Try our new research platform with insights from 80,000+ expert users

Apache Spark vs HPE Data Fabric comparison

 

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 (5th), 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 March 2026, in the Hadoop category, the mindshare of Apache Spark is 13.3%, down from 18.6% compared to the previous year. The mindshare of HPE Data Fabric is 13.5%, down from 14.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Mindshare Distribution
ProductMindshare (%)
Apache Spark13.3%
HPE Data Fabric13.5%
Other73.2%
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

"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."
"The fault tolerant feature is provided."
"The deployment of the product is easy."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"The best features in Apache Spark that I appreciate are the fast database access, the data transformation, and the data exchange."
"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."
"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."
"This solution provides a clear and convenient syntax for our analytical tasks."
"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."
"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."
 

Cons

"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"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."
"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."
"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."
"The solution’s integration with other platforms should be improved."
"They could improve the issues related to programming language for the platform."
"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

"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."
"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 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."
"The solution is affordable and there are no additional licensing costs."
"It is an open-source solution, it is free of charge."
"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."
report
Use our free recommendation engine to learn which Hadoop solutions are best for your needs.
884,873 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
23%
Manufacturing Company
8%
Computer Software Company
7%
Comms Service Provider
6%
Financial Services Firm
17%
Healthcare Company
8%
Comms Service Provider
8%
Computer Software Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise16
Large Enterprise32
By reviewers
Company SizeCount
Small Business4
Large Enterprise7
 

Questions from the Community

What do you like most about Apache Spark?
We use Spark to process data from different data sources.
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...
Ask a question
Earn 20 points
 

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: March 2026.
884,873 professionals have used our research since 2012.