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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
69
Ranking in other categories
Compute Service (6th), 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 June 2026, in the Hadoop category, the mindshare of Apache Spark is 13.9%, down from 17.6% compared to the previous year. The mindshare of HPE Data Fabric is 10.2%, down from 15.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Mindshare Distribution
ProductMindshare (%)
Apache Spark13.9%
HPE Data Fabric10.2%
Other75.9%
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

"The most significant advantage of Spark 3.0 is its support for DataFrame UDF Pandas UDF features."
"Apache Spark is a framework, which allows one organization to perform business and data analytics, at a very low cost, as compared to Ab-Initio or Informatica."
"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."
"Spark is used for transformations from large volumes of data, and it is usefully distributed."
"The product's deployment phase is easy."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"Organisations can now harness richer data sets and benefit from use cases, which add value to their business functions."
"It is an excellent tool to process massive amount of data."
"The model creation was very interesting, especially with the libraries provided by the platform."
"Outside of human error, MapR is probably the most stable of the major releases."
"Initial setup is rather straightforward thanks to detailed documentation covering all the bases."
"It is a stable solution...It is a scalable solution."
"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."
"HPE Ezmeral Data Fabric can be accessed from any namespace globally as you would access it from a machine using an NFS."
"MapR is a great distribution, although I have limited experience with other distributors."
"I like the administration part."
 

Cons

"They could improve the issues related to programming language for the platform."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"The solution needs to optimize shuffling between workers."
"The initial setup was not easy."
"When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"Although you are able to perform complex transformations using Spark libraries, the support for SQL to perform transformations is still limited."
"Apache Spark provides very good performance The tuning phase is still tricky."
"The UI for administration still has a lot of manual work to set up the cluster and get it running."
"I'd say we've had issues with pricing."
"One weakness for MapR is the Kerberos support."
"It'd like to see file system auditing, data encryption, and certification of other vendors' tools."
"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."
"The product is not user-friendly."
"It would be nice to have new developments in the Apache space (Spark, Storm, etc.)."
 

Pricing and Cost Advice

"Spark is an open-source solution, so there are no licensing costs."
"They provide an open-source license for the on-premise version."
"We are using the free version of the solution."
"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."
"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
"Apache Spark is an open-source tool."
"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."
"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
22%
Manufacturing Company
9%
Construction Company
8%
Comms Service Provider
7%
Financial Services Firm
18%
Construction Company
13%
Healthcare Company
10%
Comms Service Provider
9%
 

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
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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: June 2026.
900,644 professionals have used our research since 2012.