No more typing reviews! Try our Samantha, our new voice AI agent.

Apache Spark vs HPE Data Fabric comparison

Why PeerSpot?
 

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
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

"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"It is an excellent tool to process massive amount of data."
"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"The fault tolerant feature is provided."
"We have 1000x improvement in performance over other techniques."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"I found the solution stable. We haven't had any problems with it."
"As it uses in-memory data processing, Spark is very fast."
"I like the administration part."
"It is a stable solution...It is a scalable solution."
"Initial setup is rather straightforward thanks to detailed documentation covering all the bases."
"MapR is a great distribution, although I have limited experience with other distributors."
"HPE Ezmeral Data Fabric can be accessed from any namespace globally as you would access it from a machine using an NFS."
"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."
"Outside of human error, MapR is probably the most stable of the major releases."
"I highly recommend MapR."
 

Cons

"Better data lineage support."
"I do not know exactly what was the reason to move away from Apache Spark or the underlying database system, but it was simply a decision driven by the customer."
"Apache Spark's GUI and scalability could be improved."
"The initial setup was not easy."
"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."
"At times when users do not know how to use Spark and request a lot of resources, then the underlying JVMs can crash, which is a big sense of worry."
"HPE Ezmeral Data Fabric is not compatible with third-party tools."
"I'd say we've had issues with pricing."
"The deployment could be faster. I want more support for the data lake in the next release."
"One weakness for MapR is the Kerberos support."
"It'd like to see file system auditing, data encryption, and certification of other vendors' tools."
"The interface part, what I'm calling the integration part, could be improved."
"The biggest drawback is that it has vendor locking."
"Having the ability to extend the services provided by the platform to an API architecture, a micro-services architecture, could be very helpful."
 

Pricing and Cost Advice

"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."
"The solution is affordable and there are no additional licensing costs."
"Apache Spark is an open-source tool."
"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."
"Spark is an open-source solution, so there are no licensing costs."
"We are using the free version of the 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."
"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."
"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."
"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.
920,769 professionals have used our research since 2012.
 

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