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."It is beneficial that the solution is written with Spark as the back end."
"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."
"For me, it was that there are dedicated connectors for different targets or sources, different data sources. For example, there is direct connector to Salesforce, Oracle Service Cloud, etcetera, and that was really helpful."
"The scalability of the product is impressive."
"We have found the bulk load feature very valuable."
"The most valuable features of the solution are its ease of use and the readily available adapters for connecting with various sources."
"What I like best about Azure Data Factory is that it allows you to create pipelines, specifically ETL pipelines. I also like that Azure Data Factory has connectors and solves most of my company's problems."
"It is a complete ETL Solution."
"Scalability-wise, I rate the solution a ten out of ten."
"The computational power of Snowflake is very good."
"It's a scalable solution because you can analyze a huge amount of data in the solution."
"The most valuable feature of Snowflake Analytics is the ability to control and manage the cost."
"Considering everything I have accessed, the product's dashboard is good since it provides multiple good options, including customization options."
"Scaling is very high – there's no problem for scaling purposes. The learning curve is very small. And there are a lot of advanced features like handling duplicates, security, data governance, data sharing, and data cloning."
"It is an all-in-one platform that provides the capabilities needed for various analytics tasks, including data warehousing for machine learning."
"Its performance speed is very good."
"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."
"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."
"The deployment should be easier."
"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."
"Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations."
"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."
"Data Factory's cost is too high."
"The setup and configuration process could be simplified."
"The tool should support EIM use cases. I guess the product is already working on it. I look forward to seeing inbuilt AI generative tools in the solution's future releases. The tool's price can be a little lower. The solution's on-premises support is also very limited. We have to rely on other support services to deploy it on-premises."
"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."
"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 UI must be improved."
"The distribution methodology isn't as strong as Bethesda or SAP HANA. It's not as strong as other competitors."
"One area that could benefit from enhancement is the user interface for more visual ESM features."
"There are issues while loading data from Snowflake Analytics to the Power BI reporting."
"Moving data from legacy systems to Snowflake is not that easy. There are some cases where processors are not actually compatible with Snowflake."
Azure Data Factory is ranked 3rd in Cloud Data Warehouse with 81 reviews while Snowflake Analytics is ranked 7th in Cloud Data Warehouse with 30 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 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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