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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 (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 March 2026, in the Hadoop category, the mindshare of Apache Spark is 13.3%, down from 18.6% compared to the previous year. The mindshare of HPE Data Fabric is 13.5%, down from 14.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Mindshare Distribution
ProductMindshare (%)
Apache Spark13.3%
HPE Data Fabric13.5%
Other73.2%
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.
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

"We use Spark to process data from different data sources."
"The main feature that we find valuable is that it is very fast."
"This solution provides a clear and convenient syntax for our analytical tasks."
"The most valuable feature of Apache Spark is its flexibility."
"Apache Spark's ability to handle both batch and streaming data is the most valuable feature for me as it offers solid real-time processing capability, making it more efficient in managing data analytics."
"Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly."
"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."
"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."
"MapR is a great distribution, although I have limited experience with other distributors."
"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."
"This product enabled us opening up endless possibilities in data analytics, IOE/IOT, and predictive analysis."
"It is a stable solution...It is a scalable solution."
"I like the administration part."
"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."
"I highly recommend MapR."
"Initial setup is rather straightforward thanks to detailed documentation covering all the bases."
 

Cons

"Apache Spark's GUI and scalability could be improved."
"At the initial stage, the product provides no container logs to check the activity."
"The setup I worked on was really complex."
"Dynamic DataFrame options are not yet available."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"The UI for administration still has a lot of manual work to set up the cluster and get it running."
"It'd like to see file system auditing, data encryption, and certification of other vendors' tools."
"The product is not user-friendly."
"Installations and setups are still a bit cryptic and can be improved."
"It would be nice to have new developments in the Apache space (Spark, Storm, etc.)."
"The deployment could be faster. I want more support for the data lake in the next release."
"I'd say we've had issues with pricing."
"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."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"They provide an open-source license for the on-premise version."
"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 solution is affordable and there are no additional licensing costs."
"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."
"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."
"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
23%
Manufacturing Company
8%
Computer Software Company
7%
Comms Service Provider
6%
Financial Services Firm
17%
Healthcare Company
8%
Comms Service Provider
8%
Computer Software Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise16
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?
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...
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: March 2026.
884,873 professionals have used our research since 2012.