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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
7.0
Number of Reviews
91
Ranking in other categories
Data Integration (1st), Cloud Data Warehouse (2nd)
erwin Data Catalog by Quest
Average Rating
7.6
Reviews Sentiment
5.1
Number of Reviews
2
Ranking in other categories
Metadata Management (14th)
 

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 7.9%, down 12.2% compared to last year.
erwin Data Catalog by Quest, on the other hand, focuses on Metadata Management, holds 3.6% mindshare, up 2.8% 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 interface of Azure Data Factory is very usable with a more interactive visual experience, making it easier for people who are not as experienced in coding to work with."
"Allows more data between on-premises and cloud solutions"
"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."
"The solution handles large volumes of data very well. One of its best features is its ability to integrate data end-to-end, from pulling data from the source to accessing Databricks. This makes it quite useful for our needs."
"We haven't had any issues connecting it to other products."
"The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable."
"We have found the bulk load feature very valuable."
"This solution will allow the organisation to improve its existing data offerings over time by adding predictive analytics, data sharing via APIs and other enhancements readily."
"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

"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog."
"We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on."
"The solution can be improved by decreasing the warmup time which currently can take up to five minutes."
"There is a problem with the integration with third-party solutions, particularly with SAP."
"The one element of the solution that we have used and could be improved is the user interface."
"Data Factory's monitorability could be better."
"The pricing scheme is very complex and difficult to understand."
"Lacks in-built streaming data processing."
"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."
"Data Factory is affordable."
"Product is priced at the market standard."
"The pricing is a bit on the higher end."
"ADF is cheaper compared to AWS."
"I would rate Data Factory's pricing nine out of ten."
"Pricing appears to be reasonable in my opinion."
"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
14%
Computer Software Company
12%
Manufacturing Company
9%
Government
6%
Financial Services Firm
19%
Manufacturing Company
8%
Government
8%
Real Estate/Law Firm
6%
 

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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