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Azure Data Factory vs erwin Data Catalog by Quest comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Azure Data Factory
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
90
Ranking in other categories
Data Integration (1st), Cloud Data Warehouse (3rd)
erwin Data Catalog by Quest
Average Rating
7.6
Reviews Sentiment
5.3
Number of Reviews
2
Ranking in other categories
Metadata Management (10th)
 

Mindshare comparison

Azure Data Factory and erwin Data Catalog by Quest aren’t in the same category and serve different purposes. Azure Data Factory is designed for Data Integration and holds a mindshare of 10.1%, down 12.9% compared to last year.
erwin Data Catalog by Quest, on the other hand, focuses on Metadata Management, holds 3.0% mindshare, up 2.3% since last year.
Data Integration
Metadata Management
 

Featured Reviews

Joy Maitra - PeerSpot reviewer
Facilitates seamless data pipeline creation with good analytics and and thorough monitoring
Azure Data Factory is a low code, no code platform, which is helpful. It provides many prebuilt functionalities that assist in building data pipelines. Also, it facilitates easy transformation with all required functionalities for analytics. Furthermore, it connects to different sources out-of-the-box, making integration much easier. The monitoring is very thorough, though a more readable version would be appreciable.
Andres-Martinez - PeerSpot reviewer
Helps with metadata management, saves time, and allows us to do impact analysis on any changes
There are always ways to improve things. For example, we can use AI to be able to find out something. When we are typing something, if we don't know the exact term, Artificial Intelligence would be useful to find terms that are phonetically or syntactically similar. Instead of having to type in the exact name, they can provide those in the list. So, they can provide AI support for the search because when you have thousands and thousands of terms, it is hard to remember all the names. There were some issues when drawing the data models. If you have more than 500 or 600 tables, it takes a long time to display those in the right position on the screen. That can also be improved. They need some caching and some parallel pipelines working on the backend in order to divide it into sections.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"This solution has provided us with an easier, and more efficient way to carry out data migration tasks."
"I think it makes it very easy to understand what data flow is and so on. You can leverage the user interface to do the different data flows, and it's great. I like it a lot."
"I like that it's a monolithic data platform. This is why we propose these solutions."
"It's cloud-based, allowing multiple users to easily access the solution from the office or remote locations. I like that we can set up the security protocols for IP addresses, like allow lists. It's a pretty user-friendly product as well. The interface and build environment where you create pipelines are easy to use. It's straightforward to manage the digital transformation pipelines we build."
"The scalability of the product is impressive."
"Azure Data Factory's most valuable features are the packages and the data transformation that it allows us to do, which is more drag and drop, or a visual interface. So, that eases the entire process."
"I can do everything I want with SSIS and Azure Data Factory."
"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."
"The data catalog feature is pretty good."
"When you combine it with data lineage, every time you need to make a change, it allows you to do impact analysis on any changes and then connect to the end-users or data stewards so that they can be aware that a change is coming. That's one of the main benefits we use it for."
 

Cons

"I have not found any real shortcomings within the product."
"Data Factory could be improved in terms of data transformations by adding more metadata extractions."
"Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations."
"There is always room to improve. There should be good examples of use that, of course, customers aren't always willing to share. It is Catch-22. It would help the user base if everybody had really good examples of deployments that worked, but when you ask people to put out their good deployments, which also includes me, you usually got, "No, I'm not going to do that." They don't have enough good examples. Microsoft probably just needs to pay one of their partners to build 20 or 30 examples of functional Data Factories and then share them as a user base."
"You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats."
"I have encountered a problem with the integration with third-party solutions, particularly with SAP."
"This solution is currently only useful for basic data movement and file extractions, which we would like to see developed to handle more complex data transformations."
"The solution needs to be more connectable to its own services."
"There is room for improvement with respect to the connector and how to connect to the structured and unstructured database."
"There are always ways to improve things. For example, we can use AI to be able to find out something. When we are typing something, if we don't know the exact term, Artificial Intelligence would be useful to find terms that are phonetically or syntactically similar. Instead of having to type in the exact name, they can provide those in the list. So, they can provide AI support for the search because when you have thousands and thousands of terms, it is hard to remember all the names."
 

Pricing and Cost Advice

"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."
"Azure products generally offer competitive pricing, suitable for diverse budget considerations."
"The cost is based on the amount of data sets that we are ingesting."
"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."
"Understanding the pricing model for Data Factory is quite complex."
"The solution's pricing is competitive."
"It's not particularly expensive."
"The price you pay is determined by how much you use it."
"I am not very familiar with its pricing. I know it is not cheap, but it is also not super expensive. It depends on the company size. For a company making $1 million, it is very expensive. For a company making 10 million and above, it might be okay."
"Erwin Data Catalog is very expensive."
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
12%
Manufacturing Company
9%
Healthcare Company
7%
Financial Services Firm
20%
Government
14%
Computer Software Company
9%
Manufacturing Company
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

How do you select the right cloud ETL tool?
AWS Glue and Azure Data factory for ELT best performance cloud services.
How does Azure Data Factory compare with Informatica PowerCenter?
Azure Data Factory is flexible, modular, and works well. In terms of cost, it is not too pricey. It offers the stability and reliability I am looking for, good scalability, and is easy to set up an...
How does Azure Data Factory compare with Informatica Cloud Data Integration?
Azure Data Factory is a solid product offering many transformation functions; It has pre-load and post-load transformations, allowing users to apply transformations either in code by using Power Q...
Which ETL tool would you recommend to populate data from OLTP to OLAP?
There are two products I know about * TimeXtender : Microsoft based, Transformation logic is quiet good and can easily be extended with T-SQL , Has a semantic layer that generates metat data for cu...
 

Overview

 

Sample Customers

1. Adobe 2. BMW 3. Coca-Cola 4. General Electric 5. Johnson & Johnson 6. LinkedIn 7. Mastercard 8. Nestle 9. Pfizer 10. Samsung 11. Siemens 12. Toyota 13. Unilever 14. Verizon 15. Walmart 16. Accenture 17. American Express 18. AT&T 19. Bank of America 20. Cisco 21. Deloitte 22. ExxonMobil 23. Ford 24. General Motors 25. IBM 26. JPMorgan Chase 27. Microsoft (Azure Data Factory is developed by Microsoft) 28. Oracle 29. Procter & Gamble 30. Salesforce 31. Shell 32. Visa
Balfour Beatty Construction, Banco de México, BFSI Canada, CenturyLink, Daktronics
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