Try our new research platform with insights from 80,000+ expert users

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
5.1
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 9.5%, down 12.7% compared to last year.
erwin Data Catalog by Quest, on the other hand, focuses on Metadata Management, holds 3.3% mindshare, up 2.4% 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

"The most important feature is that it can help you do the multi-threading concepts."
"It makes it easy to collect data from different sources."
"The scalability of the product is impressive."
"It is easy to integrate."
"The function of the solution is great."
"One advantage of Azure Data Factory is that it's fast, unlike SSIS and other on-premise tools. It's also very convenient because it has multiple connectors. The availability of native connectors allows you to connect to several resources to analyze data streams."
"The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain."
"One of the most valuable features of Azure Data Factory is the drag-and-drop interface. This helps with workflow management because we can just drag any tables or data sources we need. Because of how easy it is to drag and drop, we can deliver things very quickly. It's more customizable through visual effect."
"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."
"The data catalog feature is pretty good."
 

Cons

"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."
"Real-time replication is required, and this is not a simple task."
"The speed and performance need to be improved."
"Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue."
"Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."
"There aren't many third-party extensions or plugins available in the solution."
"The performance could be better. It would be better if Azure Data Factory could handle a higher load. I have heard that it can get overloaded, and it can't handle it."
"Data Factory's cost is too high."
"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 is room for improvement with respect to the connector and how to connect to the structured and unstructured database."
 

Pricing and Cost Advice

"Product is priced at the market standard."
"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."
"In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal."
"This is a cost-effective solution."
"The price you pay is determined by how much you use it."
"The price is fair."
"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."
"The pricing is a bit on the higher end."
"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."
report
Use our free recommendation engine to learn which Data Integration solutions are best for your needs.
845,406 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
14%
Computer Software Company
12%
Manufacturing Company
9%
Healthcare Company
7%
Financial Services Firm
21%
Government
13%
Computer Software Company
10%
Manufacturing Company
10%
 

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
Find out what your peers are saying about Microsoft, Informatica, Talend and others in Data Integration. Updated: March 2025.
845,406 professionals have used our research since 2012.