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.
It's not structured support, which is why we don't use purely open-source projects without additional structured support.
The scalability part needs improvement as the sizing requires trial and error.
It is a distributed file system and scales reasonably well as long as it is given sufficient resources.
Amazon Redshift is a stable product, and I would rate it nine or ten out of ten for stability.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
They should bring the entire ETL data management process into Amazon Redshift.
Integration with AI could be a good improvement.
The problem with Apache Hadoop arose when the guys that originally set it up left the firm, and the group that later owned it didn't have enough technical resources to properly maintain it.
It's a pretty good price and reasonable for the product quality.
The cost of technical support is high.
The pricing of Amazon Redshift is expensive.
The specific features of Amazon Redshift that are beneficial for handling large data sets include fast retrieval due to cloud services and scalability, which allows us to retrieve data quickly.
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.
If you don't do the upgrades, the platform ages out, and that's what happened to the Hadoop content.
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
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