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.
"Data Factory's best features are simplicity and flexibility."
"The most important feature is that it can help you do the multi-threading concepts."
"Data Factory's most valuable feature is Copy Activity."
"One advantage of Azure Data Factory is that it's fast, unlike SSIS and other on-premise tools. It's also very convenient because it has multiple connectors. The availability of native connectors allows you to connect to several resources to analyze data streams."
"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."
"The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."
"Its integrability with the rest of the activities on Azure is most valuable."
"The solution auto-scales and it provides concurrency."
"It helps with business intelligence by providing analytics that can be reported."
"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."
"Scalability-wise, I rate the solution a ten out of ten."
"Considering everything I have accessed, the product's dashboard is good since it provides multiple good options, including customization options."
"The platform not only provides ease of use but also stands out for its speedy execution, conveying a sense of robustness and reliability that I find appealing."
"The performance has been good."
"One of the key advancements in Snowflake Analytics is data sharing."
"Azure Data Factory uses many resources and has issues with parallel workflows."
"Currently, our company requires a monitoring tool, and that isn't available in Azure Data Factory."
"Some of the optimization techniques are not scalable."
"Lacks a decent UI that would give us a view of the kinds of requests that come in."
"There is room for improvement primarily in its streaming capabilities. For structured streaming and machine learning model implementation within an ETL process, it lags behind tools like Informatica."
"There is always room to improve. There should be good examples of use that, of course, customers aren't always willing to share. It is Catch-22. It would help the user base if everybody had really good examples of deployments that worked, but when you ask people to put out their good deployments, which also includes me, you usually got, "No, I'm not going to do that." They don't have enough good examples. Microsoft probably just needs to pay one of their partners to build 20 or 30 examples of functional Data Factories and then share them as a user base."
"There is no built-in pipeline exit activity when encountering an error."
"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."
"Machine learning in Snowflake isn't as advanced as in other products. I haven't heard of any successful industry-wide use cases of machine learning implemented in Snowflake. It might take a couple of years to reach the same level as Databricks."
"The scheduling of jobs requires improvement, particularly in terms of the user interface which currently lacks certain features found in comparable platforms."
"If you have a lot of computations, it becomes very costly."
"I cannot comment on the product's stability because we are still struggling with its performance."
"The platform could work easier for AI implementation compared to one of its competitors."
"Machine learning should be improved."
"The solution's high price can be an area of concern that needs improvement."
"The solution needs to consider including some updates in the future."
Azure Data Factory is ranked 3rd in Cloud Data Warehouse with 81 reviews while Snowflake Analytics is ranked 6th in Cloud Data Warehouse with 31 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, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and IBM InfoSphere DataStage, 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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