In my 35 years of data modeling many data platforms have come and gone, while others continue to change. For many, maintaining pace with the frequency of change in data platforms is an ongoing challenge. For a modeling vendor such as ourselves, immediately adding new features to correlate to every single change to the multitude of different platforms is virtually impossible , so we prioritize by considering several factors including customer demand and market viability of new platforms and features. We realize that different customers require different platforms and features at varying rates, which is why we have designed and built ER/Studio as an advanced data architecture and modeling platform.
ER./Studio Data Architect can usually be adapted easily to work with new platforms including design, forward engineering (DDL generation) and reverse engineering functionality. You can define and create additional metadata for any model construct, as well as extend generated DDL with additional syntax (pre and post SQL). There are native connectors for many platforms, metadata import bridges, and generic capabilities including ODBC connectivity. Customization can be extended further with custom datatypes and reusable datatype mapping templates. Combined with extensive macro programming capability (Winwrap basic) and an event-driven automation engine you can adapt ER/Studio Data Architect to your requirements.
To illustrate these points, the attached document shows how to apply the capabilities of ER/Studio to the design and implementation of a data warehouse deployed to Amazon Redshift. Redshift is gaining popularity in the marketplace. However, it has a number of characteristics that are different from platforms you may be accustomed to working with. In the document, I discuss several modeling and forward engineering (DDL generation) considerations, as well as approaches to reverse engineering a pre-existing environment. If that environment was built without the use of data modeling, ER/Studio will be extremely beneficial in helping to to detect and remediate design deficiencies. Having done so, you will be able to realize even greater benefits through model-driven design, as well as building out the blueprint of your enterprise data landscape, which is essential to enabling meaningful communication and data governance.
The principles discussed use Redshift as an example, but can be applied to other platforms as well. Learning and understanding these capabilities will provide you with huge productivity benefits. Have fun impressing your boss with your new modeling superpowers!