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 product’s most valuable features are lazy evaluation and workload distribution."
"Apache Spark can do large volume interactive data analysis."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"The product's deployment phase is easy."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"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."
"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"The model creation was very interesting, especially with the libraries provided by the platform."
"HPE Ezmeral Data Fabric can be accessed from any namespace globally as you would access it from a machine using an NFS."
"I like the administration part."
"It is a stable solution...It is a scalable solution."
"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."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"Apache Spark should add some resource management improvements to the algorithms."
"We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data."
"One limitation is that not all machine learning libraries and models support it."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"The initial setup was not easy."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"HPE Ezmeral Data Fabric is not compatible with third-party tools."
"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."
"Having the ability to extend the services provided by the platform to an API architecture, a micro-services architecture, could be very helpful."
"The product is not user-friendly."
"The deployment could be faster. I want more support for the data lake in the next release."
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, IBM Spectrum Computing, MongoDB and BlueData. See our Apache Spark 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.
