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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 8.4%, down 12.3% compared to last year.
erwin Data Catalog by Quest, on the other hand, focuses on Metadata Management, holds 3.5% mindshare, up 2.7% 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 initial setup is very quick and easy."
"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."
"The most valuable feature of this solution would be ease of use."
"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."
"We have found the bulk load feature very valuable."
"Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data."
"The solution can scale very easily."
"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."
"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

"There should be a way that it can do switches, so if at any point in time I want to do some hybrid mode of making any data collections or ingestions, I can just click on a button."
"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."
"The solution can be improved by decreasing the warmup time which currently can take up to five minutes."
"Customer service is not satisfactory. Third-party personnel handle support and rely on a knowledge repository."
"Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue."
"Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations."
"Azure Data Factory's pricing in terms of utilization could be improved."
"There aren't many third-party extensions or plugins available in the solution."
"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

"Azure Data Factory gives better value for the price than other solutions such as Informatica."
"The licensing is a pay-as-you-go model, where you pay for what you consume."
"Data Factory is expensive."
"The price you pay is determined by how much you use it."
"Pricing appears to be reasonable in my opinion."
"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."
"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."
"Azure products generally offer competitive pricing, suitable for diverse budget considerations."
"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
21%
Government
8%
Manufacturing Company
6%
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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