Automated metadata-driven mapping and code generation
Here’s to data visibility, lineage and governance throughout the data integration lifecycle. Oh, and did we mention eliminating manual work and costly errors along the way? With erwin MM, you can automate enterprise data mapping and code generation for faster time-to-value and greater accuracy when it comes to data movement projects, as well as synchronize “data in motion” with your data preparation and governance efforts. Harvest metadata from data sources and map data elements from source to target within a single data catalog to determine data lineage, deploy data warehouses and other Big Data solutions, and harmonize data integration across platforms. The web-based solution reduces the need for specialized, technical resources with knowledge of ETL and database procedural code, while making it easy for business analysts, data architects, ETL developers, testers and project managers to collaborate for faster decision-making.
Efficiently transform and move data according to business requirements from a single, unified data catalog.
Easily scan metadata from various operational systems, catalog and synchronize it with core data management and governance capabilities and artifacts in real time.
Speed up data movement and transformation projects, code generation and data movement documentation and reduce delivery timeframes and errors.
Use source and target system metadata to accelerate the creation of source-to-target, metadata-driven mapping. This drag-and-drop approach eliminates manual work and costly errors as usually seen with a traditional Excel approach.
With plug-in SDKs, code generation is automated and can be customized to meet customer-specific requirements, accelerating delivery and reducing rework. This includes code generation of data integration components; data vault hub, link and satellite; and SQL code, stored procedures and DDLs to build warehouses and marts; and reverse-engineering of ETL/ELT components into mapping and lineage documentation.
Instantly identify the impact of change on an attribute or table across the warehouse, saving valuable time and resources. With built-in versioning, baseline and archive all mapping documentation and view change comparison reports over time.
Generate end-to-end lineage between repositories and view data flows from source systems all the way to the reporting layers. This includes all intermediate transformation and business logic so stakeholders have the information they need to make swift decisions.
The Value of Automated Data
Mapping & Preparation for the
The Value of Metadata-Driven Automation for the Modern
erwin MM Introduction
Solving the Enterprise Data Dilemma
The Regulatory Rationale for
Integrating Data Management
& Data Governance
2018 State of Data Governance
Data Governance Is Everyone’s Business