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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 January 2026, in the Hadoop category, the mindshare of Apache Spark is 13.9%, down from 18.2% compared to the previous year. The mindshare of HPE Data Fabric is 14.9%, up from 14.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Market Share Distribution
ProductMarket Share (%)
Apache Spark13.9%
HPE Data Fabric14.9%
Other71.2%
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.
Arnab Chatterjee - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"The good performance. The nice graphical management console. The long list of ML algorithms."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"This solution provides a clear and convenient syntax for our analytical tasks."
"Spark is used for transformations from large volumes of data, and it is usefully distributed."
"Apache Spark resolves many problems in the MapReduce solution and Hadoop, such as the inability to run effective Python or machine learning algorithms."
"Provides a lot of good documentation compared to other solutions."
"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."
"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."
"HPE Ezmeral Data Fabric can be accessed from any namespace globally as you would access it from a machine using an NFS."
"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

"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."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"Apache Spark provides very good performance The tuning phase is still tricky."
"The solution’s integration with other platforms should be improved."
"The Spark solution could improve in scheduling tasks and managing dependencies."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"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."
"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."
"HPE Ezmeral Data Fabric is not compatible with third-party tools."
"The product is not user-friendly."
"The deployment could be faster. I want more support for the data lake in the next release."
 

Pricing and Cost Advice

"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
"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."
"Spark is an open-source solution, so there are no licensing costs."
"The product is expensive, considering the setup."
"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."
"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
25%
Computer Software Company
9%
Manufacturing Company
7%
Comms Service Provider
6%
Financial Services Firm
18%
Comms Service Provider
8%
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: December 2025.
881,082 professionals have used our research since 2012.