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 fault tolerant feature is provided."

"The solution is scalable."

"The main feature that we find valuable is that it is very fast."

"There's a lot of functionality."

"The solution has been very stable."

"The product's initial setup phase was easy."

"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."

"The processing time is very much improved over the data warehouse solution that we were using."

More Apache Spark pros

"The model creation was very interesting, especially with the libraries provided by the platform."

"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."

"I like the administration part."

"HPE Ezmeral Data Fabric can be accessed from any namespace globally as you would access it from a machine using an NFS."

Cons

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

"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."

"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."

"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."

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

"Spark could be improved by adding support for other open-source storage layers than Delta Lake."

"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."

"The setup I worked on was really complex."

More Apache Spark cons

"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."

"HPE Ezmeral Data Fabric is not compatible with third-party tools."

"The deployment could be faster. I want more support for the data lake in the next release."

Pricing and Cost Advice

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

"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."

"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."

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

"It is an open-source platform. We do not pay for its subscription."

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."

"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

"Apache Spark is an expensive solution."

More Apache Spark pricing and cost advice

"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."

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

"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."

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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

Spring Boot vs Apache Spark

Compared 25% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 10% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 6% of the time

More Apache Spark Competitors

Cloudera Distribution for Hadoop vs HPE Ezmeral Data Fabric

Compared 58% of the time

Cloudera Data Platform vs HPE Ezmeral Data Fabric

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

More HPE Ezmeral Data Fabric Competitors

Product Reports

Buyer's Guide

Apache Spark

June 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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