Azure Data Factory vs erwin Data Catalog comparison

The compared Microsoft and Quest Software solutions aren't in the same category. Microsoft is ranked #1 in DI , with an average rating of 8.2, and holds a 7.4% mindshare in the category. Quest Software is ranked #16 in MM , and holds a 3.4% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 100% of Quest Software users who would recommend it.

Azure Data Factory

Read 91 Azure Data Factory reviews

15,713 Views
10,999 Comparison Views

92% willing to recommend

erwin Data Catalog

Read 2 erwin Data Catalog reviews

510 Views
184 Comparison Views

100% willing to recommend

Azure Data Factory

erwin Data Catalog

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Azure Data Factory and erwin Data Catalog based on real PeerSpot user reviews.

Find out what your peers are saying about Microsoft, Informatica, Talend and others in Data Integration.

To learn more, read our detailed Data Integration Report (Updated: August 2025).

Buyer's Guide

Data Integration

August 2025

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Helped 865,295 peers since 2012

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

Ranking in other categories

Data Integration (1st), Cloud Data Warehouse (2nd)

erwin Data Catalog

Average Rating

7.6

Reviews Sentiment

5.1

Number of Reviews

Ranking in other categories

Metadata Management (16th)

Mindshare comparison

Azure Data Factory and erwin Data Catalog aren’t in the same category and serve different purposes. Azure Data Factory is designed for Data Integration and holds a mindshare of 7.4%, down 11.9% compared to last year.
erwin Data Catalog, on the other hand, focuses on Metadata Management, holds 3.4% mindshare, up 3.0% since last year.

Data Integration

Metadata Management

Featured Reviews

Joy Maitra

Sr. Technical Architect at Hexaware Technologies Limited

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.

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Andres-Martinez

BI Data Analytics Engineer at Targa Research

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.

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Quotes from Members

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

Pros

"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."

"It is very modular. It works well. We've used Data Factory and then made calls to libraries outside of Data Factory to do things that it wasn't optimized to do, and it worked really well. It is obviously proprietary in regards to Microsoft created it, but it is pretty easy and direct to bring in outside capabilities into Data Factory."

"The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable."

"It is easy to integrate."

"The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted."

"We haven't had any issues connecting it to other products."

"The most valuable features of the solution are its ease of use and the readily available adapters for connecting with various sources."

"The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."

More Azure Data Factory pros

"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 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."

"Data Factory could be improved by eliminating the need for a physical data area. We have to extract data using Data Factory, then create a staging database for it with Azure SQL, which is very, very expensive. Another improvement would be lowering the licensing cost."

"I have not found any real shortcomings within the product."

"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."

"Lacks a decent UI that would give us a view of the kinds of requests that come in."

"Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue."

"It can improve from the perspective of active logging. It can provide active logging information."

"Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there."

More Azure Data Factory cons

"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."

"The licensing model for Azure Data Factory is good because you won't have to overpay. Pricing-wise, the solution is a five out of ten. It was not expensive, and it was not cheap."

"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."

"Pricing is comparable, it's somewhere in the middle."

"The cost is based on the amount of data sets that we are ingesting."

"The price is fair."

"ADF is cheaper compared to AWS."

"This is a cost-effective solution."

More Azure Data Factory pricing and cost advice

"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

Government

Financial Services Firm

16%

Manufacturing Company

Government

Retailer

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.

See all answers

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...

See all answers

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...

See all answers

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...

See all answers

Comparisons

Snowflake vs Azure Data Factory

Compared 9% of the time

Informatica PowerCenter vs Azure Data Factory

Compared 7% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Azure Data Factory

Compared 5% of the time

Alteryx Designer vs Azure Data Factory

Compared 5% of the time

Palantir Foundry vs Azure Data Factory

Compared 5% of the time

More Azure Data Factory Competitors

WhereScape RED vs erwin Data Catalog

Compared 31% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs erwin Data Catalog

Compared 24% of the time

Alation Data Catalog vs erwin Data Catalog

Compared 12% of the time

Palantir Foundry vs erwin Data Catalog

Compared 11% of the time

Oracle Data Integrator (ODI) vs erwin Data Catalog

Compared 10% of the time

More erwin Data Catalog Competitors

Product Reports

Buyer's Guide

Azure Data Factory

August 2025

Download Azure Data Factory product report

Buyer's Guide

Metadata Management

July 2025

Download erwin Data Catalog product report

Overview

Azure Data Factory efficiently manages and integrates data from various sources, enabling seamless movement and transformation across platforms. Its valuable features include seamless integration with Azure services, handling large data volumes, flexible transformation, user-friendly interface, extensive connectors, and scalability. Users have experienced improved team performance, workflow simplification, enhanced collaboration, streamlined processes, and boosted productivity.

Microsoft

erwin Data Catalog empowers businesses to efficiently manage their data assets, providing a comprehensive overview and governance capabilities. It supports metadata management, enhancing data discovery and curation processes.

erwin Data Catalog offers robust data management tools that streamline data discovery, lineage, and governance. Designed for sophisticated data environments, it ensures efficient metadata control, providing users with an intuitive way to handle and leverage extensive data assets. Its features cater to enterprises looking to optimize data compliance and transformation strategies.

What are the main features of erwin Data Catalog?

Metadata Management: Facilitates efficient organization and retrieval of metadata across systems.
Data Lineage: Tracks data flow from source to destination, ensuring traceability and auditing.
Data Governance: Implements policies and standards to maintain data integrity and compliance.
Integration Capabilities: Seamlessly connects with diverse data sources and platforms.
Automated Curation: Speeds up data categorization and insight extraction with AI-driven processes.

What benefits can you expect from erwin Data Catalog?

Enhanced Decision Making: Provides comprehensive data insights for strategic planning.
Improved Compliance: Ensures adherence to industry standards and regulations.
Efficient Data Management: Simplifies complex data ecosystems, reducing time and resource expenditure.
Increased Collaboration: Offers shared insights and collaborative tools for teams.

erwin Data Catalog is successfully implemented in industries such as finance, healthcare, and retail, enabling these sectors to leverage vast data volumes effectively. It enhances operational efficiency by streamlining data management practices, ensuring compliance, and supporting critical data-driven decision-making processes.

Quest Software

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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