Data modeling is a crucial step in the data warehouse design process; it involves analyzing data sources and establishing the relationships between them. It is particularly beneficial in conceptualizing and visualizing data models based on business requirements. The primary objective of building a data model is to understand how data will be collected and stored within a data warehouse.

Platforms like Snowflake can help modernize your data warehouse using multiple data modeling approaches. These approaches serve as abstraction tools representing the flow of data in the Snowflake data warehouse. Data modeling is also beneficial for summarizing, rearranging, and presenting your data in a high-level view to support data analysis.

Let’s look into the details of data modeling and how to build a Snowflake data model.

Understanding the Concepts of Data Modeling

Data modeling involves designing a data model to define how data is collected, organized, and related within a data warehouse or database. These data models provide a conceptual framework for understanding the data and its relationships using text and symbols. You can utilize the data models to create or update an application based on business needs.

What Makes Data Modeling Differ from Database Schemas?

Data modeling is a high-level, abstract design process that determines what can exist in the database schema. It involves translating a conceptual model into logical and physical data structures.

The physical implementation of the data model is the database schema, which defines tables, columns, indexes, and constraints and is stored in a metadata repository. If new business requirements arise, you cannot directly change the database schema. Instead, you must update the data model and regenerate the schema to align with the changes.

What Is the Role of a Data Modeler?

Data modelers are responsible for translating the design of a complex data warehouse into simplified diagrams representing the data flow. They build multiple data models for the same data to ensure that all the processes are mapped accurately. By choosing a data model that aligns with your business needs, you can use it as a blueprint to develop or re-engineer your data warehouse.

Data modelers collaborate closely with data architects to gather business requirements and translate them into a comprehensive data architecture plan. Then, they use this plan to build a data model tailored to meet the specific business requirements.

What Are the Advantages of Data Modeling? 

Here are the key benefits of utilizing data modeling within your organization: 

  • Data will remain consistent, standardized, and accurate across different applications.
  • You can integrate different data sources with less development time and effort.
  • You can identify errors and omissions in data models early in the development process.
  • Proper schema definitions, access controls, and integrity rules can significantly reduce the risk of data inconsistencies, security issues, and inaccuracies.
  • Better collaboration among cross-functional teams, including non-developers, promotes active participation in the data modeling process.

What Are the Primary Approaches to Data Modeling? 

There are three primary data modeling approaches:

  • Conceptual Modeling: It involves creating a visual representation of the data entities and their relationships. A conceptual model is similar to sketching a floor plan of a new home to understand how the different rooms connect. It provides a simplified overview without getting into the specific details. 
  • Logical Modeling: It serves as a technical map of data entities and their relationships. Considering the above example of building a home, logical modeling provides a more detailed blueprint showing how all parts of the house will function together. 
  • Physical Modeling: It helps in designing internal schemas that define how data is actually stored in the database. Using the logical model, physical modeling is considered similar to actually building the home according to specifications.

What Are the Primary Types of Data Models? 

Here are the four primary data models used in many applications:

  • Hierarchical Data Model: In this model, data is organized in a tree structure with a root node connected to other nodes. Extensible Markup Language (XML) and Geographic Information Systems (GIS) widely use this model. The hierarchical model maintains one-to-many relationships between the nodes.

For example, consider a supermarket as the root node, with packaged food and pantry items as child nodes. This indicates that a supermarket can contain multiple products.

  • Relational Data Model: This model organizes data in tabular format and primarily uses SQL for data definition and manipulation. It is widely used for e-commerce, ERP, CRM, and HRMS systems.
  • Entity-relationship Model: This model provides a conceptual representation of real-world data entities and their relationships using Entity-Relationship diagrams.

For example, in a database with employee and department entities, both share department ID, representing a many-to-one relationship between them.

  • Graph Data Model: In this model, data is organized in a graph-like structure to represent complex relationships within datasets. A child node can have multiple parent nodes, facilitating many-to-many relationships. It is beneficial for applications such as social networking, recommendation systems, and fraud detection.

What Is Snowflake Data Modeling?

Data modeling is a fundamental concept in the Snowflake data engineering process. To support efficient data analytics in Snowflake, data modeling techniques are used to transform and integrate new data sources into the data warehouse.

Since the Snowflake platform is ANSI SQL-compliant, you can utilize various modeling tools based on your business requirements. With appropriate tools, data within the Snowflake data warehouse can be made consistent, standardized, and enriched to suit your business use case. This allows faster and more accurate decision-making in your organization.

What Are the Features That Help in Improving Data Modeling in Snowflake?

Several Snowflake features help in improving the data modeling approaches. Here are some essential features:

Snowpark ML Modeling API

This Snowpark enhancement supports feature engineering and model training directly within the Snowflake platform. It integrates with Python ML frameworks like XGBoost and scikit-learn. This functionality simplifies data modeling by allowing you to work directly with your data in Snowflake, minimizing the need for data movement.

Snowflake Cortex

Snowflake Cortex improves analytical capabilities by introducing new machine-learning functions for anomaly detection and forecasting. These functions allow data modelers to perform complex analysis directly using SQL. Cortex simplifies the integration of advanced analytics into data models, making it easier even for those with limited ML knowledge.

