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Apache Spark vs HPE Data Fabric comparison

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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
5th
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 November 2026, in the Hadoop category, the mindshare of Apache Spark is 14.4%, down from 17.1% compared to the previous year. The mindshare of HPE Data Fabric is 9.2%, down from 14.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Mindshare Distribution
ProductMindshare (%)
Apache Spark14.4%
HPE Data Fabric9.2%
Other76.4%
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

"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"The fast performance is the most valuable aspect of the solution."
"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."
"It is an excellent tool to process massive amount of data."
"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."
"I feel the streaming is its best feature."
"Spark, as a tool, is easy to work with as you can work with Python, Scala, and Java."
"With Spark SQL we've now the capabilities to analyse very large quantities of data located in S3 on Amazon at very low cost comparing other solution we checked."
"This product enabled us opening up endless possibilities in data analytics, IOE/IOT, and predictive analysis."
"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."
"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."
"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."
"It is a stable solution...It is a scalable solution."
"MapR is a great distribution, although I have limited experience with other distributors."
"I like the administration part."
 

Cons

"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"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."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved."
"I ran into Spark application performance issues."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"The initial set-up is quite complex because you have to set-up many different configuration parameters that are deployment-specific."
"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."
"Dynamic DataFrame options are not yet available."
"One weakness for MapR is the Kerberos support."
"The deployment could be faster. I want more support for the data lake in the next release."
"The biggest drawback is that it has vendor locking."
"The interface part, what I'm calling the integration part, could be improved."
"HPE Ezmeral Data Fabric is not compatible with third-party tools."
"The UI for administration still has a lot of manual work to set up the cluster and get it running."
"Installations and setups are still a bit cryptic and can 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."
 

Pricing and Cost Advice

"It is an open-source platform. We do not pay for its subscription."
"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."
"The product is expensive, considering the setup."
"Apache Spark is an open-source tool."
"They provide an open-source license for the on-premise version."
"The solution is affordable and there are no additional licensing costs."
"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."
"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
16%
Outsourcing Company
10%
Comms Service Provider
9%
Construction Company
8%
Construction Company
17%
Financial Services Firm
15%
Comms Service Provider
10%
Healthcare Company
10%
 

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: November 2026.
920,769 professionals have used our research since 2012.