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.
"From what we have seen so far, the solution seems very stable."
"The flexibility that Azure Data Factory offers is great."
"ADF is another ETL tool similar to Informatica that can transform data or copy it from on-prem to the cloud or vice versa. Once we have the data, we can apply various transformations to it and schedule our pipeline according to our business needs. ADF integrates with Databricks. We can call our Databricks notebooks and schedule them via ADF."
"An excellent tool for pipeline orchestration."
"Azure Data Factory became more user-friendly when data-flows were introduced."
"The most valuable features are data transformations."
"We have found the bulk load feature very valuable."
"The solution has a good interface and the integration with GitHub is very useful."
"All the people who are working with Snowflake are extremely happy with it because it is designed from a data-warehousing point of view, not the other way around. You have a database and then you tweak it and then it becomes a data warehouse."
"It is a highly scalable solution. There is no limit on storage or computing."
"The solution is very easy to use."
"For us, the virtual warehousing is likely the most valuable aspect."
"Once you have finished your designs they can be easily imported to Snowflake and the information can be readily accessed without an IT expert."
"The feature that is really striking is the ability to translate the SQL workloads into the NoSQL version that can be used by Snowflake."
"I have found the solution's most valuable features to be storage, flexibility, ease of use, and security."
"My company wanted to have all our data in one single place and this what we use Snowflake for. Snowflake also allows us to build connectors to different data sources."
"The Microsoft documentation is too complicated."
"The solution can be improved by decreasing the warmup time which currently can take up to five minutes."
"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."
"There aren't many third-party extensions or plugins available in the solution."
"The speed and performance need to be improved."
"I would like to see this time travel feature in Snowflake added to Azure Data Factory."
"One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases."
"My only problem is the seamless connectivity with various other databases, for example, SAP."
"The UI could improve because sometimes in the security query the UI freezes. We then have to close the window and restart."
"There is a need for improvements in the documentation, this would allow more people to switch over to this solution."
"I see room for improvement when it comes to credit performance. The other thing I'd like to be improved is the warehouse facility."
"I have heard people having difficulty with the machine learning model, so there may be room for improvement."
"The solution needs more connectors."
"If they could bring in some tools for data integration, it would be really great."
"This solution could be improved by offering machine learning apps."
"The data science functionality could be improved in terms of the machine learning process."
Azure Data Factory is ranked 3rd in Cloud Data Warehouse with 81 reviews while Snowflake is ranked 1st in Cloud Data Warehouse with 92 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, Microsoft Azure Synapse Analytics and IBM InfoSphere DataStage, whereas Snowflake is most compared with BigQuery, Teradata, Vertica, AWS Lake Formation and Amazon EMR. See our Azure Data Factory vs. Snowflake report.
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