Microsoft Parallel Data Warehouse and Snowflake compete in the data warehousing category. Snowflake appears to have the upper hand due to its scalability, distributed architecture, and flexibility, which are highly regarded by users.
Features: Microsoft Parallel Data Warehouse offers exceptional performance with a 10-100x speed advantage for data loading. It integrates well with Microsoft products, supporting OLAP queries and ETL processes while ensuring data integrity. Snowflake's scalability and distributed architecture allow efficient handling of structured and semi-structured data, with features like time travel and zero-copy clone that facilitate version control and backup management.
Room for Improvement: Microsoft Parallel Data Warehouse could enhance BI tool compatibility and create more user-friendly interfaces, addressing error messaging issues and scalability challenges with large datasets. Snowflake needs to improve cost transparency and usability for non-coders, enhance third-party integrations, and offer on-premises options alongside improving intuitive analytics tools.
Ease of Deployment and Customer Service: Microsoft Parallel Data Warehouse suits on-premises implementations better for organizations needing infrastructure control but can be complex to deploy. Snowflake, primarily cloud-based, provides easy scalability. Microsoft receives mixed customer service feedback but benefits from a supportive ecosystem, whereas Snowflake's support and ease of use are viewed as transparent and advantageous.
Pricing and ROI: Microsoft Parallel Data Warehouse is seen as a worthwhile investment despite high costs, providing a significant ROI for handling large data volumes. Snowflake uses a pay-as-you-go model, potentially reducing overall costs. Users value its pricing flexibility, transparency, and strong returns from efficient data management.
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.
I give the scalability an eight out of ten, indicating it scales well for our needs.
As a consultant, we hire additional programmers when we need to scale up certain major projects.
Snowflake is very scalable and has a dedicated team constantly improving the product.
The billing doubles with size increase, but processing does not necessarily speed up accordingly.
Microsoft Parallel Data Warehouse is stable for us because it is built on SQL Server.
Snowflake is very stable, especially when used with AWS.
Snowflake as a SaaS offering means that maintenance isn't an issue for me.
It would be better to release patches less frequently, maybe once a month or once every two months.
When there are many users or many expensive queries, it can be very slow.
The ETL designing process could be optimized for better efficiency.
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.
Microsoft Parallel Data Warehouse is very expensive.
Snowflake's pricing is on the higher side.
Snowflake lacks transparency in estimating resource usage.
The columnstore index enhances data query performance by using less space and achieving faster performance than general indexing.
There's a feature that allows users to set alerts on triggers within reports, enabling timely actions on pending applications and effectively reducing waiting time.
Microsoft Parallel Data Warehouse is used in the logistics area for optimizing SQL queries related to the loading and unloading of trucks.
Snowflake is a data lake on the cloud where all processing happens in memory, resulting in very fast query responses.
Being able to perform AI and Machine Learning in the same location as the data is quite advantageous.
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 provides a modern data warehousing solution with features designed for seamless integration, scalability, and consumption-based pricing. It handles large datasets efficiently, making it a market leader for businesses migrating to the cloud.
Snowflake offers a flexible architecture that separates storage and compute resources, supporting efficient ETL jobs. Known for scalability and ease of use, it features built-in time zone conversion and robust data sharing capabilities. Its enhanced security, performance, and ability to handle semi-structured data are notable. Users suggest improvements in UI, pricing, on-premises integration, and data science functions, while calling for better transaction performance and machine learning capabilities. Users benefit from effective SQL querying, real-time analytics, and sharing options, supporting comprehensive data analysis with tools like Tableau and Power BI.
What are Snowflake's Key Features?In industries like finance, healthcare, and retail, Snowflake's flexible data warehousing and analytics capabilities facilitate cloud migration, streamline data storage, and allow organizations to consolidate data from multiple sources for advanced insights and AI-driven strategies. Its integration with analytics tools supports comprehensive data analysis and reporting tasks.
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