AWS Glue vs. erwin Data Modeler

When the data which requires ETL has different formats, schema, and volume, this service suits them best. So, when the volume is not consistent (typical use-case of healthcare and online shopping), AWS Glue can be the prime choice. When the data is available in both batch and streaming mode, the developer needs to generate a separate codebase. This increases the source code management efforts. So, prefer to go with Glue when the nature of the data is the same (either batched or streamed).

Incentivized

If you have a database environment that gets frequent schema object change requests. erwin Data Modeler is a great tool for version control and release control. Also, it's very useful to communicate between database and application teams.

Incentivized

• After data cleansing, the team also implemented the best practices for using AWS platform services as a Data Lake, such as job bookmarking for AWS Glue jobs, proper delimiter for the AWS Glue crawlers, partitioning in AWS S3, and transformation to parquet file for compression and faster querying time in Amazon Athena.
• Data modernization through combining data from multiple sources into a functioning datasets, rebuilding DW, and resctructuring data sources.
• Aims to lessen customer complaints, eliminate manual data extraction requests via SR from different data sources, and Increase accuracy, consistency and speed up reconciliation process.

Incentivized

• Creation of logical data models
• Reverse engineering
• Excellent communication tool in conversations with Developers and BAs
• As a company, Erwin provides lots of tutorials and how-tos.

Incentivized

• It’s integration with other cloud vendors is bit difficult
• If it can support non SQL based databases as well, it would be powerful.
• Real time data synchronisation in data source is missing

Incentivized

• erwin Data Modeler allows only 25 entities in a trial version. It really limits you when you want to try out the product before actually deciding to pay for licensing cost. It would be rather helpful if they limit the trial period by days instead.
• The UI is pretty good but sometimes the large diagrams are hard to manage. A little improvement in that area would be helpful.

Incentivized

I personally found it very usable for a data engineer's day job, particularly for performing ETL and managing the data pipelines.

Incentivized

The product is well designed and intuitive to use. The number of buttons and options can look a bit overwhelming initially but they come very handy when you get used to product.

Incentivized

Amazon responds in good time once the ticket has been generated but needs to generate tickets frequent because very few sample codes are available, and it's not cover all the scenarios.

Incentivized

CA customer support and our account manager have been able to support us with any issues that we have had, from managing our serial keys to issues we logged tickets to resolve. There are aspects of key management that have made it difficult over the years but support usually has worked with us.

Incentivized

The cataloging of data objects is the best in the case of AWS Glue. We use AWS Glue in all of our data pipelines to sync external and internal data sources and to automatically produce SQL-based ETL based on AWS Glue catalog objects. Integration with Amazon products is the other advantage.

Incentivized

I have used few other data modeling tools like Toad Data Modeler and SQL Developer Data Modeler. erwin Data Modeler is easy to use and has additional features that make overall data modeling productive and easy to manage.

Incentivized

• Positive Impact :- after ETL we can able to do some kind of automation
• Negative :- At some point of time it can hamper the cost but not really

Incentivized

• Reduced rework from when models were maintained individually.
• Reduced the amount of time to generate and compare models during development.
• Massively reduced the amount of time to reverse engineer 3rd party data models for analysis.

Incentivized