Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
They help with billing, cost determination, IAM properties, security compliance, and deployment and migration activities.
Scalability can be provisioned using the auto-scaling feature, EC2 instances, on-demand instances, and storage locations like block storage, S3, or file storage.
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.
Regular updates, patch installations, monitoring, logging, alerting, and disaster recovery activities are crucial for maintaining stability.
Microsoft Parallel Data Warehouse is stable for us because it is built on SQL Server.
There is room for improvement with respect to retries, handling the volume of data on S3 buckets, cluster provisioning, scaling, termination, security, and integration between services like S3, Glue, Lake Formation, and DynamoDB.
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.
Costs are involved based on cluster resources, data volumes, EC2 instances, instance sizes, Kubernetes, Docker services, storage, and data transfers.
Microsoft Parallel Data Warehouse is very expensive.
Amazon EMR helps in scalability, real-time and batch processing of data, handling efficient data sources, and managing data lakes, data stores, and data marts on file systems and in S3 buckets.
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.
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.
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