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Unlocking Insights: Exploring the Magic of Data Modelling for Effective Analytics

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The data tech stack in today’s environment includes a layer positioned between the visualization tool and the data warehouse. This layer, known as the data modelling layer, is invoked whenever we access data or utilize the visualization tool. Can you guess its purpose? It plays a crucial role in structuring the data. Numerous data modelling tools are available in the market, each offering unique features that simplify the tasks of data engineers and analysts. While some of these tools are open source, they require basic software knowledge to set up on your system.

Several popular data modelling tools provide features such as version control, automatic documentation and wiki creation, as well as automation and data constraint testing. Nowadays, visualization tools also incorporate some of these functionalities. Although data modelling within visualization tools may not encompass all the aforementioned tasks, it proves valuable in creating different views tailored to the specific needs of different organizations. Essentially, it allows the reuse of written SQL code.

Understanding Data Modelling

Let’s delve into data modelling:

What are data Models?

  • Data models are essentially SQL queries written on top of your data source, whether it’s a data warehouse or a data lake. Whenever these models are called, the SQL queries are executed, fetching the latest data and dynamically generating results upon each reload. These models can then be utilized to generate reports and visuals for dashboards.

What can you do with data models?

  • In data modelling, you can merge multiple tables and store data in various formats, ranging from transaction-level data (i.e., non-aggregated data) to aggregated data for a specific fiscal year or quarter. It offers high customizability based on the intended data consumers.
  • Moreover, data models can be utilized within other models. Yes, you can call a data model inside another data model or create subsets from existing data models, allowing for the creation of complex network structures within these models.
  • Another approach is establishing relationships between models, where models function similarly to tables, enabling you to join these tables on any desired column. This simplifies the process of connecting models without the need for writing extensive code to fetch simple data repeatedly.

Difference between Data Models and Tables?

  • Remember, data models are not tables that store data; instead, they are queries ready to be executed and retrieve responses from the database. Data privacy is not compromised as the data is cleared either on a cache or session basis and not stored anywhere.
  • You can compile multiple data models within a dataset, which encompasses all the required data columns, serving as a comprehensive solution for all data and dashboard requirements.


At Vindiata, we specialize in providing these systems from the base level, which is raw data itself. If you feel it’s time for your organization to make the jump to self-service BI, make sure that all the above cases are met, and if you need help and support, you can contact us as to see how we can help you achieve your goals of realizing self serve BI.

This blog is the third iteration of the visualization series. If you have not visited the first two blogs, feel free to give them a read on the links below.

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