one most widely used. It is most recommended when there are huge data volumes used at the Enterprise level of implementation.
During the design of the data model, one must have a clear understanding of the end-goals and results. A data modeler must need to know the needs of the enterprise correctly. By knowing the needs of the business, data can be prioritized and discarded.
The best approach is to keep it as simple as possible as the data can get complex as it grows. More and more datasets can be introduced once the initial models gain accuracy. This will help to identify any inconsistency during the initial stages. Once it is eliminated, then it is easier to add more data sets linking them to the existing ones.
Segregating the data based on facts, dimensions, filters, and orders makes the data modeler’s job easier. This will help in analyzing the data quickly and only keeping what is needed. It looks very easy to store huge data especially in this digital world but it will result in poor performance of the system.
Do a double-check at each small stage else it will result in a re-work of the entire thing. Every single attribute must be chosen carefully during the design. Modify the model as per business requirements and let the model evolve.
As and when the model switches gears between conceptual, logical, and physical try to incorporate business data needs as well in it.
Forward engineering and reverse engineering play a greater role here especially when amendments are needed on an existing model. Normalize to avoid data redundancy at the logical stage.
Erwin Data modeler and ER Studio are the commonly used tools for data modeling. The better the data model, the easier and better the data processing would be at any given point in time.