Azure Data Factory Reviews

Name: Azure Data Factory
Brand: Microsoft
Rating: 4.0 (87 reviews)

Vendor: Microsoft

4.0 out of 5

87 reviews
92% willing to recommend

1,342 followers

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What is Azure Data Factory?

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.

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Azure Data Factory is the #1 ranked solution in top Data Integration Tools and #3 ranked solution in top Cloud Data Warehouse solutions. PeerSpot users give Azure Data Factory an average rating of 8.0 out of 10. Based on the analysis of the 87 most recent Azure Data Factory reviews, the overall sentiment is positive, with a sentiment score of 6.9. Azure Data Factory is most commonly compared to Informatica Intelligent Data Management Cloud (IDMC): Azure Data Factory vs Informatica Intelligent Data Management Cloud (IDMC). Azure Data Factory is popular among the large enterprise segment, accounting for 70% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 13% of all views.

Helped 824,052 peers since 2012

Featured reviews

Thulani David Mngadi

Data architect at Old Mutual

The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem.

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Anil Jha

Director D&A at Iris Software Inc.

The primary use case is integrating data from different ERP systems and loading it into Azure Synapse for reporting. We use Power BI for the reporting side of it. We also have customers who are migrating to Azure Data Factory and we are assisting them with making the transition It is easy to…

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Davy Michiels

Company Owner, Data Consultant at Telenet BVBA

The most important feature is the Data Catalog. We need to define all the data fields we test. It has technical information in the Data Catalog. The main feature is data ingestion in ADF. We also extended it to PurView because PurView is an extension of the Azure data catalog. It can scan metadata. There is a limitation in ADF when setting up the data catalog.

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Azure Data Factory mindshare

Product category:

As of December 2024, the mindshare of Azure Data Factory in the Data Integration category stands at 11.0%, down from 13.3% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Data Integration

PeerAnalyst reports

Type	Title	Date
Category	Data Integration	Dec 25, 2024	Download
Product	Reviews, tips, and advice from real users	Dec 25, 2024	Download
Comparison	Azure Data Factory vs Informatica PowerCenter	Dec 25, 2024	Download
Comparison	Azure Data Factory vs Informatica Intelligent Data Management Cloud (IDMC)	Dec 25, 2024	Download
Comparison	Azure Data Factory vs SSIS	Dec 25, 2024	Download

Title	Rating	Mindshare	Recommending
Informatica Intelligent Data Management Cloud (IDMC)	4.0	N/A	93%	181 interviews Add to research
Informatica PowerCenter	4.0	11.0%	91%	80 interviews Add to research

Valuable Features

Azure Data Factory is praised for its ease of use, drag-and-drop interface, extensive connectors, and strong integration with Azure services. Users appreciate its scalability, flexibility, and robust ETL capabilities. The interface simplifies workflow management. Built-in monitoring and security features enhance usability. Data Factory efficiently handles data pipelines, orchestration, and transformations. It integrates well with databases, supports various data sources, and provides strong performance and reliability for business data management.

"The interface of Azure Data Factory is very usable with a more interactive visual experience, making it easier for people who are not as experienced in coding to work with."
"The interface of Azure Data Factory is very usable with a more interactive visual experience, making it easier for people who are not as experienced in coding to work with."
"The valuable feature of Azure Data Factory is its integration capability, as it goes well with other components of Microsoft Azure."

Room for Improvement

Azure Data Factory faces challenges in user interface intuitiveness and documentation complexity. Users express difficulties with the pricing model, integration with other platforms, and missing features like real-time processing and connectors. Performance and error feedback require enhancement, and users report issues with API connectivity and technical support responsiveness. More drag-and-drop options and better data transformation capabilities are desired, alongside improvements in deployment processes and monitoring tools.

"Some prebuilt data source or data connection aspects are generic."
"Customer service is not satisfactory. Third-party personnel handle support and rely on a knowledge repository."
"While it has a range of connectors for various systems, such as ERP systems, the support for these connectors can be lacking."

