AWS CloudFormation vs. ER/Studio Data Architect

AWS CloudFormation is well suited for scenarios where all of your resources need to be provisioned on AWS but it is not suited for hybrid cloud deployments. It's very easy for someone new to learn. The level of customization offered as part of the template specifically for AWS services is great. There is also a UI tool where we can drag and drop the services we want and it generates an yaml file which is very easy to use. The visibility of stacks and its resources and one place where we can track and identify the issues in deployment is great.

Incentivized

Data Architect is well suited at organizations of all sizes. It is never too early or unnecessary to enforce proper modelling and design standards on data solutions, and this tool will help that greatly by providing an industry leading data modelling tool, ability to import ETL mappings for data lineage, enforcing and managing naming conventions through the naming convention tool, and publishing of data dictionaries through the report publisher. I was successfully able to build models, provide traceability, and document source to target with lineage throughout for both the business (by providing business definitions in the descriptions), and technical teams (by documenting ETL instructions in text fields) along with field level mapping (by creating "Attachments" representing data sources, tables, and fields) providing easy search capabilities using business friendly terms

Incentivized

• SaaS
• Paas
• Iaas
• On promises

Incentivized

• ER/Studio has the ability to provide consistent field names and data types through domains, which are templates. This provides a way to have consistent naming of common fields, like CreatedBy and the data types for the fields. They also have the ability to change all the fields that use that domain to a different data type.
• ER/Studio provides the ability to create custom macros. These macros can be used to apply everything from standard fields based on domains to naming all constraints and indexes. I've also used a macro that comes with ER/Studio to spell check field and table names.
• My favorite feature is the ability to compare your data model to databases for deployments of changes, and to other data models.

Incentivized

• Error Description upon Failure Needs to be Improved.
• Slow to create, delete or update.
• Need to delete resources manually. It can ask before starting deletion whether to skip those resources or delete them.

Incentivized

• ER\Studio licensing can be cumbersome and upgrading from one version to another usually takes several phone calls and emails to the licensing group to get the update installed and running.
• The repository can be slow when the model count gets larger. By large I mean 20 to 30 models.
• A nice feature that I would like to see is table comments be displayed on the model along with the attributes. Currently you have to choose between the two.

Incentivized

I can call or email support and both get quick turn around. The only issue is they are on the west coast (US) and have a west coast work schedule and I'm on the East coast.

Incentivized

We didn't look into anything else as Cloudformation was "built-in" for AWS, it just kind of made sense to go with that. Terraform was something that we briefly looked into Terraform but decides to stick with Cloudformation because our task was relatively "simple". Apparently if it's a bit more complex, Terraform might be the way to go.

Incentivized

ER/Studio is the most mature, reach in features and quality and therefore useful. Unfortunately, it lacks compatibility with AWS Redshift.

Incentivized

• It has a positive ROI
• Less time inverted in bugs
• More stable source code

Incentivized

• ER/Studio has had a positive impact on my project as we can develop the data model and have a clear understanding of business needs before we continue with the development phase.

Incentivized