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 #88. 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

15,321 Views
10,725 Comparison Views

92% willing to recommend

CloverDX Designer

Read 1 CloverDX Designer review

1,048 Views
1,027 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, Palantir, Microsoft and others in Data Integration.

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

Buyer's Guide

Data Integration

September 2026

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Helped 914,938 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 (6th)

CloverDX Designer

Ranking in Data Integration

88th

Average Rating

7.0

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of October 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.2%, down from 5.1% compared to the previous year. The mindshare of CloverDX Designer is 0.3%, up from 0.3% 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.

Read full review

Quotes from Members

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

Pros

"I like its integration with SQL pools, its ability to work with Databricks, its pipelines, and the serverless architecture are the most effective features."

"The solution can scale very easily."

"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."

"The solution handles large volumes of data very well. One of its best features is its ability to integrate data end-to-end, from pulling data from the source to accessing Databricks. This makes it quite useful for our needs."

"I am one hundred percent happy with the stability."

"It makes it easy to collect data from different sources."

"I find the most valuable feature in Azure Data Factory to be its ability to handle large datasets."

"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."

More Azure Data Factory pros

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

Cons

"I would like to be informed about the changes ahead of time, so we are aware of what's coming."

"It's essentially just a black box. There is some monitoring that can be done, but when something goes wrong, even simple fixes are difficult to troubleshoot."

"The user interface could use improvement. It's not a major issue but it's something that can be improved."

"I did not see any positive impact from Azure Data Factory overall."

"In the next release, it's important that some sort of scheduler for running tasks is added."

"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."

"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."

"The only thing that we're struggling with is increasing the competency of my team, so we think that the Microsoft documentation is too complicated."

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

"ADF is cheaper compared to AWS."

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

"Azure Data Factory gives better value for the price than other solutions such as Informatica."

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

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

"Product is priced at the market standard."

"The solution's pricing is competitive."

"The solution is cheap."

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

Financial Services Firm

11%

Manufacturing Company

Computer Software Company

Construction 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

Palantir Foundry vs Azure Data Factory

Compared 3% of the time

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

Compared 3% of the time

SnapLogic vs Azure Data Factory

Compared 3% of the time

More Azure Data Factory Competitors

SAP Data Services vs CloverDX Designer

Compared 43% of the time

SSIS vs CloverDX Designer

Compared 40% of the time

Ab Initio Co>Operating System vs CloverDX Designer

Compared 10% 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

September 2026

Download Azure Data Factory product report

Buyer's Guide

Data Integration

September 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, Palantir, Microsoft and others in Data Integration. Updated: September 2026.

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