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.
Azure Data Factory efficiently manages and integrates data from various sources, enabling seamless movement and transformation across platforms. Its valuable features include seamless integration with Azure services, handling large data volumes, flexible transformation, user-friendly interface, extensive connectors, and scalability. Users have experienced improved team performance, workflow simplification, enhanced collaboration, streamlined processes, and boosted productivity.
Snowflake is a cloud-based data warehousing solution for storing and processing data, generating reports and dashboards, and as a BI reporting source. It is used for optimizing costs and using financial data, as well as for migrating data from on-premises to the cloud. The solution is often used as a centralized data warehouse, combining data from multiple sources.
Snowflake has helped organizations improve query performance, store and process JSON and XML, consolidate multiple databases into one unified table, power company-wide dashboards, increase productivity, reduce processing time, and have easy maintenance with good technical support.
Its platform is made up of three components:
Snowflake has many valuable vital features. Some of the most useful ones include:
There are many benefits to implementing Snowflake. It helps optimize costs, reduce downtime, improve operational efficiency, and automate data replication for fast recovery, and it is built for high reliability and availability.
Below are quotes from interviews we conducted with users currently using the Snowflake solution:
Sreenivasan R., Director of Data Architecture and Engineering at Decision Minds, says, "Data sharing is a good feature. It is a majorly used feature. The elastic computing is another big feature. Separating computing and storage gives you flexibility. It doesn't require much DBA involvement because it doesn't need any performance tuning. We are not doing any performance tuning, and the entire burden of performance and SQL tuning is on Snowflake. Its usability is very good. I don't need to ramp up any user, and its onboarding is easier. You just onboard the user, and you are done with it. There are simple SQL and UI, and people are able to use this solution easily. Ease of use is a big thing in Snowflake."
A director of business operations at a logistics company mentions, "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."
A Solution Architect at a wholesaler/distributor comments, "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."
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