Azure Data Factory vs CloverDX Designer comparison

Why PeerSpot?

Microsoft and CloverDX are both solutions in the Data Integration category. Microsoft is ranked #6 with an average rating of 8.0, while CloverDX is ranked #90. Microsoft holds a 2.2% mindshare in DI, compared to CloverDX’s 0.3% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 100% of CloverDX users who would recommend it.

Azure Data Factory

Read 97 Azure Data Factory reviews

16,078 Views
11,255 Comparison Views

92% willing to recommend

CloverDX Designer

Read 1 CloverDX Designer review

1,112 Views
1,079 Comparison Views

100% willing to recommend

Azure Data Factory

CloverDX Designer

Comparison Buyer's Guide

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Executive Summary

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

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

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

Buyer's Guide

Data Integration

October 2026

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Helped 921,338 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

Ranking in Data Integration

6th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (8th)

CloverDX Designer

Ranking in Data Integration

90th

Average Rating

7.0

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.2%, down from 4.4% compared to the previous year. The mindshare of CloverDX Designer is 0.3%, down from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Mindshare Distribution
Product	Mindshare (%)
Azure Data Factory	2.2%
CloverDX Designer	0.3%
Other	97.5%

Data Integration

Featured Reviews

Kunal Das

Test Engineer at Happiest Minds Technologies

Drag-and-drop pipelines have saved days of work and now automate data movement and backfilling

If the AI features were more improved so that I don't have to provide each and every detail, Azure Data Factory could be improved in a much better way by improving the AI features. For example, if I want to fetch any data from a raw source, I need to provide each and every detail. But if I am just uploading my raw data and if AI will sync with that data, it can analyze that data and give me proper suggestions on how that should be done in a proper way. Automatic suggestions could improve in a much better way. As I have mentioned, the AI features as well as more drag-and-drop activities could be improved. If I am making a pipeline, it should give me suggestions, such as which activity should be used, so that I don't have to remember each activity. If I have used one activity, I shouldn't have to remember what activity should I use next. It should give auto-suggestions. That is why I have given a nine out of 10. Currently, I don't know about its governance and security, but in view of its improvement, I think Azure Data Factory should improve in these areas. As I already mentioned, the AI features should be improved. Also, the auto-suggestion features should also improve.

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reviewer1518951

Data professional at a financial services firm with 1,001-5,000 employees

Simple, stable, and allows us to handle data from various sources, but needs enterprise features for logging, recoverability, and monitoring

If I could give any advice to the guys who are developing it, I would suggest them to really look at the enterprise features, such as being able to log what's going on, being able to capture the current state of processing, and being able to recover from error situations. So, there should be a focus on logging, recoverability, and monitoring. We should be able to monitor what's going on, and in case of any issues, we should be able to recover and restart processing and other things. For scalability and performance, I would probably suggest the Pushdown feature so that you can do the transformation directly on the data source. You do not need to do that calculation within the ETL server. For this, you should be aware of the type of data because each database or kind of storage, such as Hadoop, has its own ANSI standard or language, such as SQL. Microsoft, Oracle, and IBM have their own language. Based on the feedback that I have got, its initial setup takes some time. It could perhaps be simpler.

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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 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."

"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."

"The data is more scalable."

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

"The tools are impressively well-integrated, allowing quick development of ETL, big data, data warehousing and machine learning solutions with the flexibility to grow and adapt to changing or enhanced requirements."

"So far, I'm quite happy with the solution overall."

"On the tool itself, we've never experienced any bugs or glitches. There haven't been crashes. Stability has been good."

"Azure Data Factory is easy to use and integrates well, providing good classification to bring data from diverse parts of the data infrastructure, whether from CSV files coming from the Cisco server, MySQL, Excel machines, or local network data from remote files."

More Azure Data Factory pros

"Its simplicity and the way it handles graphs are the most valuable features."

Cons

"Data Factory's monitorability could be better."

