We performed a comparison between Azure Data Factory and Snowflake Analytics based on real PeerSpot user reviews.
Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The overall performance is quite good."
"UI is easy to navigate and I can retrieve VTL code without knowing in-depth coding languages."
"I like how you can create your own pipeline in your space and reuse those creations. You can collaborate with other people who want to use your code."
"It's cloud-based, allowing multiple users to easily access the solution from the office or remote locations. I like that we can set up the security protocols for IP addresses, like allow lists. It's a pretty user-friendly product as well. The interface and build environment where you create pipelines are easy to use. It's straightforward to manage the digital transformation pipelines we build."
"It has built-in connectors for more than 100 sources and onboarding data from many different sources to the cloud environment."
"It is beneficial that the solution is written with Spark as the back end."
"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."
"I like that it's a monolithic data platform. This is why we propose these solutions."
"It is quite a convenient tool."
"It ensures the optimization of the application development while maintaining the user-friendly nature of its UI."
"The most valuable features of Snowflake for our data analytics are its time travel capability, allowing easy data recovery, and its automatic optimization of partitioning and clustering."
"It can run complex workloads with varied compute."
"It is an all-in-one platform that provides the capabilities needed for various analytics tasks, including data warehousing for machine learning."
"The advanced features like time travel, zero copy cloning and scalability have been most useful. Snowflake requires zero maintenance for Data Warehousing on the cloud system."
"The Snowflake features I find most beneficial for data analysis are primarily related to analytics, particularly their features like materialized views and queues, which are especially useful for dashboarding purposes."
"Snowflake Analytics is pretty easy to use with the connectors for integration with the tools and systems in my company."
"We require Azure Data Factory to be able to connect to Google Analytics."
"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."
"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog."
"They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas."
"The solution needs to integrate more with other providers and should have a closer integration with Oracle BI."
"There are limitations when processing more than one GD file."
"Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there."
"When the record fails, it's tough to identify and log."
"Snowflake Analytics can improve the integration with machine learning tools and AI and it will make the solution more usable."
"The solution’s interface is good but it could be improved."
"The solution’s scalability could be improved."
"One notable absence in Snowflake's offerings is an on-premises solution."
"Snowflake could improve in the areas of advanced machine learning AML and generative AI."
"A room for improvement in Snowflake Analytics is Spark, particularly its connector for Spark. An additional feature I'd like to see in the next release of the solution is built-in analytics."
"The scheduling of jobs requires improvement, particularly in terms of the user interface which currently lacks certain features found in comparable platforms."
"Machine learning should be improved."
Azure Data Factory is ranked 3rd in Cloud Data Warehouse with 79 reviews while Snowflake Analytics is ranked 7th in Cloud Data Warehouse with 27 reviews. Azure Data Factory is rated 8.0, while Snowflake Analytics 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 Analytics writes "A scalable tool useful for data lake and data mining processes". Azure Data Factory is most compared with Informatica PowerCenter, Alteryx Designer, Informatica Cloud Data Integration, Snowflake and Microsoft Azure Synapse Analytics, whereas Snowflake Analytics is most compared with Adobe Analytics, Mixpanel, Amplitude, Glassbox and Yellowbrick Cloud Data Warehouse. See our Azure Data Factory vs. Snowflake Analytics report.
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