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Apache Spark vs HPE Data Fabric comparison

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Comparison Buyer's Guide

Executive Summary

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
6.9
Number of Reviews
69
Ranking in other categories
Compute Service (6th), Java Frameworks (2nd)
HPE Data Fabric
Ranking in Hadoop
4th
Average Rating
8.0
Reviews Sentiment
6.1
Number of Reviews
12
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of September 2026, in the Hadoop category, the mindshare of Apache Spark is 14.1%, down from 19.3% compared to the previous year. The mindshare of HPE Data Fabric is 9.6%, down from 14.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Mindshare Distribution
ProductMindshare (%)
Apache Spark14.1%
HPE Data Fabric9.6%
Other76.3%
Hadoop
 

Featured Reviews

Devindra Weerasooriya - PeerSpot reviewer
Data Architect at Devtech
Provides a consistent framework for building data integration and access solutions with reliable performance
The in-memory computation feature is certainly helpful for my processing tasks. It is helpful because while using structures that could be held in memory rather than stored during the period of computation, I go for the in-memory option, though there are limitations related to holding it in memory that need to be addressed, but I have a preference for in-memory computation. The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.
Hamid M. Hamid - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"The scalability has been the most valuable aspect of the solution."
"The product is useful for analytics."
"Apache Spark's ability to handle both batch and streaming data is the most valuable feature for me as it offers solid real-time processing capability, making it more efficient in managing data analytics."
"Spark is used for transformations from large volumes of data, and it is usefully distributed."
"Spark Streaming's micro-batch mode helps improving performance."
"We have built a product called NetBot where we take any form of data, such as large email data, images, videos, or transactional data, and transform unstructured textual and video data into structured transactional data to create an enterprise-wide smart data grid that is then used by downstream analytics tools."
"It allows the loading and investigation of very lard data sets, has MLlib for machine learning, Spark streaming, and both the new and old dataframe API."
"Spark replaced other costly products, reducing costs by thirty-eight percent."
"It is a stable solution...It is a scalable solution."
"This product enabled us opening up endless possibilities in data analytics, IOE/IOT, and predictive analysis."
"I highly recommend MapR."
"Initial setup is rather straightforward thanks to detailed documentation covering all the bases."
"Outside of human error, MapR is probably the most stable of the major releases."
"My first choice is MapR, as it is more adaptable to different contexts, and it could be customized in some way to fit the different needs, and this is my first choice and my first advice to people who ask me about this particular platform."
"Our customer purchased a paid support service and so far MapR has addressed our issues well."
"The fact that the heavy computation is required on Big Data can be distributed across many nodes in a cluster, makes this solution a winner."
 

Cons

"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"Better data lineage support."
"The migration of data between different versions could be improved."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved."
"Apache Spark lacks geospatial data."
"The initial setup was not easy."
"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."
"The interface part, what I'm calling the integration part, could be improved."
"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."
"I'd say we've had issues with pricing."
"Having the ability to extend the services provided by the platform to an API architecture, a micro-services architecture, could be very helpful."
"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."
"It would be nice to have new developments in the Apache space (Spark, Storm, etc.)."
"The biggest drawback is that it has vendor locking."
 

Pricing and Cost Advice

"The solution is affordable and there are no additional licensing costs."
"Apache Spark is an expensive solution."
"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 an open-source platform. We do not pay for its subscription."
"The product is expensive, considering the setup."
"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."
"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."
"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."
"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."
"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."
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Top Industries

By visitors reading reviews
Financial Services Firm
17%
Outsourcing Company
9%
Comms Service Provider
9%
Construction Company
9%
Construction Company
17%
Financial Services Firm
17%
Comms Service Provider
9%
Healthcare Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise16
Large Enterprise33
By reviewers
Company SizeCount
Small Business4
Large Enterprise7
 

Questions from the Community

What is your experience regarding pricing and costs for Apache Spark?
Apache Spark is open-source, so it doesn't incur any charges.
What needs improvement with Apache Spark?
I find that there really lacks the technical depth to do any recommendations for future updates of Apache Spark. I used it for two years for our prototype work and testing things, but because I had...
What is your primary use case for Apache Spark?
I attempted to use Apache Spark in one of our customer projects, but after the initial test, our customer moved to another technology and another database system. I do not have any final remarks on...
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Also Known As

No data available
MapR, MapR Data Platform
 

Overview

 

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
Find out what your peers are saying about Apache Spark vs. HPE Data Fabric and other solutions. Updated: September 2026.
913,806 professionals have used our research since 2012.