erwin has announced the latest version of erwin Data Modeler (erwin DM). The update features new metadata-driven automation capabilities and facilitates moving legacy, premise-based data sources to modern cloud platforms to ensure proper data governance.
According to the company, erwin DM provides metadata and schema visualization, a well-governed and integrated process for defining/designing data assets of all types, and centralization and integration of business and semantic metadata—which combine to accelerate data governance and increase enterprise data literacy and collaboration.
Automated schema design and migration helps organizations adopt modern DBMS platforms and data warehouse architectures. Supporting this approach, erwin recently announced its partnership with Snowflake. The partnership involves a new native integration with erwin DM to automate the design, analysis and deployment of Snowflake, as well as an erwin Data Connector for automatically extracting Snowflake metadata for ingestion into the erwin Data Intelligence Suite (erwin DI).
“As a result of COVID 19, businesses around the world are drastically stepping up their digital transformation efforts, including moving their legacy data to the cloud to ensure it’s more available for decision making,” said erwin CEO Adam Famularo. ”So we continue to invest in the technology we pioneered to ensure customers can understand, design and deploy new data sources, plus support data governance and intelligence efforts, to further reduce data management costs and data-related risks, while improving the quality and agility of an organization’s overall data capability.”
erwin DM’s specific new functionality includes:
- erwin DM Connect for DI, which automatically harvests erwin data models and the associated metadata and then ingests it into the erwin Data Intelligence Suite (erwin DI). The model metadata feeds the erwin Data Catalog and the business information stored in the models populates the erwin DI Business Glossary Manager.
- Native support for Snowflake (4.1.x) and MariaDB (10.x) databases, which removes barriers, reduces costs, and mitigates the risks associated with migrating legacy databases to these platforms. erwin’s model-driven schema transformation accelerates the successful adoption of Snowflake and MariaDB technologies, automating the engineering and deployment of schema from the models and auto-documents existing schema into reusable models.
- Centralized management and governance of naming standards, which enables erwin DM Workgroup Edition users to now can centrally create and manage reusable naming standards across erwin data models and entire data architectures.
For more information, including details on usability and design-task automation enhancements that have also been made to help increase data modeler productivity, go to https://erwin.com.