Snowflake Python API

The Snowflake Python API enhances Python’s integration with Snowflake; data modelers can utilize Python constructs for simpler data manipulation and interaction within Snowflake.

Let’s look into some of the advanced data modeling features in Snowflake:

  • DEFAULT Column Property: This feature allows you to set default values for columns in your tables. Unlike virtual columns that don’t physically store data, columns with the DEFAULT property can store the default value physically. This is enabled only when a column has a NULL value, such as when a new row is inserted without a specified value for that column. 
  • Creative Comment Usage: When transforming from a logical model to a physical database, valuable information such as cardinality definitions and relationship names can be lost. Using creative comments, you can add comprehensive details about entity relationships, cardinality, and data flow.
  • Metadata Functions: The Snowflake cloud services layer maintains the metadata for all database objects. As a result, you can define database objects, create users, and manage roles and permissions without requiring a virtual warehouse.

How to Build a Snowflake Data Model? 

Data modeling with Snowflake allows you to create and analyze diverse data structures for your data warehousing needs. Here are the primary models:

  • Dimensional Data Models: Utilized for building complex data warehouses.
  • Data Vault Models: Utilized within data warehouses to provide long-term storage for historical data while modeling.
  • Hybrid Models: Combines the benefits of dimensional and data vault approaches, overcoming the associated limitations for enhanced data warehousing.

What Is Snowflake Dimensional Data Models? 

Dimensional Data Models (DDM) in Snowflake store data using dimension tables and fact tables for efficient querying and analysis. While the dimensional table contains descriptive data about an entity, fact tables have numerical information. A fact table is linked to multiple-dimension tables through foreign keys.

Dimensional Data Model Example: Clinical Quality Reporting

In a clinical setting, dimension tables might include patient details, including PatientID, PatientName, PatientAddress, or PatientEmergencyContact. Other dimension tables might store health care provider information, such as ProviderID, ProviderName, Speciality, and Department. Similarly, many other dimensional tables store diverse clinical information.

The fact table stores data about patient encounters linking to dimension tables via keys like PatientID and ProviderID.

Snowflake Data Model: Clinical Quality Reporting Scenario to Understand Dimensional Data Model
Snowflake Data Model: Clinical Quality Reporting Scenario to Understand Dimensional Data Model

This is an example of a dimensional data model known as star schema. It is a top-down model used for data warehousing. It involves a central fact table connected to multiple-dimensional tables without normalization. Star schema uses fewer foreign keys and has high data redundancy.

Another dimensional data model Snowflake Schema, is also used for data warehousing. It is a bottom-up model with a fact table, dimensional tables, and subdimensional tables. Snowflake schema uses more foreign keys and has low data redundancy.

How to Use Snowflake Schema in Data Warehouse Model?

In the Snowflake schema, the fact table is placed at the center and connected to multiple dimensional tables. It further segregates the dimensional tables into logical subdimensions through normalization. When dimensional tables are normalized into multiple related tables, a hierarchical or snowflake structure is created.

While this whole process makes the data model more complex, it can simplify analysis for certain data types. The Snowflake schema also provides more storage efficiency because of the high normalization standards.

Let’s see how to implement a data warehouse model using Snowflake schema:  

In a sales data warehouse, the Product dimension table is normalized into a ProductCategory table. Both dimension tables are connected using the foreign key “ProductCategoryID”.

Similarly, the Employee dimension table is linked with the Department table using the foreign key “DepartmentID”. The Customer table and City dimension table are connected through the “CityID” attribute. This normalization reduces redundancy within the data warehouse and ensures data consistency.

Snowflake Data Model: Implementing a Sales Data Warehouse Using Snowflake Schema
Snowflake Data Model: Implementing a Sales Data Warehouse Using Snowflake Schema

What Are the Benefits of Using Dimensional Data Models in Data Warehousing?

Here are some reasons that Snowflake data warehouses are constructed using dimensional data modeling:

Hierarchical Nature of Data Flow

Dimensional models efficiently manage hierarchical structures, which is essential for in-depth analysis and comprehensive reporting. In the clinical quality reporting example, dimensional modeling starts with patient information and progresses through interactions with healthcare providers, medical procedures, and diagnoses.

Adapt to Business Changes

Dimension models offer the flexibility needed to adapt to business changes. This adaptability can help healthcare organizations stay compliant with regulations and integrate new data sources to improve clinical quality reporting.

Enhanced Business Intelligence

Dimensional models are highly compatible with business intelligence tools, allowing you to effectively visualize your data and generate meaningful reports. These BI tools can help identify trends, monitor performance metrics, and improve clinical quality.

What Is Data Vault Data Modeling?

The data vault modeling approach is designed for large, complex data integration. It is based on a hub-and-spoke architecture and uses the following three types of tables:

  • Hubs: A list of unique business keys representing core data entities. It establishes the primary keys around which the data vault model is built.
  • Links: A list of unique many-to-many associations between hubs. 
  • Satellites: Stores descriptive attributes and historical data for hubs and links. Satellites provide details about the business keys or relationships and record the history over time. 

