Snowflake and Azure Data Factory compete in the field of data processing and management. Snowflake holds an edge due to its scalability, high-performance processing, and support for multiple file formats, whereas Azure Data Factory stands out with its ETL capabilities and integration with Azure services.
Features: Snowflake offers features like scalability, high-performance processing of large datasets, and flexible node configurations. It supports multiple file formats and employs robust ELT techniques. Notable functionalities include multi-clustering, zero copy cloning, and time travel. Azure Data Factory is recognized for its ETL capabilities, extensive connectivity, and seamless integration with Azure services.
Room for Improvement: Snowflake needs to enhance its spatial query components and user-side implementation. Pricing transparency and support for stored procedures require attention, and users want more built-in analytics and better connectors. Azure Data Factory could improve its batch processing performance and simplify its pricing model, with users noting a need for better integration with Azure services and more detailed documentation.
Ease of Deployment and Customer Service: Snowflake is compatible with both public and private cloud environments and offers strong customer support with responsive service. It is easy to deploy across various cloud infrastructures. Azure Data Factory provides good customer support, particularly in public and hybrid clouds, but some users report slower response times compared to Snowflake.
Pricing and ROI: Snowflake has a consumption-based pricing model seen as expensive but cost-effective in the long run due to its transparent pay-as-you-go approach. Users report a positive ROI leveraging Snowflake's data management capabilities. Azure Data Factory also uses a pay-as-you-go model with multiple tiers, though its complex pricing structure draws criticism. Both systems are generally viewed as offering a good return on investment.
The technical support is responsive and helpful
The technical support from Microsoft is rated an eight out of ten.
I received great support in migrating data to Snowflake, with quick responses and innovative solutions.
Azure Data Factory is highly scalable.
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 is very stable, especially when used with AWS.
Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.
Incorporating more dedicated API sources to specific services like HubSpot CRM or Salesforce would be beneficial.
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.
It connects to different sources out-of-the-box, making integration much easier.
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.
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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