We performed a comparison between Apache Spark 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."The main feature that we find valuable is that it is very fast."
"I feel the streaming is its best feature."
"The solution has been very stable."
"The most valuable feature of Apache Spark is its flexibility."
"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."
"The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"The product's deployment phase is easy."
"I like the administration part."
"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 model creation was very interesting, especially with the libraries provided by the platform."
"It is a stable solution...It is a scalable solution."
"The product could improve the user interface and make it easier for new users."
"The setup I worked on was really complex."
"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."
"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"It's not easy to install."
"It should support more programming languages."
"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 deployment could be faster. I want more support for the data lake in the next release."
"The product is not user-friendly."
"HPE Ezmeral Data Fabric is not compatible with third-party tools."
"Having the ability to extend the services provided by the platform to an API architecture, a micro-services architecture, could be very helpful."
Apache Spark is ranked 1st in Hadoop with 60 reviews while HPE Ezmeral Data Fabric is ranked 5th in Hadoop with 12 reviews. Apache Spark is rated 8.4, while HPE Ezmeral Data Fabric is rated 8.0. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". 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". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Cloudera Distribution for Hadoop, whereas HPE Ezmeral Data Fabric is most compared with Cloudera Distribution for Hadoop, Amazon EMR, MongoDB, IBM Spectrum Computing and Informatica Big Data Parser. See our Apache Spark vs. HPE Ezmeral Data Fabric report.
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