Apache Spark vs HPE Ezmeral Data Fabric comparison

Apache and Hewlett Packard Enterprise are both solutions in the Hadoop category. Apache is ranked #1 with an average rating of 8.6, while Hewlett Packard Enterprise is ranked #4 with an average rating of 8.5. Apache holds a 18.3% mindshare in H, compared to Hewlett Packard Enterprise’s 15.2% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 100% of Hewlett Packard Enterprise users who would recommend it.

Apache Spark

Read 66 Apache Spark reviews

1,064 Views
784 Comparison Views

90% willing to recommend

HPE Ezmeral Data Fabric

Read 12 HPE Ezmeral Data Fabric reviews

1,021 Views
528 Comparison Views

100% willing to recommend

Apache Spark

HPE Ezmeral Data Fabric

Comparison Buyer's Guide

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Executive Summary

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.

To learn more, read our detailed Apache Spark vs. HPE Ezmeral Data Fabric Report (Updated: June 2025).

Buyer's Guide

Apache Spark vs. HPE Ezmeral Data Fabric

June 2025

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Apache Spark

Ranking in Hadoop

1st

Average Rating

8.4

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

Compute Service (4th), Java Frameworks (2nd)

HPE Ezmeral Data Fabric

Ranking in Hadoop

4th

Average Rating

8.0

Reviews Sentiment

6.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of July 2025, in the Hadoop category, the mindshare of Apache Spark is 18.3%, down from 20.4% compared to the previous year. The mindshare of HPE Ezmeral Data Fabric is 15.2%, up from 12.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

Open-source solution for data processing with portability

Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly. While many choices now exist, Spark remains easy to use, particularly with Python. You can utilize familiar programming styles similar to Pandas in Python, including object-oriented programming. Another advantage is its portability. I can prototype and perform some initial tasks on my laptop using Spark without needing to be on Databricks or any cloud platform. I can transfer it to Databricks or other platforms, such as AWS. This flexibility allows me to improve processing even on my laptop. For instance, if I'm processing large amounts of data and find my laptop becoming slow, I can quickly switch to Spark. It handles small and large datasets efficiently, making it a versatile tool for various data processing needs.

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Hamid M. Hamid

Data architect at Banking Sector

A stable and scalable tool that serves as a great database

The initial setup of HPE Ezmeral Data Fabric is easy. I am not sure how long it took to deploy HPE Ezmeral Data Fabric, but I haven't heard about any disadvantages when it comes to the time taken for the deployment. I remember that one of our company's clients who had purchased the product never mentioned the product's setup phase being complex. One of the drawbacks with HPE Ezmeral Data Fabric stems from the fact that the product's upgrade was not straightforward, and it was a complex process since one of the teams in my company who deals with the tool found the upgrade part to be tough. The solution is deployed on an on-premises model. My company has two dedicated staff members to look after the deployment and maintenance phases of HPE Ezmeral Data Fabric.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The most significant advantage of Spark 3.0 is its support for DataFrame UDF Pandas UDF features."

"The most valuable feature of Apache Spark is its flexibility."

"The fault tolerant feature is provided."

"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."

"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."

"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 product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."

"We use Spark to process data from different data sources."

More Apache Spark pros

"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."

"It is a stable solution...It is a scalable solution."

"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."

Cons

"The initial setup was not easy."

"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."

"For improvement, I think the tool could make things easier for people who aren't very technical. There's a significant learning curve, and I've seen organizations give up because of it. Making it quicker or easier for non-technical people would be beneficial."

"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."

"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."

"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."

"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."

"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."

More Apache Spark cons

"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 product is not user-friendly."

"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."

Pricing and Cost Advice

"Apache Spark is an expensive solution."

"They provide an open-source license for the on-premise version."

"Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."

"It is an open-source solution, it is free of charge."

"Spark is an open-source solution, so there are no licensing costs."

"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."

"Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources."

"The tool is an open-source product. If you're using the open-source Apache Spark, no fees are involved at any time. Charges only come into play when using it with other services like Databricks."

More Apache Spark pricing and cost advice

"HPE is flexible with you if you are an existing customer. They offer different models that might be beneficial for your organization. It all depends on how you negotiate."

"There is a need for my company to pay for the licensing costs of the solution."

"The tool's price is cheap and based on a usage basis. The solution's licensing costs are yearly and there are no extra costs."

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

12%

Manufacturing Company

Comms Service Provider

Financial Services Firm

20%

Computer Software Company

13%

Comms Service Provider

10%

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Apache Spark?

We use Spark to process data from different data sources.

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What is your experience regarding pricing and costs for Apache Spark?

Apache Spark is open-source, so it doesn't incur any charges.

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What needs improvement with Apache Spark?

There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...

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What do you like most about HPE Ezmeral Data Fabric?

It is a stable solution...It is a scalable solution.

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What needs improvement with HPE Ezmeral Data Fabric?

There are some drawbacks in HPE Ezmeral Data Fabric when it comes to the interoperability part. HPE Ezmeral Data Fabric is not compatible with third-party tools. For example, HPE Ezmeral Data Fabri...

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What is your primary use case for HPE Ezmeral Data Fabric?

The main purpose of HPE Ezmeral Data Fabric for me is that it acts as a database. In my company, we store our data with the help of HPE Ezmeral Data Fabric. It is possible to use Spark engine with ...

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Comparisons

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Compared 12% of the time

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Compared 10% of the time

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Compared 7% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 6% of the time

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Compared 58% of the time

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Compared 12% of the time

Amazon EMR vs HPE Ezmeral Data Fabric

Compared 9% of the time

Informatica Big Data Parser vs HPE Ezmeral Data Fabric

Compared 8% of the time

IBM Analytics Engine vs HPE Ezmeral Data Fabric

Compared 7% of the time

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Product Reports

Buyer's Guide

Apache Spark

July 2025

Download Apache Spark product report

Buyer's Guide

HPE Ezmeral Data Fabric

June 2025

Download HPE Ezmeral Data Fabric product report

Also Known As

No data available

MapR, MapR Data Platform

Overview

Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

Apache

Forward-leaning companies win market share because they leverage data more effectively than their competitors. Unlock the potential of your data assets with HPE Ezmeral Data Fabric (formerly MapR Data Platform). Empower your data science, analytics, and business teams by simplifying data management on a globally distributed scale. All with enterprise-grade reliability, security, and performance.

Hewlett Packard Enterprise

Sample Customers

NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions

Valence Health, Goodgame Studios, Pico, Terbium Labs, sovrn, Harte Hanks, Quantium, Razorsight, Novartis, Experian, Dentsu ix, Pontis Transitions, DataSong, Return Path, RAPP, HP

Buyer's Guide

Apache Spark vs. HPE Ezmeral Data Fabric

June 2025

Free Report: Apache Spark vs. HPE Ezmeral Data Fabric

Find out what your peers are saying about Apache Spark vs. HPE Ezmeral Data Fabric and other solutions. Updated: June 2025.

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