Find out in this report how the two Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
Whenever we need support, if there is an issue accessing stored data due to regional data center problems, the Amazon team is very helpful and provides optimal solutions quickly.
It's costly when you enable support.
The scalability part needs improvement as the sizing requires trial and error.
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.
Amazon Redshift is a stable product, and I would rate it nine or ten out of ten for stability.
Microsoft Parallel Data Warehouse is stable for us because it is built on SQL Server.
Integration with AI could be a good improvement.
They should bring the entire ETL data management process into Amazon Redshift.
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.
The cost of technical support is high.
It's a pretty good price and reasonable for the product quality.
The pricing of Amazon Redshift is expensive.
Microsoft Parallel Data Warehouse is very expensive.
Scalability is the best feature of Amazon Redshift. Amazon Redshift handles scalability automatically, so we do not need to scale up or down; it is all managed by Redshift.
Scalability is also a strong point; I can scale it however I want without any limitations.
Amazon Redshift's performance optimization and scalability are quite helpful, providing functionalities such as scaling up and down.
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.
Amazon Redshift is a fully administered, petabyte-scale cloud-based data warehouse service. Users are able to begin with a minimal amount of gigabytes of data and can easily scale up to a petabyte or more as needed. This will enable them to utilize their own data to develop new intuitions on how to improve business processes and client relations.
Initially, users start to develop a data warehouse by initiating what is called an Amazon Redshift cluster or a set of nodes. Once the cluster has been provisioned, users can seamlessly upload data sets, and then begin to perform data analysis queries. Amazon Redshift delivers super-fast query performance, regardless of size, utilizing the exact SQL-based tools and BI applications that most users are already working with today.
The Amazon Redshift service performs all of the work of setting up, operating, and scaling a data warehouse. These tasks include provisioning capacity, monitoring and backing up the cluster, and applying patches and upgrades to the Amazon Redshift engine.
Amazon Redshift Functionalities
Amazon Redshift has many valuable key functionalities. Some of its most useful functionalities include:
Reviews from Real Users
“Redshift's versioning and data security are the two most critical features. When migrating into the cloud, it's vital to secure the data. The encryption and security are there.” - Kundan A., Senior Consultant at Dynamic Elements AS
“With the cloud version whenever you want to deploy, you can scale up, and down, and it has a data warehousing capability. Redshift has many features. They have enriched and elaborate documentation that is helpful.”- Aishwarya K., Solution Architect at Capgemini
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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