"When the record fails, it's tough to identify and log."

"The product's technical support has certain shortcomings, making it an area where improvements are required."

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

"Some known bugs and issues with Azure Data Factory could be rectified."

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

"The support and the documentation can be improved."

"There is one particular problem with Azure Data Factory. When you have a parent-to-child relationship and the child has one more relationship, creating a hierarchy situation, there are issues."

More Azure Data Factory cons

"If I could give any advice to the guys who are developing it, I would suggest them to really look at the enterprise features, such as being able to log what's going on, being able to capture the current state of processing, and being able to recover from error situations. So, there should be a focus on logging, recoverability, and monitoring. We should be able to monitor what's going on, and in case of any issues, we should be able to recover and restart processing and other things. For scalability and performance, I would probably suggest the Pushdown feature so that you can do the transformation directly on the data source. You do not need to do that calculation within the ETL server. For this, you should be aware of the type of data because each database or kind of storage, such as Hadoop, has its own ANSI standard or language, such as SQL. Microsoft, Oracle, and IBM have their own language. Based on the feedback that I have got, its initial setup takes some time. It could perhaps be simpler."

Pricing and Cost Advice

"Pricing appears to be reasonable in my opinion."

"Our licensing fees are approximately 15,000 ($150 USD) per month."

"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."

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

"The solution's pricing is competitive."

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

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

"I would not say that this product is overly expensive."

More Azure Data Factory pricing and cost advice

"Its price and value for money would be okay for our purpose if there were some additional features."

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Top Industries

By visitors reading reviews

Manufacturing Company

11%

Financial Services Firm

10%

Outsourcing Company

Computer Software Company

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	21
Large Enterprise	64

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

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Comparisons

Snowflake vs Azure Data Factory

Compared 4% of the time

Databricks vs Azure Data Factory

Compared 4% of the time

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

Compared 3% of the time

Palantir Foundry vs Azure Data Factory

Compared 3% of the time

SnapLogic vs Azure Data Factory

Compared 3% of the time

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SAP Data Services vs CloverDX Designer

Compared 41% of the time

SSIS vs CloverDX Designer

Compared 37% of the time

Ab Initio Co>Operating System vs CloverDX Designer

Compared 9% of the time

Qlik Compose vs CloverDX Designer

Compared 7% of the time

More CloverDX Designer Competitors

Product Reports

Buyer's Guide

Azure Data Factory

November 2026

Download Azure Data Factory product report

Buyer's Guide

Data Integration

October 2026

Download CloverDX Designer 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

CloverDX Designer is a robust data integration platform offering advanced capabilities for data transformation and workflow automation, making it a versatile choice for those with intricate data management tasks.

Designed for users looking to streamline complex data processes, CloverDX Designer provides an intuitive yet powerful interface for designing, testing, and deploying data pipelines. Its primary functions include data integration, transformation, and managing intricate data workflows, catering to professionals who require flexibility and reliability in data handling.

What are the key features of CloverDX Designer?

Data Transformation: Enables conversion and mapping of data between formats.
Workflow Automation: Streamlines repetitive tasks for efficiency.
Scalability: Adapts to growing data volumes with ease.
Debugging Tools: Offers efficient ways to test and troubleshoot data flows.

What benefits or ROI aspects should be evaluated?

Cost Efficiency: Reduces manual data handling expenses.
Time Saving: Accelerates data processing and project deployment.
Customization: Offers flexibility tailored to specific needs.
Improved Data Accuracy: Ensures accurate processes with validation features.

In industries like finance, healthcare, and retail, CloverDX Designer is implemented to handle complex data transformations, automate regulatory reporting, and manage customer insights efficiently. Its versatility makes it suitable for any sector requiring reliable data integration solutions.

CloverDX

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

Allant Group, NDP, Porch, GoodData

Find out what your peers are saying about Informatica, Microsoft, Palantir and others in Data Integration. Updated: October 2026.

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