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."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."
"I like its integration with SQL pools, its ability to work with Databricks, its pipelines, and the serverless architecture are the most effective features."
"The flexibility that Azure Data Factory offers is great."
"The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted."
"The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components."
"We have been using drivers to connect to various data sets and consume data."
"From my experience so far, the best feature is the ability to copy data to any environment. We have 100 connects and we can connect them to the system and copy the data from its respective system to any environment. That is the best feature."
"The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain."
"The most valuable feature of Snowflake Analytics is its performance."
"It ensures the optimization of the application development while maintaining the user-friendly nature of its UI."
"Its performance speed is very good."
"It is an all-in-one platform that provides the capabilities needed for various analytics tasks, including data warehousing for machine learning."
"It's not necessary to use a physical server."
"One of the valuable features is the solution’s time travel capability. The solution is highly stable. The solution is highly scalable. The initial setup is straightforward, and the deployment process is quick and efficient. I recommend the solution. Overall, I rate it a perfect ten."
"Time Travel and Snowpipe are good features."
"One of the key advancements in Snowflake Analytics is data sharing."
"We have experienced some issues with the integration. This is an area that needs improvement."
"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."
"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."
"We require Azure Data Factory to be able to connect to Google Analytics."
"You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats."
"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."
"On the UI side, they could make it a little more intuitive in terms of how to add the radius components. Somebody who has been working with tools like Informatica or DataStage gets very used to how the UI looks and feels."
"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."
"Moving data from legacy systems to Snowflake is not that easy. There are some cases where processors are not actually compatible with Snowflake."
"There are issues while loading data from Snowflake Analytics to the Power BI reporting."
"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."
"Integration into different Python and Jupyter notebooks needs to be improved."
"I cannot comment on the product's stability because we are still struggling with its performance."
"The product's cost is an area of concern where improvements are required."
"The platform could work easier for AI implementation compared to one of its competitors."
"The technical support is not very good."
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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