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 7.9, 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

14,099 Views
9,728 Comparison Views

92% willing to recommend

CloverETL

Read 2 CloverETL reviews

2,406 Views
1,468 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: August 2026).

Buyer's Guide

Azure Data Factory vs. CloverETL

August 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 August 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.3%, down from 7.2% compared to the previous year. The mindshare of CloverETL is 0.8%, 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	2.3%
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

"The stability of the Azure Data Factory is very good."

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

"The data flows were beneficial, allowing us to perform multiple transformations."

"Powerful but easy-to-use and intuitive."

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

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

"It is very modular; it works well, is very flexible, and you can easily bring in outside capabilities and build any features you want."

"It's extremely consistent."

More Azure Data Factory pros

"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."

"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."

"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."

Cons

"Some of the optimization techniques are not scalable."

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

"Data Factory's monitorability could be better."

"The performance could be better. It would be better if Azure Data Factory could handle a higher load. I have heard that it can get overloaded, and it can't handle it."

"The stability of Azure as a PaaS could be improved."

"Occasionally, there are problems within Microsoft itself that impacts the Data Factory and causes it to fail."

"Azure Data Factory is a bit complicated compared to Informatica. There are a lot of connectors that are missing and there are a lot of instances where I need to create a server and install Integration Runtime."

"It's essentially just a black box. There is some monitoring that can be done, but when something goes wrong, even simple fixes are difficult to troubleshoot."

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

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

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

"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."

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

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

"The solution is cheap."

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

"Data Factory is affordable."

More Azure Data Factory pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

12%

Manufacturing Company

Computer Software Company

Construction Company

27%

Manufacturing Company

12%

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...

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 4% 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

SSIS vs CloverETL

Compared 9% of the time

Pentaho Data Integration and Analytics vs CloverETL

Compared 9% of the time

ViaSQL by Adaptigent vs CloverETL

Compared 8% of the time

Tableau Enterprise vs CloverETL

Compared 7% of the time

Informatica PowerCenter vs CloverETL

Compared 7% of the time

More CloverETL Competitors

Product Reports

Buyer's Guide

Azure Data Factory

August 2026

Download Azure Data Factory product report

Buyer's Guide

Data Integration

July 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

August 2026

Free Report: Azure Data Factory vs. CloverETL

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