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 #62. 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

16,078 Views
11,255 Comparison Views

92% willing to recommend

CloverETL

Read 2 CloverETL reviews

3,219 Views
1,931 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: November 2026).

Buyer's Guide

Azure Data Factory vs. CloverETL

November 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 (8th)

CloverETL

Ranking in Data Integration

62nd

Average Rating

7.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Visualization (34th)

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 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

"The overall architecture has been very valuable to us, as it has allowed us to scale up pretty rapidly."

"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."

"Data Factory itself is great, it's pretty straightforward, you can easily add sources, join and lookup information, etc., and the ease of use is pretty good."

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

"Data Factory's most valuable feature is Copy Activity."

"The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."

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

"UI is easy to navigate and I can retrieve VTL code without knowing in-depth coding languages."

More Azure Data Factory pros

"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."

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

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

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

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

Cons

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

"Data Factory could be improved in terms of data transformations by adding more metadata extractions."

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

"The performance and stability are touch and go."

"They should work on optimizing their licensing model and pricing structure."

"I have encountered a problem with the integration with third-party solutions, particularly with SAP."

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

"Real-time replication is required, and this is not a simple task."

More Azure Data Factory cons

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

"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."

Pricing and Cost Advice

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

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

"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."

"ADF is cheaper compared to AWS."

"Pricing appears to be reasonable in my opinion."

"The solution is cheap."

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

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

More Azure Data Factory pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Manufacturing Company

11%

Financial Services Firm

10%

Outsourcing Company

Computer Software Company

Construction Company

25%

Comms Service Provider

16%

Manufacturing Company

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...

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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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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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Compared 8% of the time

Pentaho Data Integration and Analytics vs CloverETL

Compared 7% of the time

ViaSQL by Adaptigent vs CloverETL

Compared 6% of the time

Informatica PowerCenter vs CloverETL

Compared 5% of the time

Tableau Enterprise vs CloverETL

Compared 5% 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

October 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

November 2026

Free Report: Azure Data Factory vs. CloverETL

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