Snowflake and Azure Data Factory are key players in the data management solutions category. While both platforms offer strong capabilities, Snowflake holds an advantage in scalability and flexibility, making it particularly effective for handling large datasets.
Features: Snowflake offers multi-clustering, zero copy cloning, and broad support for file formats, providing high adaptability for diverse data needs. Azure Data Factory excels in integration, with over 100 built-in connectors enabling seamless data movement and transformation within the Azure ecosystem.
Room for Improvement: Snowflake could enhance its spatial components, improve integration with external tools, and offer more pricing transparency. Azure Data Factory could benefit from enhancements in its monitoring capabilities and simplification of integration with non-Microsoft environments, along with more transparent pricing structures.
Ease of Deployment and Customer Service: Both Snowflake and Azure Data Factory leverage cloud-based architectures for easy deployment. Snowflake provides strong technical support, but lacks SLAs, while Azure Data Factory benefits from Azure platform integration but faces slower support response times compared to competitors.
Pricing and ROI: Snowflake uses a pay-as-you-go model noted for its flexibility, though some customers find its pricing structure complex. Azure Data Factory also offers a flexible pay-as-you-go model, with similar complexities in pricing. Both deliver strong ROI, with Snowflake praised for its operational cost efficiency.
The technical support is responsive and helpful
The technical support from Microsoft is rated an eight out of ten.
The technical support for Azure Data Factory is generally acceptable.
I received great support in migrating data to Snowflake, with quick responses and innovative solutions.
The technical support from Snowflake is very good, nice, and efficient.
Azure Data Factory is highly scalable.
The billing doubles with size increase, but processing does not necessarily speed up accordingly.
Snowflake is very scalable and has a dedicated team constantly improving the product.
The solution has a high level of stability, roughly a nine out of ten.
Snowflake as a SaaS offering means that maintenance isn't an issue for me.
Snowflake is very stable, especially when used with AWS.
Incorporating more dedicated API sources to specific services like HubSpot CRM or Salesforce would be beneficial.
Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.
There is a problem with the integration with third-party solutions, particularly with SAP.
Enhancements in user experience for data observability and quality checks would be beneficial, as these tasks currently require SQL coding, which might be challenging for some users.
Cost reduction is one area I would like Snowflake to improve.
The pricing is cost-effective.
It is considered cost-effective.
Snowflake's pricing is on the higher side.
Snowflake lacks transparency in estimating resource usage.
It connects to different sources out-of-the-box, making integration much easier.
I find the most valuable feature in Azure Data Factory to be its ability to handle large datasets.
The interface of Azure Data Factory is very usable with a more interactive visual experience, making it easier for people who are not as experienced in coding to work with.
One key feature is the separation of compute and storage, which eliminates storage limitations.
The scalability options it provides, addressing issues without tying workloads into one virtual machine, enhance functionality.
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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