

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.
I would rate the technical support from Amazon as ten out of ten.
We get all call support, screen sharing support, and immediate support, so there are no problems.
They help with billing, cost determination, IAM properties, security compliance, and deployment and migration activities.
I would rate my experience with technical support around six on a scale of 1 to 10 because I have not had a particular experience with technical support.
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.
The cost factor differs significantly. When you run Spark application on EKS, you run at the pod level, so you can control the compute cost. But in Amazon EMR, when you have to run one application, you have to launch the entire EC2.
I have thoughts on what would be great to see in the product, such as AI/ML features or additional options.
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.
Addressing the cost would be the number one area for improvement.
It would be better to release patches less frequently, maybe once a month or once every two months.
The ETL designing process could be optimized for better efficiency.
Cost optimization can be achieved through instance usage, cluster sharing, and auto-scaling.
I would rate the price for Amazon EMR, where one is high and ten is low, as a good one.
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 features at Amazon EMR that I have found most valuable are fully customizable functions.
Amazon EMR provides out-of-the-box functionality because we can deploy and get Spark functionality over Hadoop.
The columnstore index enhances data query performance by using less space and achieving faster performance than general indexing.
The biggest advantage of Microsoft Parallel Data Warehouse is the possibility to stop or pause the service because it can be very expensive.
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.

| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 5 |
| Large Enterprise | 12 |
| Company Size | Count |
|---|---|
| Small Business | 16 |
| Midsize Enterprise | 6 |
| Large Enterprise | 21 |
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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