Snowflake Data Model: The Ultimate Guide

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.

Data Modeling VS Database Schemas

Aspect	Data Modeling	Database Schema
Definition	A high-level design process that defines what can exist in a database.	Physical implementation of the data model.
Focus	Conceptual to logical and physical data structures.	Defines tables, columns, indexes, and constraints.
Flexibility	Can be updated with new business requirements.	Schema must be regenerated after model changes.
Stored In	Design tools or model repository.	Meta Repository.

Role of a Data Modeler: Key Responsibilities

• 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.

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.

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.
  • For example, a customer table is related to the orders table in an e-commerce database through customer_id. This provides a one-to-many relationship, as one customer can have multiple orders.
• 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.

Features that Enhance Snowflake Data Modeling

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.

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.

Using 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.

Benefits of 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.

Star vs Snowflake Schema

Star Schema	Snowflake Schema
Hierarchies for the dimensions are stored in the dimensional table.	Hierarchies are divided into separate tables.
It contains a fact table surrounded by dimension tables.	One fact table is surrounded by a dimension table, which is in turn surrounded by a dimension table
In a star schema, only a single join creates the relationship between the fact table and any dimension tables.	A snowflake schema requires many joins to fetch the data.
Denormalized Data structure and queries also run faster.	Normalized Data Structure.
High level of data redundancy	Very low-level data redundancy
The single Dimension table contains aggregated data.	Data Split into different Dimension Tables.
Cube processing is faster.	Cube processing might be slow because of the complex join.

Learn More About: Database Management with Schema Change Snowflake

Conclusion

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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