Azure Data Factory vs erwin Data Catalog by Quest comparison

The compared Microsoft and erwin by Quest solutions aren't in the same category. Microsoft is ranked #1 in DI , with an average rating of 8.0, and holds a 11.1% mindshare in the category. erwin by Quest is ranked #11 in MM , with an average rating of 7.0, and holds a 2.9% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 100% of erwin by Quest users who would recommend it.

Azure Data Factory

Read 86 Azure Data Factory reviews

23,087 Views
18,331 Comparison Views

92% willing to recommend

erwin Data Catalog by Quest

Read 2 erwin Data Catalog by Quest reviews

213 Views
171 Comparison Views

100% willing to recommend

Azure Data Factory

erwin Data Catalog by Quest

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Azure Data Factory and erwin Data Catalog by Quest 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: November 2024).

Buyer's Guide

Data Integration

November 2024

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Categories and Ranking

Azure Data Factory

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Data Integration (1st), Cloud Data Warehouse (3rd)

erwin Data Catalog by Quest

Average Rating

7.6

Reviews Sentiment

5.3

Number of Reviews

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

Thulani David Mngadi

Data architect at Old Mutual

Data flow feature is valuable for data transformation tasks

The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem.

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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

"It's extremely consistent."

"In terms of my personal experience, it works fine."

"The security of the agent that is installed on-premises is very good."

"The most valuable aspect is the copy capability."

"Its integrability with the rest of the activities on Azure is most valuable."

"The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components."

"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."

"I can do everything I want with SSIS and Azure Data Factory."

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

"The solution can be improved by decreasing the warmup time which currently can take up to five minutes."

"My only problem is the seamless connectivity with various other databases, for example, SAP."

"Data Factory would be improved if it were a little more configuration-oriented and not so code-oriented and if it had more automated features."

"The solution needs to integrate more with other providers and should have a closer integration with Oracle BI."

"We require Azure Data Factory to be able to connect to Google Analytics."

"If the user interface was more user friendly and there was better error feedback, it would be helpful."

"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."

"We have experienced some issues with the integration. This is an area that needs improvement."

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."

"ADF is cheaper compared to AWS."

"Understanding the pricing model for Data Factory is quite complex."

"It's not particularly expensive."

"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."

"Data Factory is expensive."

"There's no licensing for Azure Data Factory, they have a consumption payment model. How often you are running the service and how long that service takes to run. The price can be approximately $500 to $1,000 per month but depends on the scaling."

"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."

More Azure Data Factory pricing and cost advice

"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

12%

Manufacturing Company

Healthcare Company

Financial Services Firm

20%

Government

13%

Manufacturing Company

10%

Computer Software Company

10%

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...

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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...

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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

What do you like most about erwin Data Catalog?

The data catalog feature is pretty good.

See all answers

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.

See all answers

Comparisons

Informatica PowerCenter vs Azure Data Factory

Compared 14% of the time

Snowflake vs Azure Data Factory

Compared 11% of the time

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

Compared 8% of the time

Alteryx Designer vs Azure Data Factory

Compared 7% of the time

IBM InfoSphere DataStage vs Azure Data Factory

Compared 4% of the time

More Azure Data Factory Competitors

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

Compared 41% of the time

Oracle Data Integrator (ODI) vs erwin Data Catalog by Quest

Compared 19% of the time

WhereScape RED vs erwin Data Catalog by Quest

Compared 18% of the time

Talend Open Studio vs erwin Data Catalog by Quest

Compared 11% of the time

More erwin Data Catalog by Quest Competitors

Product Reports

Buyer's Guide

Azure Data Factory

November 2024

Download Azure Data Factory product report

Buyer's Guide

Metadata Management

November 2024

Download erwin Data Catalog by Quest product report

Learn More

Microsoft

erwin by Quest

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.

erwin Data Catalog (DC), part of the erwin Data Intelligence Suite, automates enterprise metadata management, including data mapping, code generation, data profiling, data lineage and impact analysis. It integrates and activates data in a single, unified catalog in accordance with business requirements by scheduling ongoing scans of metadata from the widest array of data sources, keeping metadata current with full versioning and change management, and easily mapping data elements from source to target, including data at rest and in motion, and harmonize data integration across platforms.

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: November 2024.

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