We performed a comparison between Amazon EMR and Amazon Redshift based on real PeerSpot user reviews.
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."It has a variety of options and support systems."
"We are using applications, such as Splunk, Livy, Hadoop, and Spark. We are using all of these applications in Amazon EMR and they're helping us a lot."
"The initial setup is straightforward."
"When we grade big jobs from on-prem to the cloud, we do it in EMR with Spark."
"The solution is pretty simple to set up."
"Amazon EMR is a good solution that can be used to manage big data."
"In Amazon EMR it is easy to rebuild anything, easy to upgrade and has good fault tolerance."
"The ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions."
"Redshift has an advantage when it comes to administration, making it easier to manage and collaborate."
"The product is relatively easy to use because there is no indexing and no partitions."
"The product offers good support for the data lake."
"We have found Machine Learning use cases are very nice."
"Redshift Spectrum is the most valuable feature."
"The solution's flexibility is its most valuable feature. It's also easy to scale and has relatively painless pricing."
"The most valuable feature of Amazon Redshift is its ability to handle really large sets of data."
"The feature that we find most useful is the ability to do analytics on the fly."
"The dashboard management could be better. Right now, it's lacking a bit."
"The legacy versions of the solution are not supported in the new versions."
"There is room for improvement in pricing."
"There were times where they would release new versions and it seemed to end up breaking old versions, which is very strange."
"The initial setup was time-consuming."
"The product must add some of the latest technologies to provide more flexibility to the users."
"Modules and strategies should be better handled and notified early in advance."
"There is no need to pay extra for third-party software."
"The solution has four maintenance windows so, when it comes to stability, I think it would be better to decrease their number."
"AWS Snowflake has a very good feature for cloning databases. It makes it easy to clone a data warehouse, which is useful. I would like to see this feature in Redshift."
"They should provide a better way to work with interim data in a structured way than to store it in parquet files locally."
"We are using third-party tools to integrate Amazon Redshift, they should create their own interface on their own for it to be easily connected on the AWS itself."
"The refreshment rate of data reaching Redshift from other sources should be faster."
"It would be good to see Redshift as a serverless offering."
"The technical support should be better in terms of their knowledge, and they should be more customer-friendly."
"Amazon should provide more cloud-native tools that can integrate with Redshift like Microsoft's development tools for Azure."
Amazon EMR is ranked 9th in Cloud Data Warehouse with 20 reviews while Amazon Redshift is ranked 4th in Cloud Data Warehouse with 58 reviews. Amazon EMR is rated 7.8, while Amazon Redshift is rated 7.8. The top reviewer of Amazon EMR writes "Provides efficient data processing features and has good scalability ". On the other hand, the top reviewer of Amazon Redshift writes "Provides one place where we can store data, and allows us to easily connect to other services with AWS". Amazon EMR is most compared with Snowflake, Cloudera Distribution for Hadoop, Azure Data Factory, Apache Spark and Microsoft Azure Synapse Analytics, whereas Amazon Redshift is most compared with AWS Lake Formation, Snowflake, Teradata, Vertica and SAP BW4HANA. See our Amazon EMR vs. Amazon Redshift report.
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We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
