We compared Snowflake and Azure Data Factory based on our user's reviews in several parameters.
Based on user reviews, Snowflake is praised for its high performance, scalability, and ease of use, while Azure Data Factory is appreciated for its seamless integration with data sources and robust monitoring capabilities. Snowflake's customer service and support received positive feedback, while Azure Data Factory is praised for its prompt assistance and responsiveness. Users find Snowflake's pricing and licensing terms flexible and reasonable compared to similar solutions, while Azure Data Factory is valued for its fair pricing and straightforward setup process. Both platforms have been reported to provide a positive ROI, with Snowflake benefiting from enhancements to improve user experience and functionality, and Azure Data Factory needing improvements in user interface, documentation, resource allocation, data integration capabilities, performance, stability, and debugging processes.
Features: Snowflake's valuable features include high performance, scalability, and ease of use. Users appreciate its efficient handling of large volumes of data and its user-friendly interface. On the other hand, Azure Data Factory is praised for its seamless integration with various data sources, ability to orchestrate complex data workflows, and robust monitoring capabilities.
Pricing and ROI: Snowflake and Azure Data Factory both receive positive feedback regarding their pricing, setup process, and licensing options. Users find Snowflake's setup process relatively uncomplicated, while Azure Data Factory's setup is described as seamless. Additionally, both products offer flexible and adaptable licensing options to meet various business needs., Snowflake: User reviews indicate positive ROI. Azure Data Factory: User feedback shows positive ROI with cost savings, improved productivity, streamlined data integration and migration, scalability, flexibility, and robust functionality.
Room for Improvement: Snowflake could benefit from enhancements to enhance user experience and functionality, while Azure Data Factory has areas for improvement in its user interface, documentation, resource allocation, data integration capabilities, performance, stability, and debugging process.
Deployment and customer support: Based on user feedback, Snowflake and Azure Data Factory have differences in the duration required for establishing a new tech solution. While Snowflake emphasizes the importance of considering separate deployment and setup phases, Azure Data Factory users reported varying timeframes, with some taking three months for deployment and others only a week for setup., Snowflake's customer service has been positively received by users, particularly for the expertise and effectiveness of their support team. On the other hand, Azure Data Factory's customer service has been consistently praised for their prompt assistance and knowledgeable staff.
The summary above is based on 84 interviews we conducted recently with Snowflake and Azure Data Factory users. To access the review's full transcripts, download our report.
"The most valuable features of the solution are its ease of use and the readily available adapters for connecting with various sources."
"It is very modular. It works well. We've used Data Factory and then made calls to libraries outside of Data Factory to do things that it wasn't optimized to do, and it worked really well. It is obviously proprietary in regards to Microsoft created it, but it is pretty easy and direct to bring in outside capabilities into Data Factory."
"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."
"The trigger scheduling options are decently robust."
"I like the basic features like the data-based pipelines."
"Data Factory itself is great. It's pretty straightforward. You can easily add sources, join and lookup information, etc. The ease of use is pretty good."
"The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."
"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."
"Everything is automatic, and I don't have to do any maintenance."
"Its speed and performance were the most valuable. Easy configuration of Snowflake in any cloud was also a benefit."
"Can be leveraged with respect to better performance, auto tuning and competition."
"The most efficient way for real-time dashboards or analytical business intelligence reports to be sent to the customer."
"Data Science capabilities are the most valuable feature."
"Its performance is a big advantage. When you run a query, its performance is very good. The inbound and outbound share features are also very useful for sharing a particular database. By using these features, you can allow others to access the Snowflake database and query it, which is another advantage of this solution. It has good security, and we can easily integrate it. We can connect it with multiple source systems."
"The technical support on offer is excellent."
"The most valuable features are the clustering, LS50, being able to change the size, the pay per use feature, the flexibility with many different sources and analytic applications."
"We have experienced some issues with the integration. This is an area that needs improvement."
"When working with AWS, we have noticed that the difference between ADF and AWS is that AWS is more customer-focused. They're more responsive compared to any other company. ADF is not as good as AWS, but it should be. If AWS is ten out of ten, ADF is around eight out of ten. I think AWS is easier to understand from the GUI perspective compared to ADF."
"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."
"There's space for improvement in the development process of the data pipelines."
"Data Factory could be improved by eliminating the need for a physical data area. We have to extract data using Data Factory, then create a staging database for it with Azure SQL, which is very, very expensive. Another improvement would be lowering the licensing cost."
"User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial."
"The user interface could use improvement. It's not a major issue but it's something that can be improved."
"Data Factory would be improved if it were a little more configuration-oriented and not so code-oriented and if it had more automated features."
"This solution could be improved by offering machine learning apps."
"It doesn't enforce typical relational database constraints. Quite expensive."
"I would like to see more transparency in data processing, ATLs, and compute areas - which should give more comfort to the end users."
"The user interface continues to be an issue, especially when we need to get data out of Snowflake. It's very easy to get data in, but it's not too easy to get it out or extract it."
"It's not that flexible when compared to Oracle."
"The solution could improve the user interface and add functionality to the system."
"I don't know about GCP, if they have connected for GCP. If they don't, they should allow for it."
"Some SQL language functions could be included."
Azure Data Factory is ranked 3rd in Cloud Data Warehouse with 81 reviews while Snowflake is ranked 1st in Cloud Data Warehouse with 94 reviews. Azure Data Factory is rated 8.0, while Snowflake is rated 8.4. The top reviewer of Azure Data Factory writes "The data factory agent is quite good but pricing needs to be more transparent". On the other hand, the top reviewer of Snowflake writes "Good usability, good data sharing and elastic compute features, and requires less DBA involvement". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, IBM InfoSphere DataStage and Palantir Foundry, whereas Snowflake is most compared with BigQuery, Teradata, Vertica, AWS Lake Formation and Oracle Autonomous Data Warehouse. See our Azure Data Factory vs. Snowflake report.
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