Azure Data Factory vs CloverDX Designer comparison

Why PeerSpot?

Microsoft and CloverDX are both solutions in the Data Integration category. Microsoft is ranked #5 with an average rating of 8.0, while CloverDX is ranked #88. Microsoft holds a 2.2% mindshare in DI, compared to CloverDX’s 0.4% 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

14,793 Views
10,207 Comparison Views

92% willing to recommend

CloverDX Designer

Read 1 CloverDX Designer review

1,012 Views
982 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 914,262 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

5th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (7th)

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 September 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.2%, down from 5.5% compared to the previous year. The mindshare of CloverDX Designer is 0.4%, 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	0.4%
Other	97.4%

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

"It works very well with Azure Data Factory to pull the records, parse them quickly and post them in the database and data warehouse."

"The most valuable feature of this solution is that it allows more data between on-premises and cloud solutions."

"The most valuable aspect is the copy capability."

"Data Flow and Databricks are going to be extremely valuable services, allowing data solutions to scale as the business grows and new data sources are added."

"The most valuable part of this product is the ease of use, as it is easy to use and rather intuitive, and because it is easy to use, you can do things with it easily, making your work easier and therefore more valuable."

"The trigger scheduling options are decently robust."

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

More Azure Data Factory pros

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

Cons

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

"The solution should offer better integration with Azure machine learning. We should be able to embed the cognitive services from Microsoft, for example as a web API. It should allow us to embed Azure machine learning in a more user-friendly way."

"Additionally, the ability to handle the largest volumes of data is another concern; if I have to manage more than one terabyte of data every day, I am not comfortable dealing with Azure Data Factory and had to switch to Oracle Data Integrators (ODI) because it lacks performance features."

"There aren't many third-party extensions or plugins available in the solution."

"It would be helpful if they could adjust the data capture feature so that when there are source-side changes ADF could automatically figure it out."

"There's space for improvement in the development process of the data pipelines."

"The product integration with advanced coding options could cater to users needing more customization."

"You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats."

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

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

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

"I would rate Data Factory's pricing nine out of ten."

"The price you pay is determined by how much you use it."

"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."

"Data Factory is affordable."

"Pricing appears to be reasonable in my opinion."

"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."

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...

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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

More Azure Data Factory Competitors

SAP Data Services vs CloverDX Designer

Compared 43% of the time

SSIS vs CloverDX Designer

Compared 39% of the time

Ab Initio Co>Operating System vs CloverDX Designer

Compared 11% 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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