Maintain a tight bond with your friends, but maintain an even closer relationship with your digital entities
In the realm of data management, an Entity Relationship Diagram (ERD) serves as a powerful enabler for data consumers, democratizing knowledge that is often hidden within mature organizations. This visual blueprint is especially crucial in the design and use of a data warehouse, where it helps clarify the structure and connections of the data.
Visualizing and Organizing Data
ERDs provide a clear, graphical representation of tables (entities) and their relationships (e.g., one-to-one, one-to-many), offering a comprehensive, understandable map of data entities and their interactions. This visual representation is invaluable for data architects and stakeholders, aiding them in grasping the logical organization of the warehouse data.
Facilitating Design and Documentation
As blueprints during data warehouse design, ERDs guide the creation of tables and relationships with minimum redundancy, thereby helping to avoid design flaws and redundant data. They act as a guide for developers and database administrators, ensuring a well-structured and efficient data warehouse schema.
Enhancing Communication
By using standardized symbols and a visual format, ERDs help eliminate misunderstandings among various stakeholders. They provide a shared vocabulary and understanding of the data model, fostering effective communication between business analysts, developers, and database administrators.
Supporting Data Lineage and Usage Analysis
In modern data warehouses, ERDs can also incorporate inferred relationships from SQL query history, revealing how data is actually used and joined in practice. This insight into data flows and dependencies improves understanding of the data ecosystem.
Reducing Data Redundancy
By explicitly defining entities and relationships, ERDs help identify and eliminate duplicate data, which is essential for maintaining data quality and warehouse efficiency.
The Jaffle Shop Demo Project and Real-World Applications
The Jaffle Shop demo project by dbt is a fictional ecommerce business with two core entities: Customers and Transactions. Creating an ERD before building a data warehouse can help quickly understand the end-state data warehouse's structure and how to connect the dots in the semantic layer. For instance, in the Jaffle Shop demo project, Customers and Transactions have a One:Many relationship, with each Customer having multiple Transactions.
Real-world data warehouses often have exponentially higher complexity than the Jaffle Shop example, making ERDs even more important for understanding and navigating them. At YouTube, analysts needed to navigate across multiple entities, including Ads, Users, Countries, etc., to answer questions about the data. During his time at YouTube, a colleague noted the high cognitive overhead required to use the internal data warehouse correctly due to the complexity of the business, entities, and events it was mirroring.
When joining Mux in 2021, a crude ERD was created to understand the core entities and events in the data warehouse. Although it didn't account for newer products, invoices, viewer sessions, GTM systems, etc., the ERD still proved invaluable. It helped Mux build dbt models and LookML, resulting in a stable set of tables that reflect core entities and events.
In conclusion, an ERD is an essential tool in data warehouse design and operation. It serves as a comprehensive, understandable map of data entities and their interactions, supporting efficient architecture, communication, and maintenance of the data warehouse system. The time spent creating an ERD can be some of the most valuable in the data team's work.
