Schemarise ALFA Models

Introduction

Once you have some background on Schemarise and ALFA, you may be interested in understanding how your data models are represented using ALFA.

The most effective way to explore this—short of viewing your own models—is to examine models that are already familiar to you, or that closely resemble those used within your organization.

ALFA supports the import of schemas from a wide range of existing formats. Enablement of a new source format can typically be completed in days or weeks, allowing organizations to rapidly leverage their existing modelling investments.

This site presents examples of publicly available models imported into ALFA, demonstrating how external standards and industry models can be represented in a consistent, governed, and executable form.

Unlike traditional data modelling, governance, ontology, or lineage tools, ALFA extends beyond documentation to provide a true end-to-end data architecture solution, as outlined below.

Capabilities at-a-glance

• Platform and language agnostic models, DQ rules, transformations, tests, metadata, APIs and business rules - versioned and capture in a single place ensuring consistent data standards across the organization.
• Generated into multiple languages for use as libraries by application developers - enabling true Model-Driven-Development.
• Run DQ rules directly against multiple data formats - JSON, CSV, XML, Database, Python Pandas, Apache Spark, Databricks.
• Data models packaged as Data Products by data domain owners, versioned and released, driving higher quality governance and data.
• Fully navigable documentation is automatically generated from the data model, enabling stakeholders to understand data structures and share knowledge effectively.
• Models can be extended with additional attributes and Data Quality rules aligned with EDM Council Data Quality standards.
• A versioned JSON-based data interchange format is provided by default, with binary formats also supported for selected target programming languages.

Imported Model Examples

You may be encountering these formats and models for the first time, or you may be an industry expert deeply familiar with them.

In either case, the value lies in having your organizational models represented in a consistent, transparent layout, alongside widely used industry models. This enables reuse where appropriate, visibility across stakeholders, and faster delivery of business applications.

• XML Schemas

  Multiple examples imported from ISO 20022, EMIR, LEI, FpML.

• JSON Schemas

  Example containing imported FIRE data model.

• FINOS Models
  • CDM (Common Domain Model) - Financial Products and LifeCycle
  • ISDA DRR (Digital Regulatory Reporting)

• Open Risk Engine - Platform for Risk Analytics and XVA

  Open Risk Engine models imported into ALFA.

• OpenGamma Strata - Analytics and Market Risk Models

  OpenGamma models imported into ALFA.

_{The imported models are shown for demostration purposes only. Their use as examples should not be considered an endorsement or recommendation.}

Showcase Data Models

• Interest Rates in Banking Book - IRBB, Model and Calculations

  A complete, worked example demonstrating:

  • Loading and transforming Position data into a Standarized model
  • Calculating IRBB on set of Trades, alongside tests to validate
  • Enabling a Medallion Architecture based approach
  • The code expressed in ALFA can be generated into Java, Spark, Databricks, Pandas and other platforms

• Capital Market Technology Organization

  An illustrative example showing how a financial institution can model its front-to-back data architecture, including:

  • Breakdown by business/technology function
  • Showcase how Data Products are consumed and produced
  • Clear view of data sharing and flowing between systems
  • Even if some parts are more complete than others, coverage can be expanded incrementally

To learn more, please contact us at info@schemarise.com.