Developer(s) |
|
---|---|
Stable release | |
Operating system | Microsoft Windows |
Type | CASE tool |
License | |
Website | www.erwin.com |
As of | October 2017 |
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erwin Data Modeler (stylized as erwin but formerly as ERwin) is computer software for data modeling. Originally developed by Logic Works, erwin has since been acquired by a series of companies, before being spun-off by the private equity firm Parallax Capital Partners, which acquired and incorporated it as a separate entity, erwin, Inc., managed by CEO Adam Famularo.
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The software’s engine is based on the IDEF1X method, although it now also supports diagrams displayed with a variant information technology engineering notation, as well as a dimensional modeling notation.
History[edit]
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ERwin was created by Logic Works, which was based in Princeton, New Jersey.[1] In May 1993, Logic Works released ERwin/ERX, a version of the tool designed to work in conjunction with PowerBuilder. The database models created using ERwin could be translated into software built through the PowerBuilder integrated development environment (IDE).[2] In May 1995, Logic Works ERwin was expanded to include several other IDEs, adding SQLWindows from Gupta Technologies and Visual Basic from Microsoft.[3] As of 1996, ERwin was among several data modeling software solutions being used to facilitate a wide move to the client–server model in database management.[4]
In 1998, Logic Works was acquired by Platinum Technology which was later acquired, in May 1999, by Computer Associates (CA).[5] ERwin was initially made part of CA’s Jasmine suite but it was later added to their new AllFusion suite[6] under the name AllFusion ERwin Data Modeler.[7][8] The tool was later renamed to CA ERwin Data Modeler.[9]
In 2014 Embarcadero Technologies sought to acquire the product from CA, Inc.[1] This acquisition was blocked by the Department of Justice over anti-competitive concerns.[10][11]
In April 2016, Parallax Capital Partners, a private equity firm, acquired the software from CA Technologies[12] and appointed Adam Famularo as CEO.[13] The company now operates under a new name stylized as erwin, Inc.[14] In September 2016, erwin announced that it had acquired Corso, a British enterprise architecture service provider.[15] In December of the same year, erwin acquired the business process modeling software Casewise, with a plan to integrate the two.[16] In 2017, erwin released its Data Modeler NoSQL, an enterprise-class data modeling solution for MongoDB. In April 2018, NoSQL data modeling support for Couchbase was added.[17] Also that year, erwin launched a data governance solution with impact analysis and integrations to its business process, enterprise architecture and data modeling suites.[18][19] In January 2018, the company acquired data harvesting technology and data governance consulting services company A&P Consulting.[20]
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They also developed the erwin EDGE software platform, with EDGE as an acronym for “enterprise data governance experience”, intending to use data governance for. PCWorld. Retrieved 2017-10-02.
Further reading[edit]
- DeAngelis, Carla (2000). Data Modeling with ERwin. Sams. ISBN0-672-31868-7.
External links[edit]
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Retrieved from 'https://en.wikipedia.org/w/index.php?title=Erwin_Data_Modeler&oldid=919722210'
Benefits of the erwin Data Modeling Tool
erwin data models reduce complexity, making it easier to design, deploy and understand data sources to meet business needs. erwin DM also automates and standardizes model design tasks, including complex queries, to improve business alignment, ensure data integrity and simplify integration. With erwin DM, users experience these benefits:
Visualization of Any Data
From Anywhere
See structured or unstructured enterprise data regardless of location – in a relational database, a data warehouse or in the cloud – within a single interface.
Automated Data Model &
Database Schema Generation
Automatically generate data models and database designs to increase efficiency and reduce errors.
Centralized Model Development
& Management
An integrated view of conceptual, logical and physical data models helps business and technical stakeholders understand data structures and meaning.
Data Literacy, Collaboration
& Accountability
Improve data intelligence and decision-making across the enterprise by maximizing the ability of stakeholders to use, understand and trust relevant data.
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Agile Application Development
Consolidate and build applications with hybrid architectures, including traditional, Big Data, cloud and on premise.
Increased Data Quality
By using data models to define and deploy enterprise data standards, you’ll realize more consistency and higher quality in data management.
Reduced Risks & Costs
Automation and standardization of data definitions and structures reduces risks and costs, plus you can test changes and new applications before they go into production.
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Successful Cloud Adoption
Automated schema engineering and deployment accelerates and ensures successful adoption of cloud platforms, like Snowflake, including auto documenting existing schema into reusable models.