ER - Entity Relationship

Enhanced Definition

Entity-Relationship (ER) modeling is a conceptual data modeling technique used to represent the structure of a database by illustrating entities (data objects) and the relationships between them. In the mainframe context, it is a fundamental tool for designing and understanding the data architecture of systems utilizing hierarchical databases like IMS DB or relational databases like DB2. In the mainframe context, an Entity-Relationship (ER) model is a high-level conceptual data model used to design databases, particularly **hierarchical** (IMS), **network** (IDMS), and **relational** (DB2) databases on z/OS. It visually represents the structure of data by identifying entities (things of interest), their attributes (properties), and the relationships between them. This modeling technique helps in understanding and organizing complex data requirements before physical database implementation.

Key Characteristics

- Entities: Represent real-world objects or concepts about which data is stored (e.g., CUSTOMER, ORDER, PRODUCT). In DB2, these map to tables; in IMS, they often map to segments.
- Attributes: Properties or characteristics that describe an entity (e.g., CUSTOMER_ID, CUSTOMER_NAME, ORDER_DATE). These map to columns in DB2 or fields within IMS segments.
- Relationships: Associations or links between two or more entities, indicating how they are related (e.g., a CUSTOMER places an ORDER). Relationships can be one-to-one, one-to-many, or many-to-many.
- Cardinality: Specifies the number of instances of one entity that can be associated with instances of another entity (e.g., one customer can have many orders, but an order belongs to one customer).
- Modality: Indicates whether a relationship is optional or mandatory (e.g., an ORDER must have a CUSTOMER, but a CUSTOMER may not have an ORDER yet).
- Diagrammatic Representation: ER models are typically visualized using ER diagrams (ERDs), employing notations like Chen's or Crow's Foot to graphically depict entities, attributes, and relationships.

Use Cases

- Database Design and Development: Essential for designing new DB2 tables, defining PRIMARY KEY and FOREIGN KEY constraints, or structuring IMS databases with appropriate segments and pointers.
- Application Development: Provides COBOL and PL/I programmers with a clear understanding of the data structures they will be interacting with when writing SQL queries for DB2 or DL/I CALLs for IMS.
- Data Migration and Integration: Helps analyze existing data structures to plan for data migration projects (e.g., moving data from VSAM files to DB2) or integrating data across different mainframe and distributed systems.
- System Documentation: Serves as critical documentation for mainframe applications, detailing the underlying data model, which is invaluable for maintenance, troubleshooting, and future enhancements.
- Business Requirements Analysis: Facilitates communication between business users and technical teams to accurately capture and model the data requirements for new or enhanced mainframe systems.

Related Concepts

ER modeling is foundational to database management on the mainframe. For DB2, an ER model directly translates into the logical and physical schema, defining TABLES, COLUMNS, PRIMARY KEYS, FOREIGN KEYS, and RELATIONSHIPS through Data Definition Language (DDL). For IMS DB, while hierarchical, ER diagrams help identify the parent-child relationships that form the basis of IMS SEGMENTS and their logical connections. COBOL and JCL programs then interact with these database structures; COBOL uses SQL or DL/I calls to manipulate the data, and JCL defines the execution environment, including access to DB2 plans/packages or IMS DBD and PSB libraries. ER models are often stored and managed within a Data Dictionary or Repository on the mainframe.

Best Practices:

Iterative Design: Start with a high-level conceptual ER model, refine it into a logical model, and then map it to a physical database design (e.g., DB2 DDL or IMS DBD/PSB).
Normalization (for Relational): For DB2 designs, apply normalization rules (e.g., 3NF, BCNF) to reduce data redundancy, improve data integrity, and optimize storage and retrieval efficiency.
Involve Stakeholders: Collaborate closely with business analysts, application developers, and database administrators to ensure the ER model accurately reflects business rules and technical requirements.
Thorough Documentation: Document all entities, attributes, relationships, cardinality, modality, and business rules clearly within the ER model to ensure long-term maintainability and understanding.
Consider Performance: While ER is conceptual, keep performance in mind during logical and physical design, especially for high-volume mainframe transactions, by considering indexing strategies for DB2 or segment sequencing for IMS.