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Azure Data Factory vs Dataloader.io comparison

 

Comparison Buyer's Guide

Executive Summary

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
1st
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
92
Ranking in other categories
Cloud Data Warehouse (2nd)
Dataloader.io
Ranking in Data Integration
45th
Average Rating
7.6
Reviews Sentiment
7.5
Number of Reviews
2
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of October 2025, in the Data Integration category, the mindshare of Azure Data Factory is 5.2%, down from 11.0% compared to the previous year. The mindshare of Dataloader.io is 0.3%, up from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration Market Share Distribution
ProductMarket Share (%)
Azure Data Factory5.2%
Dataloader.io0.3%
Other94.5%
Data Integration
 

Featured Reviews

KandaswamyMuthukrishnan - PeerSpot reviewer
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.
Aditi Bhardwaj - PeerSpot reviewer
Provides an ease of access and an automated mapping feature
We need help with large data migrations. It only works well for a few thousand records or less than a million records. Above that, we need to look for alternative solutions. They could provide automated transformation or mapping features around 10 to 15 independent data objects. We could have a default mark or limit of free usage for standard objects. It will be helpful. Additionally, we can have more integrations with large data volumes as we need a lot of exercises to handle the files in case of complex sites.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"It is beneficial that the solution is written with Spark as the back end."
"We use the solution to move data from on-premises to the cloud."
"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."
"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."
"The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."
"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."
"From my experience so far, the best feature is the ability to copy data to any environment. We have 100 connects and we can connect them to the system and copy the data from its respective system to any environment. That is the best feature."
"Azure Data Factory became more user-friendly when data-flows were introduced."
"DataLoader is cost-effective since it is free."
"I find DataLoader's ability to easily integrate with external keys valuable, which is a bit more challenging with DBM."
"he product’s most valuable feature is ease of access."
 

Cons

"The Microsoft documentation is too complicated."
"There's space for improvement in the development process of the data pipelines."
"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."
"Occasionally, there are problems within Microsoft itself that impacts the Data Factory and causes it to fail."
"Lacks in-built streaming data processing."
"There is a need for better data transformation, especially since many people are now depending on DataBricks more for connectivity and data integration."
"I rate Azure Data Factory six out of 10 for stability. ADF is stable now, but we had problems recently with indexing on an SQL database. It's slow when dealing with a huge volume of data. It depends on whether the database is configured as general purpose or hyperscale."
"If the user interface was more user friendly and there was better error feedback, it would be helpful."
"Dataloader has limitations, including constraints with file sizes and transactions. Additionally, at times it can be slow, and when integrating DBM, we find it more complex than Dataloader."
"We need help with large data migrations. It only works well for a few thousand records or less than a million records."
"DataLoader has limitations, including constraints with file sizes and transactions."
 

Pricing and Cost Advice

"Data Factory is affordable."
"I would rate Data Factory's pricing nine out of ten."
"Understanding the pricing model for Data Factory is quite complex."
"The licensing is a pay-as-you-go model, where you pay for what you consume."
"Azure Data Factory gives better value for the price than other solutions such as Informatica."
"The licensing model for Azure Data Factory is good because you won't have to overpay. Pricing-wise, the solution is a five out of ten. It was not expensive, and it was not cheap."
"Pricing is comparable, it's somewhere in the middle."
"ADF is cheaper compared to AWS."
"The product is inexpensive and economical."
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
12%
Manufacturing Company
9%
Government
7%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise19
Large Enterprise55
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.
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...
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...
What do you like most about Dataloader.io?
he product’s most valuable feature is ease of access.
What is your experience regarding pricing and costs for Dataloader.io?
Dataloader.io is cost-effective, particularly since it is free.
What needs improvement with Dataloader.io?
DataLoader has limitations, including constraints with file sizes and transactions. Additionally, at times it can be slow, and when integrating DBM, we find it more complex than DataLoader.
 

Overview

 

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
UCSF, Box, CareFusion, Unilever, Hershey's
Find out what your peers are saying about Azure Data Factory vs. Dataloader.io and other solutions. Updated: September 2025.
869,760 professionals have used our research since 2012.