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Azure Data Factory vs Skyvia comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Mar 1, 2026

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
94
Ranking in other categories
Cloud Data Warehouse (5th)
Skyvia
Ranking in Data Integration
56th
Average Rating
9.0
Reviews Sentiment
7.8
Number of Reviews
1
Ranking in other categories
Cloud Data Integration (26th)
 

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 Skyvia is 0.7%, up from 0.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration Mindshare Distribution
ProductMindshare (%)
Azure Data Factory2.4%
Skyvia0.7%
Other96.9%
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.
RH
CTO & Developer at a consultancy with self employed
The product works, is simple to use, and is reliable.
Error handling. This has caused me many problems in the past. When an error occurs, the event on the connection that is called does not seem to behave as documented. If I attempt a retry or opt not to display an error dialog, it does it anyway. In all fairness, I have never reported this. I think it is more important that a unique error code is passed to the error event that identifies a uniform type of error that occurred, such as ecDisconnect, eoInvalidField. It is very hard to find what any of the error codes currently passed actually mean. A list would be great for each database engine. Trying to catch an exception without displaying the UniDAC error message is impossible, no matter how you modify the parameters in the OnError of the TUniConnection object. I have already implemented the following things myself. They are suggestions rather than specific requests. Copy Datasets: This contains an abundance of redundant options. I think that a facility to copy one dataset to another in a single call would be handy. Redundancy: I am currently working on this. I have extended the TUniConnection to have an additional property called FallbackConnection. If the TUniConnection goes offline, the connection attempts to connect the FallbackConnection. If successful, it then sets the Connection properties of all live UniDatasets in the app to the FallbackConnection and re-opens them if necessary. The extended TUniConnection holds a list of datasets that were created. Each dataset is responsible for registering itself with the connection. This is a highly specific feature. It supports an offline mode that is found in mission critical/point of sale solutions. I have never seen it implement before in any DACs, but I think it is a really unique feature with a big impact. Dataset to JSON/XML: A ToSql function on a dataset that creates a full SQL Text statement with all parameters converted to text (excluding blobs) and included in the returned string. Extended TUniScript:- TMyUniScript allows me to add lines of text to a script using the normal dataset functions, Script.Append, Script.FieldByName(‘xxx’).AsString := ‘yyy’, Script.AddToScript and finally Script.Post, then Script.Commit. The AddToScript builds the SQL text statement and appends it to the script using #e above. Record Size Calculation. It would be great if UniDac could estimate the size of a particular record from a query or table. This could be used to automatically set the packet fetch/request count based on the size of the Ethernet packets on the local area network. This I believe would increase performance and reduce network traffic for returning larger datasets. I am aware that this would also be a unique feature to UniDac but would gain a massive performance enhancement. I would suggest setting the packet size on the TUniConnection which would effect all linked datasets.

Quotes from Members

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

Pros

"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."
"One of the most valuable features of Azure Data Factory is the drag-and-drop interface, which helps with workflow management because we can just drag any tables or data sources we need and, because of how easy it is to drag and drop, we can deliver things very quickly."
"I find that the solution integrates well with cloud technologies, which we are using for different clouds like Snowflake and AWS"
"It is easy to deploy workflows and schedule jobs."
"Azure Data Factory is an integration tool, an orchestration service tool; it is for data integration for the cloud."
"Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data."
"One of the most valuable features of Azure Data Factory is the drag-and-drop interface. This helps with workflow management because we can just drag any tables or data sources we need. Because of how easy it is to drag and drop, we can deliver things very quickly. It's more customizable through visual effect."
"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."
"For what it offers, I think this solution is a must for any Delphi programmer."
 

Cons

"For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy."
"We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on."
"They should work on optimizing their licensing model and pricing structure."
"Lacks in-built streaming data processing."
"The user interface could use improvement. It's not a major issue but it's something that can be improved."
"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."
"I find that Azure Data Factory is still maturing, so there are issues."
"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."
"Error handling has caused me many problems in the past; when an error occurs, the event on the connection that is called does not seem to behave as documented."
 

Pricing and Cost Advice

"The price is fair."
"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."
"This is a cost-effective solution."
"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."
"The cost is based on the amount of data sets that we are ingesting."
"The licensing is a pay-as-you-go model, where you pay for what you consume."
"Our licensing fees are approximately 15,000 ($150 USD) per month."
"Pricing is comparable, it's somewhere in the middle."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
12%
Computer Software Company
10%
Manufacturing Company
9%
Government
6%
Performing Arts
20%
Construction Company
11%
Outsourcing Company
8%
Computer Software Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise20
Large Enterprise57
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...
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Earn 20 points
 

Also Known As

No data available
Skyvia, Skyvia Data Integration
 

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
Boeing, Sony, Honda, Oracle, BMW, Samsung
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