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HPE Data Fabric vs IBM Analytics Engine 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

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
IBM Analytics Engine
Ranking in Hadoop
11th
Average Rating
8.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2026, in the Hadoop category, the mindshare of HPE Data Fabric is 9.2%, down from 14.5% compared to the previous year. The mindshare of IBM Analytics Engine is 2.9%, up from 2.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Mindshare Distribution
ProductMindshare (%)
HPE Data Fabric9.2%
IBM Analytics Engine2.9%
Other87.9%
Hadoop
 

Featured Reviews

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.
Saket Pandey - PeerSpot reviewer
Product Manager at a hospitality company with 51-200 employees
Good solution for small and medium-sized businesses and highly stable
I would advise instead of only going through other reviews; it would be great if you could schedule a talk with the IBM team that would be helping you implement this solution. They would deep dive into the process and protocols you are currently set up in, and then they will provide you an optimal solution and optimal price. So I believe talking with the support team was really amazing. They even helped us in some other parts as well. It is a good solution for small and medium-sized businesses. Overall, I would rate the solution an eight out of ten because of the support team. They were able to resolve issues, which helped us deploy higher-grade solutions correctly and quickly. We were able to ensure that our processes were working correctly, and we saved about 15-16% of a week's time by using this solution. In terms of return on investment, we saved about $7,000 a month.

Quotes from Members

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

Pros

"I highly recommend MapR."
"Initial setup is rather straightforward thanks to detailed documentation covering all the bases."
"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."
"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."
"It is a stable solution...It is a scalable solution."
"This product enabled us opening up endless possibilities in data analytics, IOE/IOT, and predictive analysis."
"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."
"I like the administration part."
"The best part was that we could make minor changes in the way we were bifurcating the data, even at a very small scale. The accuracy of conversion was also very high."
 

Cons

"The interface part, what I'm calling the integration part, could be improved."
"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 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."
"Installations and setups are still a bit cryptic and can be improved."
"I'd say we've had issues with pricing."
"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."
"One area for improvement would be the initial setup stage, which took longer than expected."
 

Pricing and Cost Advice

"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."
Information not available
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Top Industries

By visitors reading reviews
Construction Company
17%
Financial Services Firm
15%
Comms Service Provider
10%
Healthcare Company
10%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business4
Large Enterprise7
No data available
 

Also Known As

MapR, MapR Data Platform
No data available
 

Overview

 

Sample Customers

Valence Health, Goodgame Studios, Pico, Terbium Labs, sovrn, Harte Hanks, Quantium, Razorsight, Novartis, Experian, Dentsu ix, Pontis Transitions, DataSong, Return Path, RAPP, HP
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