Why Data Vault Modeling Is a Better Option for Data Warehousing in Snowflake?

In the clinical quality reporting (CQR) scenario, data accuracy and quality are essential. Any data quality issues, such as misleading data sets, could result in regulatory fines. Since the metadata in the Data Vault preserves the original data pattern, it enables easy tracking back to the data source when needed.

The data sources for CQR frequently change based on regulatory and operational requirements. However, dimensional models are less flexible and require extensive transformations before loading into the data warehouse. On the other hand, a Data Vault is closely aligned with the data source format, making it easy to audit and track data lineage. 

With Snowflake’s MPP compute clusters, adaptive data warehouse technology, and optimized storage format, you can achieve better results with Data Vault loads and queries than dimensional data models. Data Vault 2.0 is especially suitable for loading multiple Point-In-Time (PIT) tables simultaneously using a single join query.

Snowflake Data Model: Implementation of Data Vault Data Modeling 
Snowflake Data Model: Implementation of Data Vault Data Modeling 

What Are the Limitations of Data Vault Data Modeling?

Here are some challenges associated with Data Vault modeling:

  • High Complexity: Merging multiple tables can be challenging when developing Snowflake dashboards or performing data analysis, making the process increasingly complex.
  • Similarity with Dimensional Models: Data Vault incorporates Point-In-Time tables for ad-hoc querying and analysis. This setup can result in uncertainty as it vaguely resembles dimensional model fact tables attached to a data vault.

How Does a Hybrid Data Model Overcome Data Vault Data Modeling Limitations?

A hybrid data model is a bottom-up model mainly used for logical enterprise data warehouses. It merges the data vault and a part of the star schema to utilize the benefits of both dimensional and data vault modeling approaches.

Snowflake Data Model: implementation of Hybrid Model 
Snowflake Data Model: implementation of Hybrid Model 

The above CQR example shows an entity-relationship diagram of a hybrid model that includes a fact table from the dimensional model and all the entities from the data vault models. 

In this hybrid model, the data flows from left to right leverage the advantages of the data vault, while those from right to left use the benefits of the dimensional model. The satellite entity in the data vault serves as a satellite table or a dimensional table, depending on the model it’s connected to. 

Using Snowflake’s Materialized Views feature, you can establish a virtual dimensional model on top of the Data Vault. However, this approach may introduce additional overhead in data processing and performance. It also lacks the flexibility to incorporate new business rules and trace the data lineage.

As a result, the hybrid model would be a more effective solution. Here are a few advantages of a hybrid model:

  • Allows parallel loading of data, making it suitable for processing large volumes.
  • Metadata separation helps in flexible Snowflake data transformation even after the data is loaded.
  • Reduces complexity in data extraction and analysis for business applications.
  • Strengthens the connection between fixed dimensions and fact tables in the Data Vault, helping in troubleshooting and auditing.
  • Separation of structural and descriptive information improves flexibility and minimizes the need for re-engineering during updates.

How Does Hevo Data Reduce the Complexity of the Snowflake Data Model?

When your organization’s data is scattered around multiple databases, consider loading all the data into a Snowflake data warehouse designed using a data model. For easy integration, you can utilize a real-time ELT, no-code data pipeline platform like Hevo Data. It cost-effectively automates the data migration process to Snowflake. For more information on how to configure Snowflake as your destination, read the Hevo Documentation for Snowflake

Here are the key features of Hevo Data that help simplify the process:

  • Data Transformation: Hevo offers analyst-friendly Python-based scripts and drag-and-drop transformations. These options remove any inconsistencies in the data before loading it to Snowflake, reducing the transformation tasks required during the data modeling process.
  • Incremental Data Load: Hevo Data facilitates real-time data transfer, allowing continuous integration into Snowflake. This ensures that your data model for Snowflake remains up-to-date with the latest information from different sources.


The Snowflake data model provides a flexible approach for organizing and analyzing data within a data warehouse. This article highlights various data modeling approaches that contribute to modernizing your data warehouse.

The dimensional and data vault models offer several benefits, like transforming data, retaining the data pattern from the source, and tracking data lineage. However, when used separately, these models introduce complexities. A hybrid model offers an impressive solution by merging the benefits of both approaches.

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Share your experience of Snowflake Data Modeling in the comments section below!


  1. What are the Snowflake data model best practices for performance optimization and cost savings in self-service reporting with BI tools? 

A. Following are some best practices when using a Snowflake data model:

  • Use hybrid tables, when handling row-level merge, update, insert, and delete DML statements.
  • To reduce costs, utilize Snowflake only for data warehousing, leaving ETL tasks to a real-time integration platform like Hevo Data.
  • If your Snowflake tables undergo frequent manipulation, use normalized data and star schema.
  1. What are the different data modeling tools for Snowflake?

A. The different data modeling tools for Snowflake include ER/Studio,, and Tree Schema. For more information about the tools, read Snowflake modeling tools.

Customer Experience Engineer, Hevo Data

Dimple, an experienced Customer Experience Engineer, possesses four years of industry proficiency, with the most recent two years spent at Hevo. Her impactful contributions significantly contribute to refining customer experiences within the innovative data integration platform.

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