Azure Data Factory vs CloverETL comparison

Why PeerSpot?

Microsoft and Javlin are both solutions in the Data Integration category. Microsoft is ranked #6 with an average rating of 8.0, while Javlin is ranked #61. Microsoft holds a 2.2% mindshare in DI, compared to Javlin’s 0.7% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 100% of Javlin 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

CloverETL

Read 2 CloverETL reviews

2,886 Views
1,732 Comparison Views

100% willing to recommend

Azure Data Factory

CloverETL

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 19, 2024

CloverETL and Azure Data Factory compete in the data integration platform category. Azure Data Factory has the upper hand due to its advanced features and scalability, ideal for larger enterprises.

Features: CloverETL provides customizable data transformation, a wide range of connectors, and flexibility, beneficial for small to mid-sized teams. Azure Data Factory supports extensive Microsoft ecosystem integrations, seamless cloud and on-premises data movement, and robust scheduling, appealing to enterprises with complex workflow needs.

Ease of Deployment and Customer Service: Azure Data Factory users benefit from extensive Microsoft support resources and comprehensive documentation, which streamlines deployment for Microsoft-experienced teams. CloverETL offers a straightforward deployment model suitable for organizations with limited resources, but its support availability is less extensive.

Pricing and ROI: CloverETL's competitive pricing and lower initial setup costs provide a favorable ROI for small to mid-sized businesses. Azure Data Factory, with pricing reflecting its enterprise-level capabilities, requires a higher initial investment, yet offers scalable solutions that justify the cost for businesses with complex data processing needs.

To learn more, read our detailed Azure Data Factory vs. CloverETL Report (Updated: September 2026).

Buyer's Guide

Azure Data Factory vs. CloverETL

September 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

Ranking in Data Integration

6th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (6th)

CloverETL

Ranking in Data Integration

61st

Average Rating

7.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Visualization (34th)

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 CloverETL is 0.7%, up from 0.5% 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%
CloverETL	0.7%
Other	97.1%

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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it_user856614

Lead Programmer at a healthcare company with 10,001+ employees

Very easy to schedule jobs and monitor them, however we run out heap space even with a high allocation

Flexibility: We can bring in data from multiple sources, e.g., databases, text files, JSON, email, XML, etc. This has been very helpful Connectivity to various data sources: The ability to extract data from different data sources gives greater flexibility. Server features for scheduler: It is…

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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

"We have used other ETL solutions in the past, and Azure Data Factory is the best one."

"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."

"Microsoft supported us when we planned to provision Azure Data Factory over a private link. As a result, we received excellent support from Microsoft."

"It is very modular. It works well. We've used Data Factory and then made calls to libraries outside of Data Factory to do things that it wasn't optimized to do, and it worked really well. It is obviously proprietary in regards to Microsoft created it, but it is pretty easy and direct to bring in outside capabilities into Data Factory."

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

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

"The most valuable features are data transformations."

"My only advice is that Azure Data Factory, particularly for data ingestion, is a good choice."

More Azure Data Factory pros

"Server features for scheduler: It is very easy to schedule jobs and monitor them. The interface is easy to use."

"Familiar, intuitive GUI coming from a Java development background, in-depth, descriptive, and well-laid-out documentation, responsive support through forums directly from Clover staff, a wealth of customizable pre-defined components, descriptive logging for error messages, and ease of install with a light footprint make it very effective to use."

"Connectivity to various data sources: The ability to extract data from different data sources gives greater flexibility."

"Key features include wealth of pre-defined components; all components are customizable; descriptive logging, especially for error messages."

"No dependence on native language and ease of use."

"We switched to CloverETL because of its flexibility to connect to various data sources and no dependence on native language and ease of use."

Cons

"They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas."

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

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

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

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

"Data Factory's performance during heavy data processing isn't great."

"There is always room to improve. There should be good examples of use that, of course, customers aren't always willing to share. It is Catch-22. It would help the user base if everybody had really good examples of deployments that worked, but when you ask people to put out their good deployments, which also includes me, you usually got, "No, I'm not going to do that." They don't have enough good examples. Microsoft probably just needs to pay one of their partners to build 20 or 30 examples of functional Data Factories and then share them as a user base."

"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog."

More Azure Data Factory cons

"Its documentation could be improved."

"Needs easier automated failure recovery, more and more intuitive auto-generated or filled-in code for components, and easier or more automated sync between CloverETL Designer and CloverETL Server."

"Resource management: We typically run out of heap space, and even the allocation of high heap space does not seem to be enough."

Pricing and Cost Advice

"The licensing cost is included in the Synapse."

"ADF is cheaper compared to AWS."

"Azure products generally offer competitive pricing, suitable for diverse budget considerations."

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

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

"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."

"I don't see a cost; it appears to be included in general support."

"Pricing appears to be reasonable in my opinion."

More Azure Data Factory pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

11%

Manufacturing Company

Computer Software Company

Construction Company

26%

Manufacturing Company

13%

Comms Service Provider

13%

Computer Software Company

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

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Compared 4% of the time

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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

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Compared 9% of the time

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ViaSQL by Adaptigent vs CloverETL

Compared 7% of the time

Tableau Enterprise vs CloverETL

Compared 6% of the time

Informatica PowerCenter vs CloverETL

Compared 6% of the time

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Product Reports

Buyer's Guide

Azure Data Factory

October 2026

Download Azure Data Factory product report

Buyer's Guide

Data Integration

September 2026

Download CloverETL 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

CloverETL is a data integration and transformation tool tailored for businesses seeking efficient data processing capabilities. It is known for its ability to automate complex data workflows, making it an essential asset for IT departments.

With its focus on flexibility and efficiency, CloverETL enables businesses to streamline data management processes. It supports data integration, transformation, and orchestration across diverse platforms. Users find it particularly valuable in scenarios involving large-scale data processing, where automated data pipeline management is crucial.

What are the most important features of CloverETL?

Data Transformation: Efficiently alters data formats to meet system needs.
ETL Automation: Streamlines workflows with robust automated processes.
Scalability: Adapts to growing data demands seamlessly.
Cross-Platform Connectivity: Integrates easily across multiple data sources.

What benefits or ROI should users look for?

Reduced Operational Costs: Automation decreases manual intervention-related expenses.
Improved Data Consistency: Ensures accurate and reliable data across systems.
Enhanced Productivity: Saves time with faster data transformations.

In industries like finance and healthcare, CloverETL is implemented to manage and analyze large volumes of data. Its capability to adapt to industry-specific requirements makes it an effective choice for businesses aiming to enhance their data operational efficiency.

Javlin

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

IBM, Oracle, MuleSoft, GoodData, Thomson Reuters, salesforce.com, Comcast, Active Network, SHOP.CA

Buyer's Guide

Azure Data Factory vs. CloverETL

September 2026

Free Report: Azure Data Factory vs. CloverETL

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