ROI

Users indicate Azure Data Factory enhances time and effort efficiency, providing a return when contemplating the manual alternatives. Organizations acknowledge savings in data migration and infrastructure overhead. The firm implementation within client setups proves impressive with notable routing value and reported satisfaction, potentially achieving a 20% to 30% return over five years. Azure facilitates insights through centralized data analysis, benefiting data scientists in understanding complex datasets and extracting valuable insights, driving organizational benefits.

Pricing

Azure Data Factory employs a pay-as-you-go pricing model based on data processing and pipeline activities, leading to fluctuations in monthly costs. While some users find the solution affordable and comparable to market standards, others highlight the complexity in estimating costs due to variable consumption patterns. Licensing fees are generally integrated with Azure services, with additional expenses primarily arising when using supplementary computing resources. Users appreciate its competitive pricing but advise careful monitoring of resource usage to manage expenses effectively.

"Azure products generally offer competitive pricing, suitable for diverse budget considerations."
"The solution is cheap."
"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."

Popular Use Cases

Azure Data Factory is primarily utilized for data integration, ETL, and data migration, connecting diverse data sources to Azure-based systems. Users implement it for data warehousing, analytics, and integration across multiple environments. It facilitates the extraction, transformation, and loading of data for reporting and analytics. Organizations employ it for creating data pipelines, managing data movement, and converting on-premises data to cloud platforms, supporting various big data and machine learning projects.

Service and Support

Many find Azure Data Factory's support helpful, with responsive community and ample online resources. Some users note delays and wish for quicker issue resolution. Microsoft partners receive prioritized assistance. Ratings often sit between seven and nine out of ten, reflecting satisfaction but acknowledging areas for improvement. Documentation is generally praised, though some feel it lags. Paid support is seen as costly. Customer service's efficacy varies, with some dissatisfied due to extended response times. Lack of direct support is also mentioned.

Deployment

Azure Data Factory's initial setup is generally straightforward but varies in complexity. Many find it easy, taking around a few hours to a week, while others experience moderate to high complexity based on their environment. Deployment often involves multiple team members, especially when integrating with on-premise systems. Preparation and prior Azure knowledge help simplify the process. Companies appreciate Azure Data Factory's efficiency, though some technical expertise may be required for more complex configurations.

Scalability

Azure Data Factory offers impressive scalability, easily handling medium to large projects. Users appreciate its cloud-based nature, facilitating seamless scaling for growing demands and workloads. Many highlight its ability to manage substantial data volumes, though expansion may incur additional costs. Some users rate its scalability highly, noting minor improvements needed. Organizations across various sizes, from a few users to thousands, utilize its capabilities, with consistent reports of robust performance in different environments.

Stability

Users perceive Azure Data Factory as generally stable, offering consistent performance and reliability. Most individuals report minimal disruptions and high reliability, though some encounter occasional connection issues or downtime, often tied to infrastructure or specific configurations. There are mentions of rating stability between seven to nine out of ten, acknowledging minor areas for improvement. Though a few note that operation with large data sets could slow processes, most users are satisfied with its stability.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

Download our Azure Data Factory Buyer's Guide for additional reliable information.

Review data by company size

By reviewers

By visitors reading reviews

Top industries

By visitors reading reviews

Financial Services Firm

13%

Computer Software Company

12%

Manufacturing Company

Healthcare Company

Government

Energy/Utilities Company

Insurance Company

Retailer

Educational Organization

University

Comms Service Provider

Media Company

Construction Company

Real Estate/Law Firm

Non Profit

Legal Firm

Transportation Company

Wholesaler/Distributor

Hospitality Company

Pharma/Biotech Company

Logistics Company

Performing Arts

Consumer Goods Company

Outsourcing Company

Recreational Facilities/Services Company

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Azure Data Factory 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