How do you map your logical data model to your physical data model?

There is one additional consideration that should be covered in the logical model. Business rules such as default values may be included if they are not part of the metadata that is available to the team implementing the physical model

Logical Data Model consists of Entities, Attributes and Relationships. Physical Data Model consists of Tables, Columns and Referential Integrity. Logical Data Model is of semi-business and semi-technical. Physical Data Model is of technical. One Entity (of LDM) can be converted into one or more tables in PDM. Physical Data Models have surrogate keys introduced;

In business, there are three key personas: ? Executives ? Architects: Translate Executives’ vision into technical structure. ? Engineers: translate Architects’ structure into solutions. The three levels of data models (conceptual, logical, and physical) facilitate communication between these personas. - Conceptual model describes the core concepts and business rules of an organization and their meanings, Its main use case is to ensure that the CDO, CPO, and CTO understand the exact business requirements. - Logical models show the rules that govern data and the relationships between data objects. - Physical models represent the translation of the logical data model into the physical structure of a particular database or application.

Remember that logical and physical data models can have similar diagram; but can happen it doesn't. In begin of business, they may be are same, and that's ok! (depent of database paradigm) So depend of performance needs, you will need to add several data indexing, data partitions, data sharing, passing single data base to cluster data base, use enum domain in column to represent tables and so on; give you a separation in both diagrams: logical data modeling is updated from semantic knowledge structure of business, while physical data modeling is updated based support of performance and how reflect of entities with relationships defined by logical modeling from database.

The specifics of mapping a logical data model to a physical data model vary from DBMS to DBMS. MySQL offers choices of storage engines such as InnoDB and MyISAM, and specialized index types such as FULLTEXT. PostgreSQL stands out for its advanced data types and extensions, such as PostGIS, as well as its complex constraints. Oracle uses tablespaces for storage management, offers various types of partitioning, and offers advanced security features such as VPD and TDE. Microsoft SQL Server, on the other hand, offers filegroups for organizing database objects, partitioning options and optimized indexes for analytical queries.

Some benefitcs are: 1.- preparing the data team about that tools and techniques will need to learn to implement physical model. 2.- integrate other teams like data quality team or architecture team make sure topics they are interested: data quality team -> check constraints in tables; architecture/platform team -> implement a cluster database to use techniques like data partition or sharding in tables.

Who's your audience, if you don't know answer to this then don't waste effort creating this mapping. Most times your app developers and analysts happy with physical datamodel diagram that helps them run queries in db. Mapping between business friendly names/glossaries to physical attributes maybe the need of Enterprise data catalogue program that is steered by Data governance managers and happens throughout the application lifecycle not just during design phase. There's a lot of traction in this space with all leading data vendors adding the cataloging capabilities in their product suite like Azure purview, Databricks Unity catalog, Snowflake polaris and Collibra

I have found considering "metadata as data" a useful approach to translating logical data models to physical ones. A well-formed abstract entity framework (e.g., dbdiagram.io's DBML, an abstract entity JSON schema) has many benefits: - portability: - makes possible future migrations easier - allows rapid prototyping for alternative storage engines - version control of the logical data model - easy delivery to visualization services to represent the data model - compatibility with other abstraction libraries like SQL Alchemy for Python - efficiency: helps conceptualize and iterate on the model logically while feeling confident it will materialize in whatever storage engine you have implemented

Mapping or converting logical data model to physical data model is done typically through normalization. It is not always straightforward that entities get converted to tables. An entity may become more than one table in the physical data model. Attributes get converted into columns in the tables. A predefined naming conventions would help on good metadata. Depending on the business requirements and context, the relationships need to be defined. Many-to-many need to be taken care of. Physical data model defines the data types, nullability, primary key possibly with auto-generation, range of accepted values, format (especially for date columns) constraints, indexes, default values and length. Consider denormalization for performance.

When transitioning from logical to physical data model, it’s essential to approach the steps with a flexible mindset. While normalization helps reduce redundancy, there might be cases where denormalization is necessary for performance reasons, especially in systems that require fast read operations. Additionally, consider the database’s storage and retrieval characteristics—columnar databases, for instance, might require different physical modeling approaches compared to row-oriented databases. It's also important to iterate on your model as you gather performance data; what works well in theory may need adjustments when tested with actual workloads. While optimization is key, always balance performance enhancements with maintainability.