Azure Data Factory vs Skyvia comparison

Microsoft and Skyvia are both solutions in the Data Integration category. Microsoft is ranked #1 with an average rating of 8.1, while Skyvia is ranked #56. Microsoft holds a 10.1% mindshare in DI, compared to Skyvia’s 0.3% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 100% of Skyvia users who would recommend it.

Azure Data Factory

Read 90 Azure Data Factory reviews

19,379 Views
15,325 Comparison Views

92% willing to recommend

Skyvia

Read 1 Skyvia review

263 Views
230 Comparison Views

100% willing to recommend

Azure Data Factory

Skyvia

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Executive SummaryUpdated on Feb 6, 2025

Skyvia and Azure Data Factory are competing in the data integration and management space. Azure Data Factory appears to have the upper hand due to its extensive feature set, making it suitable for large-scale, complex operations.

Features: Skyvia provides cloud synchronization, data migration, and automation tools without requiring coding. Azure Data Factory offers powerful ETL capabilities, integration with multiple Azure services, advanced data transformation options. The main difference is in integration capabilities, with Azure Data Factory being more suitable for sophisticated workflows.

Ease of Deployment and Customer Service: Skyvia offers an easily deployable cloud-based platform with straightforward customer support, making initial setup simple for smaller teams. Azure Data Factory, needing more investment in deployment skills, offers comprehensive support through Microsoft's ecosystem, appealing to enterprises with technical resources.

Pricing and ROI: Skyvia provides a cost-effective solution with simpler pricing, ideal for businesses seeking quick ROI. Azure Data Factory, while generally more expensive, offers significant ROI through scalability and feature-rich environment, advantageous for larger enterprises ready to invest in extensive data projects.

To learn more, read our detailed Data Integration Report (Updated: January 2025).

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Data Integration

January 2025

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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

1st

Average Rating

8.0

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (3rd)

Skyvia

Ranking in Data Integration

56th

Average Rating

9.0

Reviews Sentiment

7.8

Number of Reviews

Ranking in other categories

Cloud Data Integration (32nd)

Mindshare comparison

As of February 2025, in the Data Integration category, the mindshare of Azure Data Factory is 10.1%, down from 12.9% compared to the previous year. The mindshare of Skyvia is 0.3%, up from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration

Featured Reviews

Joy Maitra

Sr. Technical Architect at Hexaware Technologies Limited

Facilitates seamless data pipeline creation with good analytics and and thorough monitoring

Azure Data Factory is a low code, no code platform, which is helpful. It provides many prebuilt functionalities that assist in building data pipelines. Also, it facilitates easy transformation with all required functionalities for analytics. Furthermore, it connects to different sources out-of-the-box, making integration much easier. The monitoring is very thorough, though a more readable version would be appreciable.

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Robert-Harris

CTO & Developer 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.

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Top Industries

By visitors reading reviews

Financial Services Firm

13%

Computer Software Company

12%

Manufacturing Company

Healthcare Company

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.

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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...

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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

Informatica PowerCenter vs Azure Data Factory

Compared 13% of the time

Snowflake vs Azure Data Factory

Compared 10% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Azure Data Factory

Compared 8% of the time

Alteryx Designer vs Azure Data Factory

Compared 7% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 5% of the time

More Azure Data Factory Competitors

SSIS vs Skyvia

Compared 84% of the time

Boomi iPaaS vs Skyvia

Compared 16% of the time

More Skyvia Competitors

Product Reports

Buyer's Guide

Azure Data Factory

February 2025

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Buyer's Guide

Cloud Data Integration

January 2025

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Also Known As

No data available

Skyvia, Skyvia Data Integration

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

Skyvia is a cloud-based data integration platform enabling seamless data connection, management, and analysis across cloud apps and databases.

Skyvia offers a comprehensive suite for ETL, data backup, replication, and connectivity across major cloud services. It's designed for users requiring robust data manipulation and transfer capabilities without extensive coding knowledge. Its intuitive tools simplify complex tasks like data synchronization and transformation.

What are the key features of Skyvia?

Data Integration: Connects disparate systems for seamless data flow.
Cloud Backup: Provides automated, secure backups of essential data.
Data Replication: Ensures reliable data copying for high availability.
Migration Services: Facilitates easy data migration between platforms.
Data Connectors: Extensive support for databases and apps.

What benefits and ROI should users consider?

Ease of Use: Streamlined processes reduce manual effort and potential errors.
Cost-Effective: Offers flexible pricing models suitable for different needs.
Scalability: Adapts as data needs grow without significant investment.
Time-Saving: Automates time-consuming tasks, increasing productivity.

Industries like e-commerce, finance, and healthcare implement Skyvia to achieve efficient data processing and analytics integration, ensuring consistent data flow and real-time insights into operations.

Skyvia

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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