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Azure Data Factory vs Devart Skyvia comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Azure Data Factory
Average Rating
8.0
Number of Reviews
85
Ranking in other categories
Data Integration (1st), Cloud Data Warehouse (3rd)
Devart Skyvia
Average Rating
9.0
Number of Reviews
1
Ranking in other categories
Cloud Data Integration (31st)
 

Mindshare comparison

While both are Data Integration and Access solutions, they serve different purposes. Azure Data Factory is designed for Data Integration and holds a mindshare of 11.1%, down 13.3% compared to last year.
Devart Skyvia, on the other hand, focuses on Cloud Data Integration, holds 0.2% mindshare, up 0.2% since last year.
Data Integration
Cloud Data Integration
 

Featured Reviews

Camilo Velasco - PeerSpot reviewer
Oct 27, 2022
No deployment cost, quick implementation, pay only for the processing time and data
The primary use case of this solution is to extract ETLS, transform and load data, and organize database synchronization The most valuable feature of this solution is the data flow, which is the same SQL server in important service, integration services, which is a very robust and powerful tool…
RH
Jul 5, 2017
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:
 

Pricing and Cost Advice

"Azure products generally offer competitive pricing, suitable for diverse budget considerations."
"The price you pay is determined by how much you use it."
"The solution's pricing is competitive."
"Our licensing fees are approximately 15,000 ($150 USD) per month."
"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."
"Product is priced at the market standard."
"Pricing appears to be reasonable in my opinion."
"The cost is based on the amount of data sets that we are ingesting."
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Top Industries

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

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
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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Also Known As

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Skyvia
 

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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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814,763 professionals have used our research since 2012.