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 workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem."
"The overall performance is quite good."
"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."
"We haven't had any issues connecting it to other products."
"On the tool itself, we've never experienced any bugs or glitches. There haven't been crashes. Stability has been good."
"The solution includes a feature that increases the number of processors used which makes it very powerful and adds to the scalability."
"It is a complete ETL Solution."
"It makes it easy to collect data from different sources."
"I am impressed with the product's data-sharing feature."
"The computational power of Snowflake is very good."
"The most valuable feature of Snowflake Analytics is its performance."
"It can run complex workloads with varied compute."
"It helps with business intelligence by providing analytics that can be reported."
"Snowflake Analytics is pretty easy to use with the connectors for integration with the tools and systems in my company."
"Snowflake Analytics' most valuable feature is its inbuilt infrastructure for executing queries, which I don't have to manage based on my data volume as it's taken care of by Snowflake."
"The most valuable feature of Snowflake Analytics is the ability to control and manage the cost."
"There's no Oracle connector if you want to do transformation using data flow activity, so Azure Data Factory needs more connectors for data flow transformation."
"Lacks in-built streaming data processing."
"We require Azure Data Factory to be able to connect to Google Analytics."
"The product's technical support has certain shortcomings, making it an area where improvements are required."
"The deployment should be easier."
"The user interface could use improvement. It's not a major issue but it's something that can be improved."
"If the user interface was more user friendly and there was better error feedback, it would be helpful."
"Areas for improvement in Azure Data Factory include connectivity and integration. When you use integration runtime, whenever there's a failure, the backup process in Azure Data Factory takes time, so this is another area for improvement."
"The scheduling of jobs requires improvement, particularly in terms of the user interface which currently lacks certain features found in comparable platforms."
"The technical support is not very good."
"The solution’s interface is good but it could be improved."
"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."
"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."
"Snowflake should include a WHERE clause for building data pipelines."
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.
See our list of best Cloud Data Warehouse vendors.
We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
