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."We use Spark to process data from different data sources."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"The most valuable feature of Apache Spark is its ease of use."
"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"The scalability has been the most valuable aspect of the solution."
"It is a stable solution...It is a scalable solution."
"HPE Ezmeral Data Fabric can be accessed from any namespace globally as you would access it from a machine using an NFS."
"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."
"I like the administration part."
"The model creation was very interesting, especially with the libraries provided by the platform."
"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."
"One limitation is that not all machine learning libraries and models support it."
"Dynamic DataFrame options are not yet available."
"The migration of data between different versions could be improved."
"It requires overcoming a significant learning curve due to its robust and feature-rich nature."
"There were some problems related to the product's compatibility with a few Python libraries."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."
"The product is not user-friendly."
"Having the ability to extend the services provided by the platform to an API architecture, a micro-services architecture, could be very helpful."
"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."
"HPE Ezmeral Data Fabric is not compatible with third-party tools."
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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