We compared Snowflake and Azure Data Factory based on our user's reviews in several parameters.
Based on user reviews, Snowflake is praised for its high performance, scalability, and ease of use, while Azure Data Factory is appreciated for its seamless integration with data sources and robust monitoring capabilities. Snowflake's customer service and support received positive feedback, while Azure Data Factory is praised for its prompt assistance and responsiveness. Users find Snowflake's pricing and licensing terms flexible and reasonable compared to similar solutions, while Azure Data Factory is valued for its fair pricing and straightforward setup process. Both platforms have been reported to provide a positive ROI, with Snowflake benefiting from enhancements to improve user experience and functionality, and Azure Data Factory needing improvements in user interface, documentation, resource allocation, data integration capabilities, performance, stability, and debugging processes.
Features: Snowflake's valuable features include high performance, scalability, and ease of use. Users appreciate its efficient handling of large volumes of data and its user-friendly interface. On the other hand, Azure Data Factory is praised for its seamless integration with various data sources, ability to orchestrate complex data workflows, and robust monitoring capabilities.
Pricing and ROI: Snowflake and Azure Data Factory both receive positive feedback regarding their pricing, setup process, and licensing options. Users find Snowflake's setup process relatively uncomplicated, while Azure Data Factory's setup is described as seamless. Additionally, both products offer flexible and adaptable licensing options to meet various business needs., Snowflake: User reviews indicate positive ROI. Azure Data Factory: User feedback shows positive ROI with cost savings, improved productivity, streamlined data integration and migration, scalability, flexibility, and robust functionality.
Room for Improvement: Snowflake could benefit from enhancements to enhance user experience and functionality, while Azure Data Factory has areas for improvement in its user interface, documentation, resource allocation, data integration capabilities, performance, stability, and debugging process.
Deployment and customer support: Based on user feedback, Snowflake and Azure Data Factory have differences in the duration required for establishing a new tech solution. While Snowflake emphasizes the importance of considering separate deployment and setup phases, Azure Data Factory users reported varying timeframes, with some taking three months for deployment and others only a week for setup., Snowflake's customer service has been positively received by users, particularly for the expertise and effectiveness of their support team. On the other hand, Azure Data Factory's customer service has been consistently praised for their prompt assistance and knowledgeable staff.
The summary above is based on 84 interviews we conducted recently with Snowflake and Azure Data Factory users. To access the review's full transcripts, download our report.
"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."
"Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data."
"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."
"I can do everything I want with SSIS and Azure Data Factory."
"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 security of the agent that is installed on-premises is very good."
"The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."
"It is beneficial that the solution is written with Spark as the back end."
"The syntax is advanced which reduces the time to write code."
"The technical support is pretty good, particularly if you are a more technical user."
"The features that I have found most valuable are the ease of use, the rapidness, how quickly the solution can be implemented, and of course that it's been very easy to move from the on-premise world to the Cloud world because Snowflake is based on SQL also."
"My company wanted to have all our data in one single place and this what we use Snowflake for. Snowflake also allows us to build connectors to different data sources."
"The pricing is reasonable and matches the rest of the market."
"A user-friendly and reliable solution."
"The most efficient way for real-time dashboards or analytical business intelligence reports to be sent to the customer."
"It is quite easy to manage."
"The solution can be improved by decreasing the warmup time which currently can take up to five minutes."
"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."
"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."
"It would be better if it had machine learning capabilities."
"The thing we missed most was data update, but this is now available as of two weeks ago."
"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."
"In the next release, it's important that some sort of scheduler for running tasks is added."
"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."
"Every product has room for improvement, although in this case, it needs some broadening of the functionality."
"Some SQL language functions could be included."
"We would like to be able to do modeling with Snowflake. It should support statistical modeling."
"The solution should offer an on-premises version also. We have some requirements where we would prefer to use it as a template."
"We are yet to figure out how to integrate tools, such as Liquibase, to release changes to our data warehouse model."
"The aspect of it that was more complicated was stored procedures. It does not support SQL language-based stored procedures. You have to write in JavaScript. If they supported SQL language and stored procedures, it would make migration from on-prem much simpler. In most cases, if an on-prem solution has stored procedures, they're usually written in SQL. They're not written as what most on-prem DBMS would refer to as an external stored procedure, which is what these feel like to most people because they're written in a language outside of SQL."
"Its stability could be better."
"They need to improve its ETL functionality so that Snowflake becomes an ETL product. Snowpipe can do some pipelines and data ingestion, but as compare to Talend, these functionalities are limited. The ETL feature is not good enough. Therefore, Snowflake can only be used as a database. You can't use it as an ETL tool, which is a limitation. We have spoken to the vendor, and they said they are working on it, but I'm not sure when they will bring it to production."
Azure Data Factory is ranked 3rd in Cloud Data Warehouse with 81 reviews while Snowflake is ranked 1st in Cloud Data Warehouse with 94 reviews. Azure Data Factory is rated 8.0, while Snowflake 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 writes "Good usability, good data sharing and elastic compute features, and requires less DBA involvement". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, IBM InfoSphere DataStage and Palantir Foundry, whereas Snowflake is most compared with BigQuery, Teradata, Vertica, AWS Lake Formation and Oracle Autonomous Data Warehouse. See our Azure Data Factory vs. Snowflake report.
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