Building LDM-Centric Applications for Rapid Forward Engineering

The Eclipse Free BIRD Tools aim to leverage the rich, explicit BIRD Logical Data Model (LDM) — a cornerstone for building powerful solutions. Thanks to the LDM's detail and semantic clarity, our tools, code, and AI can deeply understand financial concepts and relationships at both broad levels (e.g. Derivatives) and very granular levels (e.g. Exchange Tradable Options).

This capability lets us efficiently autocreate implementation-focused solutions. Whether generating ETL processes, creating validation rules, producing test data, or deriving concise transformations, the LDM's structure supercharges these tasks.

By engineering solutions directly against the BIRD LDM, we can seamlessly propagate those solutions to any forward-engineered Input Layer. The key? A principled approach to Forward Engineering the Logical Datamodel that supports this automated adaptation — as demonstrated by the existing BIRD Input Layer models.

This extends the concepts of Forward engineering models (from logical to implementation focussed), to Forward engineering entire solutions.

Principled Approach to Forward Engineering

Forward Engineering translates one data representation into another — transforming the rich, hierarchical logical data model into a fully relational Input Layer. A 'Principled Forward Engineering' process ensures these essential capabilities, by following clear and transparent principles:

1. Structural Compatibility: Anything stored in the LDM can be captured in the Input Layer, respecting relationship cardinalities. For example, if the LDM can represent a term loan with multiple debtors, the Input Layer must too.

2. Hierarchy Capture: Hierarchical structures can collapse into a single table with enumerated discriminator columns (e.g. TYP_INSTRMNT), preserving original levels like TYP_INSTRMNT='other loans'. These enumerations drive both data representations and transformation logic downstream.

3. Structural Validation: Validation rules distilled from the logical model ensure structural consistency in the Input Layer. This keeps our validation concise and automation-ready.

4. Full Lineage: Generate metadata that traces each LDM attribute to its Input Layer column, preserving transparency and auditability.

With this principled process, we can confidently design solutions at the logical level and quickly roll them forward into implementation-focused systems — driving automation in ETL, validation, reporting, and beyond.