Azure Data Factory vs CloverETL comparison

Microsoft and Javlin are both solutions in the Data Integration category. Microsoft is ranked #4 with an average rating of 8.2, while Javlin is ranked #57. Microsoft holds a 2.4% mindshare in DI, compared to Javlin’s 0.8% 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 94 Azure Data Factory reviews

12,241 Views
8,446 Comparison Views

92% willing to recommend

CloverETL

Read 2 CloverETL reviews

1,766 Views
1,060 Comparison Views

100% willing to recommend

Azure Data Factory

CloverETL

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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: April 2026).

Buyer's Guide

Azure Data Factory vs. CloverETL

April 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

4th

Average Rating

8.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (5th)

CloverETL

Ranking in Data Integration

57th

Average Rating

7.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Visualization (31st)

Mindshare comparison

As of May 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.4%, down from 8.6% compared to the previous year. The mindshare of CloverETL is 0.8%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Mindshare Distribution
Product	Mindshare (%)
Azure Data Factory	2.4%
CloverETL	0.8%
Other	96.8%

Data Integration

Featured Reviews

KandaswamyMuthukrishnan

Director at a computer software company with 1,001-5,000 employees

Integrates diverse data sources and streamlines ETL processes effectively

Regarding potential areas of improvement for Azure Data Factory, there is a need for better data transformation, especially since many people are now depending on DataBricks more for connectivity and data integration. Azure Data Factory should consider how to enhance integration or filtering for more transformations, such as integrating with Spark clusters. I am satisfied with Azure Data Factory so far, but I suggest integrating some AI functionality to analyze data during the transition itself, providing insights such as null records, common records, and duplicates without running a separate pipeline or job. The monitoring tools in Azure Data Factory are helpful for optimizing data pipelines; while the current feature is adequate, they can improve by creating a live dashboard to see the online process, including how much percentage has been completed, which will be very helpful for people who are monitoring the pipeline.

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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

"Azure Data Factory is great because it's a cloud service; you do not have to take care of the installation and configuration yourself."

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

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

"I think it makes it very easy to understand what data flow is and so on; you can leverage the user interface to do the different data flows, and it's great, I like it a lot."

"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."

"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 overall performance is quite good."

"Azure Data Factory's most valuable features are the packages and the data transformation that it allows us to do, which is more drag and drop, or a visual interface, so that eases the entire process."

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."

"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."

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

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

Cons

"When you raise an issue, sometimes the people who are available are unfamiliar with that particular technology, so they have to route the issue to the concerned person."

"There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run."

"Data Factory has so many features that it can be a little difficult or confusing to find some settings and configurations."

"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."

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

"Currently, our company requires a monitoring tool, and that isn't available in Azure Data Factory."

"The solution needs to be more connectable to its own services."

"Azure Data Factory didn't bring a lot of good when we were also using Alteryx."

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."

"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."

"Needs: easier automated failure recovery; more, and more intuitive auto-generated/filled-in code for components; easier/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."

"Its documentation could be improved."

Pricing and Cost Advice

"My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use. On a scale of one to five, pricing for Azure Data Factory is a four. It's just the usage fees my company pays monthly."

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

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

"ADF is cheaper compared to AWS."

"Understanding the pricing model for Data Factory is quite complex."

"The pricing is a bit on the higher end."

"Product is priced at the market standard."

"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."

More Azure Data Factory pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

12%

Computer Software Company

10%

Manufacturing Company

Government

Construction Company

27%

Manufacturing Company

13%

Computer Software Company

10%

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	20
Large Enterprise	57

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

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

Compared 5% of the time

Snowflake vs Azure Data Factory

Compared 4% of the time

Databricks vs Azure Data Factory

Compared 3% of the time

Palantir Foundry vs Azure Data Factory

Compared 3% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 3% of the time

More Azure Data Factory Competitors

Pentaho Data Integration and Analytics vs CloverETL

Compared 11% of the time

SSIS vs CloverETL

Compared 9% of the time

ViaSQL by Adaptigent vs CloverETL

Compared 9% of the time

Tableau Enterprise vs CloverETL

Compared 8% of the time

Informatica PowerCenter vs CloverETL

Compared 7% of the time

More CloverETL Competitors

Product Reports

Buyer's Guide

Azure Data Factory

May 2026

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Buyer's Guide

Data Integration

May 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

April 2026

Free Report: Azure Data Factory vs. CloverETL

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