Microsoft Parallel Data Warehouse and Snowflake both compete in the data warehousing space. Based on the features, Snowflake shows an advantage with its modern cloud-native architecture, while Microsoft shines through integration within its ecosystem.
Features: Microsoft Parallel Data Warehouse improves performance with large data volumes and supports multiple database systems. Key features include its integration with Microsoft's products, bulk data-load capabilities, and MPP processing. Snowflake offers high flexibility and performance, efficiently managing structured and semi-structured data. Its features include real-time scalability, support for various data formats, and cloud-native processing with options like time travel and data sharing.
Room for Improvement: Microsoft Parallel Data Warehouse demands significant infrastructure and SQL expertise, posing challenges for new users. Enhancing compatibility with BI tools and reducing hardware requirements are suggested. Snowflake could improve unstructured data support, enhance the client UI, and provide clearer pricing, alongside better integration with third-party tools and governance enhancements.
Ease of Deployment and Customer Service: Microsoft Parallel Data Warehouse is generally deployed on-premises, suitable for users seeking data control and privacy, though customer service receives mixed reviews. Snowflake, predominantly cloud-based, offers ease and scalability for modern needs. Technical support is satisfactory, but some concerns over direct communication exist, with its deployment ease aided by its cloud-native nature.
Pricing and ROI: Microsoft Parallel Data Warehouse is cost-effective for large data requirements but incurs high overall expenses due to infrastructure. Its ROI is achieved through integration with existing Microsoft tools. Snowflake uses a pay-as-you-go model, offering cost efficiency for diverse workloads. Although perceived as costly due to usage-related pricing, it delivers significant ROI by enabling efficient data management.
I received great support in migrating data to Snowflake, with quick responses and innovative solutions.
I give the scalability an eight out of ten, indicating it scales well for our needs.
Snowflake is very scalable and has a dedicated team constantly improving the product.
Microsoft Parallel Data Warehouse is stable for us because it is built on SQL Server.
Snowflake is very stable, especially when used with AWS.
When there are many users or many expensive queries, it can be very slow.
Microsoft Parallel Data Warehouse is excellent but very expensive.
The ETL designing process could be optimized for better efficiency.
Cost reduction is one area I would like Snowflake to improve.
Microsoft Parallel Data Warehouse is very expensive.
Snowflake's pricing is on the higher side.
The columnstore index enhances data query performance by using less space and achieving faster performance than general indexing.
Microsoft Parallel Data Warehouse is used in the logistics area for optimizing SQL queries related to the loading and unloading of trucks.
The interface is very user-friendly.
One key feature is the separation of compute and storage, which eliminates storage limitations.
The traditional structured relational data warehouse was never designed to handle the volume of exponential data growth, the variety of semi-structured and unstructured data types, or the velocity of real time data processing. Microsoft's SQL Server data warehouse solution integrates your traditional data warehouse with non-relational data and it can handle data of all sizes and types, with real-time performance.
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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