Azure Data Factory vs CloverETL comparison

Microsoft and Javlin are both solutions in the Data Integration category. Microsoft is ranked #5 with an average rating of 8.1, while Javlin is ranked #58. Microsoft holds a 2.3% 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 96 Azure Data Factory reviews

13,392 Views
9,240 Comparison Views

92% willing to recommend

CloverETL

Read 2 CloverETL reviews

2,180 Views
1,330 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: June 2026).

Buyer's Guide

Azure Data Factory vs. CloverETL

June 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

5th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (7th)

CloverETL

Ranking in Data Integration

58th

Average Rating

7.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Visualization (35th)

Mindshare comparison

As of July 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.3%, down from 7.6% 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	2.3%
CloverETL	0.8%
Other	96.9%

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

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

"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 data copy template is a valuable feature, and with the pipeline template, it takes only a few clicks for the on-premises data to come in."

"The solution is okay."

"The most important feature is that it can help you do the multi-threading concepts."

"The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted."

"It's a good tool, a good product that does what it's supposed to do well, which is ingesting data from a source to your target, to another cloud, to another source."

"The security of the agent that is installed on-premises is very good."

More Azure Data Factory pros

"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."

"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."

"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."

Cons

"On the UI side, they could make it a little more intuitive in terms of how to add the radius components. Somebody who has been working with tools like Informatica or DataStage gets very used to how the UI looks and feels."

"When working with AWS, we have noticed that the difference between ADF and AWS is that AWS is more customer-focused. They're more responsive compared to any other company. ADF is not as good as AWS, but it should be. If AWS is ten out of ten, ADF is around eight out of ten. I think AWS is easier to understand from the GUI perspective compared to ADF."

"This solution is currently only useful for basic data movement and file extractions, which we would like to see developed to handle more complex data transformations."

"The pricing model should be more transparent and available online."

"There is one particular problem with Azure Data Factory. When you have a parent-to-child relationship and the child has one more relationship, creating a hierarchy situation, there are issues."

"Areas for improvement would be the product's performance and its mapping of data flow."

"Azure Data Factory uses many resources and has issues with parallel workflows."

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

More Azure Data Factory cons

"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."

"Its documentation could be improved."

"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

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

"I would not say that this product is overly expensive."

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

"The solution is cheap."

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

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

"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."

"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

Manufacturing Company

Construction Company

28%

Manufacturing Company

13%

Computer Software Company

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	21
Large Enterprise	63

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 5% of the time

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

Compared 4% of the time

Databricks vs Azure Data Factory

Compared 4% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 3% of the time

Palantir Foundry vs Azure Data Factory

Compared 3% of the time

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SSIS vs CloverETL

Compared 10% of the time

ViaSQL by Adaptigent vs CloverETL

Compared 9% of the time

Pentaho Data Integration and Analytics vs CloverETL

Compared 9% of the time

Tableau Enterprise vs CloverETL

Compared 7% of the time

Informatica PowerCenter vs CloverETL

Compared 6% of the time

More CloverETL Competitors

Product Reports

Buyer's Guide

Azure Data Factory

June 2026

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Buyer's Guide

Data Integration

June 2026

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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

June 2026

Free Report: Azure Data Factory vs. CloverETL

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