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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
5th
Average Rating
8.0
Reviews Sentiment
6.7
Number of Reviews
96
Ranking in other categories
Cloud Data Warehouse (7th)
Dataloader.io
Ranking in Data Integration
50th
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 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 Dataloader.io is 0.7%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration Mindshare Distribution
ProductMindshare (%)
Azure Data Factory2.3%
Dataloader.io0.7%
Other97.0%
Data Integration
 

Featured Reviews

KandaswamyMuthukrishnan - PeerSpot reviewer
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.
reviewer2542599 - PeerSpot reviewer
Lead Database Administrator at a insurance company with 201-500 employees
Integrating external keys seamlessly while has transaction constraints
I find DataLoader's ability to easily integrate with external keys valuable, which is a bit more challenging with DBM. It provides automation for scheduling data loads, and we use the server's functionality for this. Additionally, DataLoader is cost-effective since it is free. As long as I have stable network access, uploading and downloading data is straightforward.

Quotes from Members

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

Pros

"It is a complete ETL Solution."
"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."
"Feature-wise, one of the most valuable ones is the data flows introduced recently in the solution."
"The function of the solution is great."
"I like that it's a monolithic data platform. This is why we propose these solutions."
"It is easy to deploy workflows and schedule jobs."
"Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data."
"It works very well with Azure Data Factory to pull the records, parse them quickly and post them in the database and data warehouse."
"DataLoader is cost-effective since it is free."
"he product’s most valuable feature is ease of access."
"I find DataLoader's ability to easily integrate with external keys valuable, which is a bit more challenging with DBM."
 

Cons

"I have encountered a problem with the integration with third-party solutions, particularly with SAP."
"Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue."
"Azure Data Factory didn't bring a lot of good when we were also using Alteryx."
"You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats."
"Understanding the pricing model for Data Factory is quite complex. It needs to be simplified, and easier to understand."
"The main challenge with implementing Azure Data Factory is that it processes data in batches, not near real-time. To achieve near real-time processing, we need to schedule updates more frequently, which can be an issue. Its interface needs to be lighter."
"The initial setup is not very straightforward."
"The only thing that we're struggling with is increasing the competency of my team, so we think that the Microsoft documentation is too complicated."
"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."
 

Pricing and Cost Advice

"Pricing appears to be reasonable in my opinion."
"The price is fair."
"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."
"The solution is cheap."
"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."
"Data Factory is expensive."
"Our licensing fees are approximately 15,000 ($150 USD) per month."
"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."
"The product is inexpensive and economical."
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Top Industries

By visitors reading reviews
Financial Services Firm
12%
Manufacturing Company
9%
Computer Software Company
9%
Construction Company
7%
Construction Company
20%
Comms Service Provider
18%
Insurance Company
8%
Transportation Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise21
Large Enterprise63
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 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.
What advice do you have for others considering Dataloader.io?
For small to mid-range businesses, DataLoader is perfectly fine, offering everything needed for uploading. On a scale of one to ten, I would rate DataLoader a seven or eight depending on specific n...
 

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: August 2026.
908,800 professionals have used our research since 2012.