What database design patterns are most effective for ERP systems?

1 Star Schema

One of the most common database design patterns for ERP systems is the star schema, which is a type of dimensional modeling that organizes data into fact and dimension tables. A fact table contains the quantitative or measurable data, such as sales, orders, or transactions, while a dimension table contains the descriptive or qualitative data, such as customer, product, or location. The fact table is connected to multiple dimension tables by foreign keys, forming a star-like shape. The star schema is effective for ERP systems because it simplifies the data structure, improves query performance, and supports analytical operations, such as aggregation, filtering, and slicing.

2 Snowflake Schema

Another popular database design pattern for ERP systems is the snowflake schema, which is a variation of the star schema that normalizes the dimension tables to reduce data redundancy and storage space. A snowflake schema splits the dimension tables into smaller sub-dimension tables that are linked by foreign keys, forming a snowflake-like shape. The snowflake schema is effective for ERP systems because it eliminates data anomalies, increases data integrity, and facilitates data maintenance. However, it also adds complexity to the data structure, reduces query performance, and requires more joins.

3 Data Vault

A more advanced database design pattern for ERP systems is the data vault, which is a type of hybrid modeling that combines the best features of the star schema and the snowflake schema. A data vault consists of three types of tables: hub, link, and satellite. A hub table contains the unique identifiers or keys of the business entities, such as customer, product, or order. A link table contains the associations or relationships between the business entities, such as customer-order or product-order. A satellite table contains the attributes or details of the business entities or their relationships, such as customer name, product price, or order date. The data vault is effective for ERP systems because it enables fast data loading, supports historical tracking, and accommodates changes in business requirements.

4 Anchor Modeling

A newer database design pattern for ERP systems is the anchor modeling, which is a type of temporal modeling that captures the changes in data over time. An anchor model consists of four types of tables: anchor, attribute, tie, and knot. An anchor table contains the unique identifiers or keys of the business entities, such as customer, product, or order. An attribute table contains the attributes or details of the business entities, such as customer name, product price, or order date, along with their timestamps and version numbers. A tie table contains the associations or relationships between the business entities, such as customer-order or product-order, along with their timestamps and version numbers. A knot table contains the distinct values of the attributes or ties, such as customer names, product prices, or order dates. The anchor model is effective for ERP systems because it preserves the history of data, supports complex queries, and reduces data duplication.

5 Microservice Architecture

A more modern database design pattern for ERP systems is the microservice architecture, which is a type of distributed modeling that divides the system into smaller and independent services that communicate with each other through APIs. Each service has its own database that is optimized for its specific function and data, such as accounting, inventory, human resources, sales, or manufacturing. The microservice architecture is effective for ERP systems because it increases scalability, flexibility, and reliability, as well as enables faster development and deployment.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?