Azure Data Factory vs erwin Data Catalog comparison

The compared Microsoft and Quest Software solutions aren't in the same category. Microsoft is ranked #3 in DI , with an average rating of 8.2, and holds a 3.0% mindshare in the category. Quest Software is ranked #13 in MM , and holds a 3.1% 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 93 Azure Data Factory reviews

11,065 Views
7,745 Comparison Views

92% willing to recommend

erwin Data Catalog

Read 2 erwin Data Catalog reviews

661 Views
317 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, IBM and others in Data Integration.

To learn more, read our detailed Data Integration Report (Updated: January 2026).

Buyer's Guide

Data Integration

January 2026

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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

6.8

Number of Reviews

Ranking in other categories

Data Integration (3rd), Cloud Data Warehouse (2nd)

erwin Data Catalog

Average Rating

7.6

Reviews Sentiment

5.1

Number of Reviews

Ranking in other categories

Metadata Management (13th)

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 3.0%, down 9.8% compared to last year.
erwin Data Catalog, on the other hand, focuses on Metadata Management, holds 3.1% mindshare, up 2.0% since last year.

Data Integration Market Share Distribution
Product	Market Share (%)
Azure Data Factory	3.0%
SSIS	3.7%
Informatica Intelligent Data Management Cloud (IDMC)	3.5%
Other	89.8%

Data Integration

Metadata Management Market Share Distribution
Product	Market Share (%)
erwin Data Catalog	3.1%
Collibra Platform	18.4%
Informatica Intelligent Data Management Cloud (IDMC)	15.2%
Other	63.3%

Metadata Management

Featured Reviews

KandaswamyMuthukrishnan

Director at a computer software company with 1,001-5,000 employees

Integrates diverse data sources and streamlines ETL processes effectively

Regarding potential areas of improvement for Azure Data Factory, there is a need for better data transformation, especially since many people are now depending on DataBricks more for connectivity and data integration. Azure Data Factory should consider how to enhance integration or filtering for more transformations, such as integrating with Spark clusters. I am satisfied with Azure Data Factory so far, but I suggest integrating some AI functionality to analyze data during the transition itself, providing insights such as null records, common records, and duplicates without running a separate pipeline or job. The monitoring tools in Azure Data Factory are helpful for optimizing data pipelines; while the current feature is adequate, they can improve by creating a live dashboard to see the online process, including how much percentage has been completed, which will be very helpful for people who are monitoring the pipeline.

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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 most valuable feature of this solution would be ease of use."

"The data flows were beneficial, allowing us to perform multiple transformations."

"We have found the bulk load feature very valuable."

"Data Factory's best features include its data source connections, GUI for building data pipelines, and target loading within Azure."

"When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit."

"We use the solution to move data from on-premises to the cloud."

"From what we have seen so far, the solution seems very stable."

"The most valuable feature is the copy activity."

More Azure Data Factory pros

"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."

"For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better."

"There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run."

"There's no Oracle connector if you want to do transformation using data flow activity, so Azure Data Factory needs more connectors for data flow transformation."

"When working with AWS, we have noticed that the difference between ADF and AWS is that AWS is more customer-focused. They're more responsive compared to any other company. ADF is not as good as AWS, but it should be. If AWS is ten out of ten, ADF is around eight out of ten. I think AWS is easier to understand from the GUI perspective compared to ADF."

"Data Factory could be improved in terms of data transformations by adding more metadata extractions."

"Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically."

"It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory."

More Azure Data Factory cons

"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

"ADF is cheaper compared to AWS."

"The pricing model is based on usage and is not cheap."

"The solution's pricing is competitive."

"The licensing is a pay-as-you-go model, where you pay for what you consume."

"Data Factory is affordable."

"It's not particularly expensive."

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

"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."

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

13%

Computer Software Company

11%

Manufacturing Company

Government

Financial Services Firm

13%

Government

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	19
Large Enterprise	57

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

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

Compared 6% of the time

Snowflake vs Azure Data Factory

Compared 6% of the time

FME vs Azure Data Factory

Compared 4% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 4% of the time

Palantir Foundry vs Azure Data Factory

Compared 4% of the time

More Azure Data Factory Competitors

WhereScape RED vs erwin Data Catalog

Compared 24% of the time

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

Compared 20% of the time

SAP PowerDesigner vs erwin Data Catalog

Compared 14% of the time

Alation Data Catalog vs erwin Data Catalog

Compared 14% of the time

IBM InfoSphere Information Governance Catalog vs erwin Data Catalog

Compared 11% of the time

More erwin Data Catalog Competitors

Product Reports

Buyer's Guide

Azure Data Factory

February 2026

Download Azure Data Factory product report

Buyer's Guide

Metadata Management

January 2026

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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