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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
68
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 February 2026, in the Hadoop category, the mindshare of Apache Spark is 13.4%, down from 18.4% compared to the previous year. The mindshare of HPE Data Fabric is 14.3%, down from 14.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Market Share Distribution
ProductMarket Share (%)
Apache Spark13.4%
HPE Data Fabric14.3%
Other72.3%
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

"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"The solution is scalable."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"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."
"Spark is used for transformations from large volumes of data, and it is usefully distributed."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."
"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."
"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."
"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

"From my perspective, the only thing that needs improvement is the interface, as it was not easily understandable."
"Apache Spark should add some resource management improvements to the algorithms."
"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"Very often in many of my experiments, the data set has had to be partitioned, and there have been issues in handling very large data sets, with most of my work done using Python machine learning libraries, requiring chunking, and speed of prediction has been an issue of concern in some experiments where we have had to shut down processes due to CPU requirements, then restart with different Apache configurations, and resourcing support is a major determinant if I were to name a constraint in terms of running machine learning experiments."
"The solution must improve its performance."
"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."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"The product is not user-friendly."
"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."
"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."
 

Pricing and Cost Advice

"Spark is an open-source solution, so there are no licensing costs."
"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 expensive solution."
"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."
"We are using the free version of the solution."
"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 solution is affordable and there are no additional licensing costs."
"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."
"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."
"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."
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Top Industries

By visitors reading reviews
Financial Services Firm
25%
Computer Software Company
8%
Manufacturing Company
7%
University
6%
Financial Services Firm
18%
Comms Service Provider
7%
Performing Arts
7%
Computer Software Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise15
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?
Areas for improvement are obviously ease of use considerations, though there are limitations in doing that, so while various tools like Informatica, TIBCO, or Talend offer specific aspects, licensi...
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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: February 2026.
881,707 professionals have used our research since 2012.