We performed a comparison between Amazon EMR and VMware Tanzu Data Services 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."When we grade big jobs from on-prem to the cloud, we do it in EMR with Spark."
"The initial setup is pretty straightforward."
"This is the best tool for hosts and it's really flexible and scalable."
"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 ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions."
"It has a variety of options and support systems."
"It allows users to access the data through a web interface."
"The solution is pretty simple to set up."
"Very fast for query processing."
"The most valuable feature is asynchronous calls, which are easy to configure."
"The product's reliability is the most valuable feature."
"Reliability for the messages is key. RabbitMQ ensures your messages are safe. They are not deleted and stuff."
"The most valuable feature is that it's really customizable."
"The message routing is the most valuable feature. It is effective and flexible."
"Pivotal Greenplum's shared-nothing architecture."
"Scalability is simple because it's an MPP database. If you need more processing power or you need more storage, you just add a few more nodes in the cluster. It works on common commodity hardware. You can use any type of server. You don't need to have proprietary hardware. It's fairly flexible."
"Modules and strategies should be better handled and notified early in advance."
"As people are shifting from legacy solutions to other technologies, Amazon EMR needs to add more features that give more flexibility in managing user data."
"The legacy versions of the solution are not supported in the new versions."
"The product must add some of the latest technologies to provide more flexibility to the users."
"The product's features for storing data in static clusters could be better."
"Amazon EMR is continuously improving, but maybe something like CI/CD out-of-the-box or integration with Prometheus Grafana."
"There is room for improvement in pricing."
"The dashboard management could be better. Right now, it's lacking a bit."
"It will be very useful if we could communicate with other database types from Greenplum (using a database link)."
"They should add more analytics. Their documentation could also be improved so that I don't have to bother my co-workers and tech support so often."
"The product is pretty hard to configure."
"Lacks sufficient inbuilt machine-learning functions for complex use cases."
"Their implementation is quite tricky. It's not that easy to implement RabbitMQ as a cluster."
"Tanzu Greenplum's compression for GPText could be made more efficient."
"The fact that a single queue can't be distributed across multiple instances/nodes is a major disadvantage."
"The solution needs improvement on performance."
Amazon EMR is ranked 8th in Cloud Data Warehouse with 20 reviews while VMware Tanzu Data Services is ranked 5th in Data Warehouse with 81 reviews. Amazon EMR is rated 7.8, while VMware Tanzu Data Services is rated 8.0. The top reviewer of Amazon EMR writes "Provides efficient data processing features and has good scalability ". On the other hand, the top reviewer of VMware Tanzu Data Services writes "Reliable queueing functionality and versatile tool that can be used with any programming languages ". Amazon EMR is most compared with Snowflake, Cloudera Distribution for Hadoop, Azure Data Factory, Amazon Redshift and Apache Spark, whereas VMware Tanzu Data Services is most compared with IBM MQ, Anypoint MQ, Apache Kafka, Red Hat AMQ and ActiveMQ. See our Amazon EMR vs. VMware Tanzu Data Services 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.