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.
"The scalability of the product is impressive."
"Azure Data Factory's most valuable features are the packages and the data transformation that it allows us to do, which is more drag and drop, or a visual interface. So, that eases the entire process."
"On the tool itself, we've never experienced any bugs or glitches. There haven't been crashes. Stability has been good."
"It is a complete ETL Solution."
"The most valuable aspect is the copy capability."
"The feature I found most helpful in Azure Data Factory is the pipeline feature, including being able to connect to different sources. Azure Data Factory also has built-in security, which is another valuable feature."
"For developers that are very accustomed to the Microsoft development studio, it's very easy for them to complete end-to-end data integration."
"We haven't had any issues connecting it to other products."
"The solution is very stable."
"The solution is very easy to use."
"It requires no maintenance on our part. They handle all that. The speed is phenomenal. The pricing isn't really anything more than what you would be paying for a SQL server license or another tool to execute the same thing. We have zero maintenance on our side to do anything and the speed at which it performs queries and loads the data is amazing. It handles unstructured data extremely well, too. So, if the data is in a JSON array or an XML, it handles that super well."
"It's ultra-fast at handling queries, which is what we find very convenient."
"The most valuable feature of Snowflake is its performance. We can access the data quickly. Additionally, it handles structured and non-structured data."
"The most valuable feature is the clone copy."
"The ability to share the data and the ability to scale up and down easily are the most valuable features. The concept of data sharing and data plumbing made it very easy to provide and share data. The ability to refresh your Dev or QA just by doing a clone is also valuable. It has the dynamic scale up and scale down feature. Development and deployment are much easier as compared to other platforms where you have to go through a lot of stuff. With a tool like DBT, you can do modeling and transformation within a single tool and deploy to Snowflake. It provides continuous deployment and continuous integration abilities. There is a separation of storage and compute, so you only get charged for your usage. You only pay for what you use. When we share the data downstream with business partners, we can specifically create compute for them, and we can charge back the business."
"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 initial setup is not very straightforward."
"Lacks in-built streaming data processing."
"Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations."
"We have experienced some issues with the integration. This is an area that needs improvement."
"Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue."
"For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better."
"The solution should offer better integration with Azure machine learning. We should be able to embed the cognitive services from Microsoft, for example as a web API. It should allow us to embed Azure machine learning in a more user-friendly way."
"We require Azure Data Factory to be able to connect to Google Analytics."
"An additional feature I'd like to see is called materialized views, which can speed up some run times. I'd like it to be able to be used where you can have multiple tables inside them; materialized view. That would be nice. As well as being able to run cursors, to be able to do some bulk updates and some more advanced querying, table building on the fly."
"The design of the product is easily misunderstood."
"There are some challenges with loading unstructured data and integrating some message queues or brokers. In one project, we had a problem connecting to one of the message queues and we had to take a different route altogether on Microsoft Azure."
"Its stability could be better."
"Snowflake has support for stored procedures, but it is not that powerful."
"The user interface continues to be an issue, especially when we need to get data out of Snowflake. It's very easy to get data in, but it's not too easy to get it out or extract it."
"The scheduling system can definitely be better because we had to use external airflow for that. There should be orchestration for the scheduling system. Snowflake currently does not support machine learning, so it is just storage. They also need some alternatives for SQL Query. There should also be support for Spark in different languages such as Python."
"There are always a few operation updates here and there that can be made."
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 Microsoft Azure Synapse Analytics, whereas Snowflake is most compared with BigQuery, Teradata, Vertica, AWS Lake Formation and Amazon EMR. See our Azure Data Factory vs. Snowflake report.
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