We performed a comparison between Amazon EMR and HPE Ezmeral Data Fabric based on real PeerSpot user reviews.
Find out in this report how the two Hadoop 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."
"One of the valuable features about this solution is that it's managed services, so it's pretty stable, and scalable as much as you wish. It has all the necessary distributions. With some additional work, it's also possible to change to a Spark version with the latest version of EMR. It also has Hudi, so we are leveraging Apache Hudi on EMR for change data capture, so then it comes out-of-the-box in EMR."
"It allows users to access the data through a web interface."
"The initial setup is pretty straightforward."
"It has a variety of options and support systems."
"The solution is scalable."
"The solution helps us manage huge volumes of data."
"The initial setup is straightforward."
"It is a stable solution...It is a scalable solution."
"I like the administration part."
"The model creation was very interesting, especially with the libraries provided by the platform."
"My customers find the product cheaper compared to other solutions. The previous solution that we used did not have unified analytics like the runtime or the analog."
"HPE Ezmeral Data Fabric can be accessed from any namespace globally as you would access it from a machine using an NFS."
"The legacy versions of the solution are not supported in the new versions."
"There is no need to pay extra for third-party software."
"The dashboard management could be better. Right now, it's lacking a bit."
"We don't have much control. If we have multiple users, if they want to scale up, the cost will go and increase and we don't know how we can restrict that price part."
"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 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."
"Amazon EMR can improve by adding some features, such as megastore services and HiveServer2. Additionally, the user interface could be better, similar to what Apache service provides, cross-platform services."
"Upgrading Ezmeral to a new version is a pain. They're trying to make the solution more container-friendly, so I think they're going in the right direction. The only problem we've had in the past was the upgrades. The process isn't smooth due to how the Red Hat operating system upgrades currently work."
"The product is not user-friendly."
"The deployment could be faster. I want more support for the data lake in the next release."
"Having the ability to extend the services provided by the platform to an API architecture, a micro-services architecture, could be very helpful."
"HPE Ezmeral Data Fabric is not compatible with third-party tools."
Amazon EMR is ranked 3rd in Hadoop with 20 reviews while HPE Ezmeral Data Fabric is ranked 5th in Hadoop with 12 reviews. Amazon EMR is rated 7.8, while HPE Ezmeral Data Fabric 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 HPE Ezmeral Data Fabric writes "It's flexible and easily accessible across multiple locations, but the upgrade process is complicated". Amazon EMR is most compared with Snowflake, Cloudera Distribution for Hadoop, Azure Data Factory, Amazon Redshift and Hortonworks Data Platform, whereas HPE Ezmeral Data Fabric is most compared with Cloudera Distribution for Hadoop, MongoDB, IBM Spectrum Computing, Informatica Big Data Parser and BlueData. See our Amazon EMR vs. HPE Ezmeral Data Fabric report.
See our list of best Hadoop vendors.
We monitor all Hadoop 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.