11.10.2024
6 min read

Data Modeling Best Practices

The theme of dimensional data modeling is actual and essential for data collection. It involves organizing data with an approach that is easy to understand with precise analysis and reporting. This theme is still applicable. The only thing that has altered is that today’s data warehouse has several applications than merely analysis and reporting.
Data sciencemachine learning, and data engineering are a few of the emergent applications for big data stored in modern-day data warehouses or data banks. However, this alteration doesn’t need us to develop an entirely new approach to data modeling. Some tweaks in data modeling design can meet the extensive data requirements of today’s much-extended audiences.

Without a good data model, the data and business processes will be unorganized or disorganized.

Why is Data Modeling Important?

Proper data modeling takes time, so a big question might be why it’s important and worth all the effort? Let’s look at some of the direct and indirect benefits of data modeling.

The most obvious benefits of data modeling is that by planning how you are going to organize your data, you have the ability to improve performance, reduce errors and reduce the chances of designing things wrong and having to rewrite code. All of these things combined means you should save time and money in the long run.  

Some secondary benefits of data modeling are that the end result should make data analysis easier for everybody in your organization, which will allow for better insights that will help your business. For more information about Data Analysis you may apply to the course “Data Analytics Full Course”
Another benefit is that you can choose the best type of database for your workload, which will make life easier for you in the future because you won’t have to worry about scaling or performance issues.

4 Best Practices for Data Modeling

There are four principles and best practices for data modeling design to help you enhance the productivity of your data warehouse.
When about designing data models, there are four considerations that you should keep in mind while you’re developing in order to help you maximize the effectiveness of your data warehouse:

  • Grain
  • Naming
  • Materialization
  • Permissioning and governance

Grain

The most important data modeling concept is the grain of a relation. It defines what a single row represents in the relation. In a table like orders, the grain might be single order, so every order is on its own row and there is exactly one row per order. Or in users, the grain might be a single user.
At other times you may have a grain of a table that is more complicated. In this relation each order could have multiple rows reflecting the different states of that order (placed, paid, canceled, delivered, refunded, etc.).
Next stage is indicating the level of granularity at which the data will be kept. Usually, the least proposed grain would be the starting point for data modeling. Then, we should modify and combine the data to obtain summary insights.
When designing a new relation, you should determine the grain of the relation, then name the relation that the grain is clear. Finally, we should be sure that all of the columns in the relation apply to the appropriate grain.
By ensuring that your relations have clear, consistent, and distinct grains your users will be able to better reason about how to combine the relations to solve the problem they’re trying to solve.

Naming

Naming things remains a problem in data modeling. The ideal practice is to pick and adhere to a naming scheme.
We should utilize schemas to identify name-space relations, such as data sources or business units. For instance, you might use the marketing schema to hold the tables most relevant to the marketing team, and the analytics schema to store advanced concepts such as long-term value.

Materialization

As a data modeler one of the most important tools, you have for building a top-notch data model is materialization. If you create the relation as a table, you precompute any required calculations, which means that your users will see faster query response times. If you leave the relation as a view, your users will get more up-to-date data when they query, but response times will be slower. Folks from the software engineering world also refer to this concept as “caching.”
Depending on what data warehousing technology you’re using (and how you’re billed for those resources) you might make different tradeoffs with respect to materialization. In general, when building a data model for end users you’re going to materialize as much as possible. This often means normalizing as much as possible so that, instead of having a star schema where joins are performed on the fly, you have a few really wide tables (many columns) with all of the relevant information for a given object available.

In addition to renormalizing your data so that querying is faster you also get the added benefit of making queries simpler for end users to write. In my experience, most non-experts can adeptly write a query that selects from a single table, but once they need to include joins the chance of errors climbs dramatically.
To ensure that my end users have a good querying experience, I like to review database logs for slow queries to see if I could find other precomputing that could be done to make it faster. If an expensive CTE (common table expression) is being used frequently, or there’s an expensive join happening somewhere, those are good candidates for materialization.

Permissioning and Governance

Data modelers should be aware of the varying rights and data governance requirements of the enterprise. Working collaboratively with your security team to verify that your data warehouse adheres to all applicable regulations would be beneficial.
For instance, firms that deal with medical data sets are subject to HIPAA data authorization and privacy rules. All customer-facing internet firms should be aware of the EU General Data Protection Regulation (EU GDPR), and SaaS enterprises are frequently constrained in their ability to exploit client data depending on the terms of their contracts.
In addition to determining the content of the data models and how the relations are materialized, data modelers should be aware of the permissioning and governance requirements of the business, which can vary substantially in how cumbersome they are. You should work with your security team to make sure that your data warehouse obeys the relevant policies. For example, businesses that deal with health care data are often subject to HIPAA regulations about data access and privacy. Any customer-facing internet business should be worried about GDPR, and SaaS businesses are often limited in how they can use their customers’ data based on what is stipulated in the contract.

Data Modeling plays a vital role in designing data solutions. The data model is the blueprint for the persistent tier in the application. It is the basis for developing Data Access Layer (DAL), business layer and service tier components. When developing data-centered enterprise applications one has to create a robust data model to facilitate enhancing, migrating to future release and most importantly increasing performance.
Consider the user’s demands, plan and put efforts to create the data model that will best assist those planning. Once all the criteria match, you and your small-sized or enterprise-level business can expect your data modeling to bring substantial business value.

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