
You must make several smart decisions regarding technology, architecture, and design to create an effective data management system. Most technology needs can be met using modern cloud-based data platforms such as Snowflake Data Cloud, but you still need to ensure that the data structure and design fit well with your chosen technology and effectively meet all of your business needs.
You're in the right place if your organization opts for a data model. Data modeling is integral to the design process to ensure your data platform works effectively.
In this article, you will learn what a data model is and why choosing the suitable model is critical to data management. Ultimately, this blog will help you more confidently select and implement the appropriate data model for your data warehousing needs, including pros and cons and a comprehensive scorecard.
Data Warehouse Modeling: What Is It?
Creating and setting up data models in a data warehouse platform is called data warehouse modeling. The design and organization process involves creating the necessary databases and schemas to transform and store data in a way that makes sense to the end user.
When modeling a data warehouse, it is essential to consider unique access control and separation of environments. Think through which users should have access to specific databases, forms, tables, and views. It is also important to remember that although the development and production environments should be separate, their functions should be similar.
The Three Types of Modeling Used in A Data Warehouse
What exactly is data warehouse modeling? DBT has led to the popularity of three main types of data models. Each type serves a specific function and is organized differently in the data warehouse. We will discuss the purpose of these models and their place in the data warehouse.
Baseline Or Preparatory Models
Baseline models are predictions that are contained directly in the raw data. But now calls them preparatory models. They include the base forecasts and renaming of columns so that the models are consistent across data sources. They decide what type of timestamp to use, how to name date fields, what case to use in column names (snake or camel), and how to define primary keys in these models. Data analysts refer to the underlying models in their reports and dashboards, not the source data tables.
In this case, I'm changing the column names to match the snake example better. Not only do I change the naming convention to end with at, but I also change the date and time column to represent a specific type of date and time. The date and time column is formatted according to the date and follows a naming convention ending in at. This makes it easier for the reader of the model to identify the column containing the date and the column containing the date and time.
My data has been transformed, but its content has stayed the same. The base templates do not contain calculations, composites, or totals. The table must have the same size and number of columns as the source data table.
Intermediate Level Models
Intermediate models are essential when using a tool like dbt to model data transformation. Intermediate-level models are designed to reduce the execution time of data models and help analytics engineers solve problems in more complex models.
Intermediate models serve as a link between the parent and core models and sit in between them. They play an essential role in the transformation process, although they are usually only available to the analysis engineer who created them. They allow the same code to be executed once and then referenced so that it does not have to be repeated in multiple master data models.
These models do not reference either the source data or the underlying models. Analysts cannot use them as a tool rather than a source or core model for visualization. Fortunately, access to some models can be easily restricted, so they are not used in the final analysis or reporting by data-warehouses such as Snowflake, Google Big Query, and others.
This data model maps an anonymous session to a client ID. This mapping is necessary to understand which web sessions belong to which clients - or perhaps users who are not even clients. We want this model to exist independently, as it will likely be used in many essential data models.
Master Models
Base models create a fully transformed data set that business stakeholders and data analysts can use. They are the result of a transformation process. Base models rely on core and intermediate models to create the final dataset. These can be simple dimensionality matrices linking related primary models or complex data models with complex secondary logic.
To better understand the names of specific identifiers in the data, we link primary models to the corresponding underlying models for mapping. This data warehouse operation avoids the need for direct links between tables in reports and dashboards. Including it in the base model makes sense, as this is what a data analyst typically does to get the information they need!
Data Warehouse Modeling Results
When modeling a data warehouse, the primary, intermediate, and master models should be considered when designing the architecture. Master models are needed to protect the source data and define standard naming conventions for different data sources. Modular data models can be created using intermediate models as intermediaries between the primary and master models. The result of the transformations the data analyst applies is the base model.
Before starting the modeling process, it is essential to design and create databases and schemas for the data warehouse. In this way, meaningful models can be made that will provide practical information to business teams. Data warehouse models can change the data culture of an organization.