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Azure Data Factory vs erwin Data Catalog by Quest comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Azure Data Factory
Average Rating
8.0
Number of Reviews
85
Ranking in other categories
Data Integration (1st), Cloud Data Warehouse (3rd)
erwin Data Catalog by Quest
Average Rating
7.6
Number of Reviews
2
Ranking in other categories
Metadata Management (11th)
 

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 11.1%, down 13.3% compared to last year.
erwin Data Catalog by Quest, on the other hand, focuses on Metadata Management, holds 2.9% mindshare, up 2.5% since last year.
Data Integration
Metadata Management
 

Featured Reviews

Camilo Velasco - PeerSpot reviewer
Oct 27, 2022
No deployment cost, quick implementation, pay only for the processing time and data
The primary use case of this solution is to extract ETLS, transform and load data, and organize database synchronization The most valuable feature of this solution is the data flow, which is the same SQL server in important service, integration services, which is a very robust and powerful tool…
Andres-Martinez - PeerSpot reviewer
Mar 21, 2022
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

"It is easy to deploy workflows and schedule jobs."
"The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."
"The data copy template is a valuable feature."
"The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable."
"The feature I found most helpful in Azure Data Factory is the pipeline feature, including being able to connect to different sources. Azure Data Factory also has built-in security, which is another valuable feature."
"The most valuable feature of this solution would be ease of use."
"For developers that are very accustomed to the Microsoft development studio, it's very easy for them to complete end-to-end data integration."
"I like how you can create your own pipeline in your space and reuse those creations. You can collaborate with other people who want to use your code."
"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

"One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases."
"You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats."
"The product could provide more ways to import and export data."
"The pricing model should be more transparent and available online."
"The solution should offer better integration with Azure machine learning. We should be able to embed the cognitive services from Microsoft, for example as a web API. It should allow us to embed Azure machine learning in a more user-friendly way."
"The solution needs to be more connectable to its own services."
"It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory."
"It does not appear to be as rich as other ETL tools. It has very limited capabilities."
"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

"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."
"Azure products generally offer competitive pricing, suitable for diverse budget considerations."
"Azure Data Factory gives better value for the price than other solutions such as Informatica."
"I would not say that this product is overly expensive."
"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."
"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."
"The licensing cost is included in the Synapse."
"The price is fair."
"Erwin Data Catalog is very expensive."
"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."
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
13%
Manufacturing Company
9%
Healthcare Company
7%
Financial Services Firm
19%
Government
13%
Computer Software Company
11%
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...
What is your experience regarding pricing and costs for erwin Data Catalog?
Erwin Data Catalog is very expensive. I would rate it a two out of five for affordability.
 

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: October 2024.
814,649 professionals have used our research since 2012.