What is Data Modeling?

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Data modeling is the process of organizing and mapping data using simplified diagrams, symbols, and text to represent data associations and flow.

Engineers use these models to develop new software and to update legacy software. Data modeling also ensures the consistency and quality of data. Data modeling differs from database schemas. A schema is a database blueprint while a data model is an overarching design that determines what can exist in the schema.

Benefits of Data Modeling

Improved accuracy, standardization, consistency, and predictability of data
Expanded access to actionable insights
Smoother integration of data systems with less development time
Faster, less expensive maintenance and updates of software
Quicker identification of errors and omissions
Reduced risk
Better collaboration between teams, including non-developers
Expedited training and onboarding for anyone accessing data

Types of Approaches

There are four primary approaches to data modeling.

1. Hierarchical

A hierarchical database model organizes data into tree-like structures with data stored as interconnected records with one-to-many arrangements. Hierarchical database models are standard in XML and GIS.

2. Relational

A relational data model, AKA a relational model, manages data by providing methodology for specifying data and queries. Most relational data models use SQL for data definition and query language.

3. Entity-relationship

Entity-relationship models use diagrams to portray data and their relationships. Integrated with relational data models, entity-relationship models graphically depict data elements to understand underlying models.

4. Graph

Graph data models are visualizations of complex relationships within data sets that are limited by a chosen domain.

Types of Data Models

There are three primary types of data models.

1. Conceptual, defining what data system contains, used to organize, scope, and define business concepts and rules.

2. Logical, defining how a data system should be implemented, used to develop a technical map of rules and data structures.

3. Physical, defining how the data system will be implemented according to the specific use case.

Role of a Modeler

A data modeler maps complex software system designs into easy-to-understand diagrams, using symbols and text to represent proper data flows. Data modelers often build multiple models for the same data to ensure all data flows and processes have been properly mapped. Data modelers work closely with data architects.

Data Modeling versus Database Architecture

Data architecture defines a blueprint for managing data assets by aligning with organizational needs to establish data requirements and designs to meet these requirements.

Database architecture and data modeling align when new systems are integrated into an existing system, as part of the overall architecture. With data modeling, it’s possible to compare data from two systems and integrate smoothly.

Snowflake Data Cloud and Data Modeling

The Snowflake’s platform is ANSI SQL-compliant, allowing customers to leverage a wide selection of data modeling tools tailored to specific needs and purposes.

Snowflake has introduced several features enhancing data modeling capabilities.

Snowpark Enhancements: The Snowpark ML Modeling API, now generally available, allows data modelers to use Python ML frameworks like scikit-learn and XGBoost for feature engineering and model training within Snowflake. This integration simplifies the data modeling process by enabling direct operation on the data stored in Snowflake, reducing the need for data movement.
Advanced Analytics with Snowflake Cortex: The new ML-based functions for forecasting and anomaly detection provide data modelers with powerful tools to perform complex analyses directly through SQL. This simplifies the process of incorporating advanced analytics into data models, making it accessible even to those with limited ML expertise.
Developer Experience with Snowflake Python API: In public preview, this API enhances Python's integration with Snowflake, making it easier for data modelers to manipulate and interact with data within Snowflake using familiar Python constructs.

Learn more about the Data Cloud, or see Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial.