Apache Spark vs HPE Data Fabric comparison

Apache and Hewlett Packard Enterprise are both solutions in the Hadoop category. Apache is ranked #2 with an average rating of 8.8, while Hewlett Packard Enterprise is ranked #4 with an average rating of 8.0. Apache holds a 19.0% mindshare in H, compared to Hewlett Packard Enterprise’s 14.4% 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 67 Apache Spark reviews

3,754 Views
788 Comparison Views

90% willing to recommend

HPE Data Fabric

Read 12 HPE Data Fabric reviews

1,085 Views
901 Comparison Views

100% willing to recommend

Apache Spark

HPE Data Fabric

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Apache Spark and HPE 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 Data Fabric Report (Updated: September 2025).

Buyer's Guide

Apache Spark vs. HPE Data Fabric

September 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

2nd

Average Rating

8.4

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

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

HPE 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 October 2025, in the Hadoop category, the mindshare of Apache Spark is 19.0%, up from 18.7% compared to the previous year. The mindshare of HPE Data Fabric is 14.4%, up from 13.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Market Share Distribution
Product	Market Share (%)
Apache Spark	19.0%
HPE Ezmeral Data Fabric	14.4%
Other	66.6%

Hadoop

Featured Reviews

Omar Khaled

Data Engineer at a tech company with 10,001+ employees

Empowering data consolidation and fast decision-making with efficient big data processing

I can improve the organization's functions by taking less time to make decisions. To make the right decision, you need the right data, and a solution can provide this by hiring talent and employees who can consolidate data from different sources and organize it. Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming. To make the right decision, you should have both accurate and fast data. Apache Spark itself is similar to the Python programming language. Python is a language with many libraries for mathematics and machine learning. Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code. Within it, there are many APIs, including SQL APIs, allowing you to write SQL code within a Python function in Apache Spark. You can also use Apache Spark Structured Streaming and machine learning APIs.

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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

"Features include machine learning, real time streaming, and data processing."

"The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."

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

"Spark is used for transformations from large volumes of data, and it is usefully distributed."

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

"The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."

"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."

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

More Apache Spark pros

"I like the administration part."

"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."

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

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

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

Cons

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

"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."

"It requires overcoming a significant learning curve due to its robust and feature-rich nature."

"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."

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

"They could improve the issues related to programming language for the platform."

"I would like to see integration with data science platforms to optimize the processing capability for these tasks."

"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."

More Apache Spark cons

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

"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."

"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."

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

Pricing and Cost Advice

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

"Apache Spark is an expensive solution."

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

"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."

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

"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."

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

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

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."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

11%

Comms Service Provider

Manufacturing Company

Financial Services Firm

15%

Computer Software Company

13%

Comms Service Provider

12%

Government

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	27
Midsize Enterprise	15
Large Enterprise	32

By reviewers
Company Size	Count
Small Business	4
Large Enterprise	7

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.

See all answers

What needs improvement with Apache Spark?

Regarding Apache Spark, I have only used Apache Spark Structured Streaming, not the machine learning components. I am uncertain about specific improvements needed today. However, after five years, ...

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Comparisons

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AWS Lambda vs Apache Spark

Compared 8% of the time

Pentaho Business Analytics vs Apache Spark

Compared 3% of the time

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Cloudera Distribution for Hadoop vs HPE Data Fabric

Compared 44% of the time

Cloudera Data Platform vs HPE Data Fabric

Compared 16% of the time

Amazon EMR vs HPE Data Fabric

Compared 10% of the time

IBM Analytics Engine vs HPE Data Fabric

Compared 9% of the time

Spark SQL vs HPE Data Fabric

Compared 7% of the time

More HPE Data Fabric Competitors

Product Reports

Buyer's Guide

Apache Spark

October 2025

Download Apache Spark product report

Buyer's Guide

HPE Data Fabric

October 2025

Download HPE 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 Data Fabric

September 2025

Free Report: Apache Spark vs. HPE Data Fabric

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

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