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 17.8% 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

1,404 Views
1,090 Comparison Views

90% willing to recommend

HPE Ezmeral Data Fabric

Read 12 HPE Ezmeral Data Fabric reviews

1,225 Views
640 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: April 2025).

Buyer's Guide

Apache Spark vs. HPE Ezmeral Data Fabric

April 2025

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Helped 850,028 peers since 2012

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

7.7

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 May 2025, in the Hadoop category, the mindshare of Apache Spark is 17.8%, down from 21.4% compared to the previous year. The mindshare of HPE Ezmeral Data Fabric is 15.2%, up from 11.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

Reliable, able to expand, and handle large amounts of data well

We use batch processing. It works well with our formats and file versions. There's a lot of functionality. In our pipeline each hour, we make a copy of data from MongoDB, of the changes from MongoDB to some specific file. Each time pipeline copied all of the data, it would do it each time without changes to all of the tables. Tables have a lot of data, and in the last MongoDB version, there is a possibility to read only changed data. This reduced the cost and configuration of the cluster, and we saved about $150,000. The solution is scalable. It's a stable product.

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Arnab Chatterjee

Regional Head of Data and Application Platform at a financial services firm with 10,001+ employees

It's flexible and easily accessible across multiple locations, but the upgrade process is complicated

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. They're transforming their host stack to increase cloud readiness and edge compute capability. HPE is transitioning from a standard data-driven approach to one powered by AI analytics. That's something they have released very recently. I haven't tried that, but it will probably make things easier. The ability to adapt Ezmeral to the public cloud is probably missing. I've heard that they're getting leaner. However, it doesn't have a clear managed services offering for you if you want to deploy this stack on the cloud. That's a problem. This probably won't meet your needs if you require consistency across on-prem and the cloud. It's not Ezmeral's fault. None of the products would fit the bill. Cloud offerings are biased towards their own implementation. It's a general issue on most big data platforms. They're already working towards that, but it hasn't been released.

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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

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

"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."

"Spark can handle small to huge data and is suitable for any size of company."

"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 most valuable feature of this solution is its capacity for processing large amounts of data."

"Provides a lot of good documentation compared to other solutions."

"Apache Spark provides a very high-quality implementation of distributed data processing."

"The product’s most valuable features are lazy evaluation and workload distribution."

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."

"I like the administration part."

"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."

Cons

"The Spark solution could improve in scheduling tasks and managing dependencies."

"It should support more programming languages."

"At the initial stage, the product provides no container logs to check the activity."

"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."

"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."

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

"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."

"It's not easy to install."

More Apache Spark cons

"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."

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

"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."

Pricing and Cost Advice

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

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."

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

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

"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."

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

"Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."

"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

13%

Manufacturing Company

Comms Service Provider

Financial Services Firm

19%

Computer Software Company

15%

Retailer

Comms Service Provider

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.

See all answers

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

Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requires significant investment in infrastructure, which can be expensive. While cloud...

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What needs improvement with Apache Spark?

The Spark solution could improve in scheduling tasks and managing dependencies. Spark alone cannot handle sequential tasks, requiring environments like Airflow scheduler or scripts. For instance, o...

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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 26% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 7% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

More Apache Spark Competitors

Cloudera Distribution for Hadoop vs HPE Ezmeral Data Fabric

Compared 59% of the time

Spark SQL vs HPE Ezmeral Data Fabric

Compared 9% of the time

Amazon EMR vs HPE Ezmeral Data Fabric

Compared 8% of the time

IBM Analytics Engine vs HPE Ezmeral Data Fabric

Compared 7% of the time

Cloudera Data Platform vs HPE Ezmeral Data Fabric

Compared 7% of the time

More HPE Ezmeral Data Fabric Competitors

Product Reports

Buyer's Guide

Apache Spark

April 2025

Download Apache Spark product report

Buyer's Guide

HPE Ezmeral Data Fabric

April 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

April 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: April 2025.

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