HPE Data Fabric vs IBM InfoSphere BigInsights [EOL] comparison

Why PeerSpot?

Hewlett Packard Enterprise and IBM are both solutions in the Hadoop category. Additionally, 100% of Hewlett Packard Enterprise users are willing to recommend the solution, compared to 83% of IBM users who would recommend it.

HPE Data Fabric

Read 12 HPE Data Fabric reviews

2,863 Views
2,376 Comparison Views

100% willing to recommend

IBM InfoSphere BigInsights ...

Read 7 IBM InfoSphere BigInsights [EOL] reviews

83% willing to recommend

HPE Data Fabric

IBM InfoSphere BigInsights ...

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Executive Summary

We performed a comparison between HPE Data Fabric and IBM InfoSphere BigInsights [EOL] based on real PeerSpot user reviews.

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop.

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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

Average Rating

8.0

Reviews Sentiment

6.1

Number of Reviews

Ranking in other categories

Hadoop (5th)

IBM InfoSphere BigInsights ...

Average Rating

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Featured Reviews

Arnab Chatterjee

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.

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it_user743022

BigData Consultant at a tech services company with 10,001+ employees

Served our customers better by giving real-time suggestions and proactive maintenance, however the UI was not interactive

* The UI was not interactive: Responses used to be very slow and hang up at times. * The UI was not really helping to track the real-time jobs and its logs. * You can bring in a better UI for job management and health checks. * Developer API documentation needs improvement.

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Quotes from Members

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

Pros

"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."

"The model creation was very interesting, especially with the libraries provided by the platform."

"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."

"Outside of human error, MapR is probably the most stable of the major releases."

"MapR is a great distribution, although I have limited experience with other distributors."

More HPE Data Fabric pros

"The thing that I have found most valuable in this solution is the BIQSQL implementation which is fully SQL ANSI compliant."

"InfoSphere Streams was the one core product from the platform in which we were using. We were building a real-time response system and we built it on InfoSphere Streams."

"This is a very helpful product, with continuous improvements by IBM and a great customer service which enables easy access to valuable information for both Hadoop developers and system administrators."

"It gives us the option of extending our analytics system."

"Definitely a product worth evaluating, esp if you are an IBM shop and if done on Bluemix, it gives a jump start on protoypes/POCs."

"This helped us to serve our customers better by giving real-time suggestions and proactive maintenance."

"It integrates with JSqsh, enabling us to submit long-running exports from the shell."

"Watson is the perfect engine for text analysis for us, but in 2014 it doesn’t support the Russian language."

Cons

"The product is not user-friendly."

"The biggest drawback is that it has vendor locking."

"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."

"Installations and setups are still a bit cryptic and can be improved."

"The deployment could be faster. I want more support for the data lake in the next release."

"The interface part, what I'm calling the integration part, could be improved."

"It would be nice to have new developments in the Apache space (Spark, Storm, etc.)."

"It'd like to see file system auditing, data encryption, and certification of other vendors' tools."

More HPE Data Fabric cons

"I'd like to see faster execution time, especially for simple queries that don't touch on many rows and don't involve many operations (Joins, Unions, Groupbys)."

"The UI was not interactive: Responses used to be very slow and hang up at times."

"I encountered issues with having the appropriate documentation resources, as well as getting the right stability when explored virtualized environments based on Virtualbox and HyperV software."

"For our business customer pricing is very important motivation, so I can advise change licensing policy from “by volume in the cluster” to “number of machines in the cluster”."

"Unfortunately the stability of the platform was an issue."

"I have found a lot of issues in Fluid Query and BigInsights Applications to move data in the enterprise version."

"Initial setup is rather complex in comparison with Cloudera."

Pricing and Cost Advice

"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."

"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 Size	Count
Small Business	4
Large Enterprise	7

By reviewers
Company Size	Count
Small Business	3
Large Enterprise	4

Comparisons

Cloudera Distribution for Hadoop vs HPE Data Fabric

Compared 17% of the time

IBM Spectrum Computing vs HPE Data Fabric

Compared 11% of the time

Amazon EMR vs HPE Data Fabric

Compared 11% of the time

IBM Analytics Engine vs HPE Data Fabric

Compared 11% of the time

Zettaset Data Platform vs HPE Data Fabric

Compared 9% of the time

More HPE Data Fabric Competitors

Cloudera Distribution for Hadoop vs IBM InfoSphere BigInsights [EOL]

Compared 42% of the time

Apache Spark vs IBM InfoSphere BigInsights [EOL]

Compared 18% of the time

Amazon EMR vs IBM InfoSphere BigInsights [EOL]

Compared 9% of the time

IBM Spectrum Computing vs IBM InfoSphere BigInsights [EOL]

Compared 8% of the time

More IBM InfoSphere BigInsights [EOL] Competitors

Product Reports

Buyer's Guide

HPE Data Fabric

October 2026

Download HPE Data Fabric product report

Buyer's Guide

Hadoop

October 2026

Download IBM InfoSphere BigInsights [EOL] product report

Also Known As

MapR, MapR Data Platform

InfoSphere BigInsights

Overview

HPE Data Fabric delivers robust data management with features like multi-tenancy, security, and ease of configuration. It supports high performance and unified analytics, making it a reliable choice for organizations looking to manage extensive data efficiently.

HPE Data Fabric provides a comprehensive data management platform with clustered node distribution and no single point of failure, ensuring high availability. Its compatibility with MapR-DB and NFS functionality allows integration with existing systems. Although there are challenges with third-party tool compatibility and upgrades, it supports big data initiatives by acting as both a database and messaging layer. Users benefit from bundled ecosystem support and simplified administration, enhancing usability across multiple teams and locations.

What features make HPE Data Fabric valuable?

Multi-tenancy: Supports multiple users or tenants securely.
Clustered Node Distribution: Distributes data across nodes for reliability.
POSIX Compliance: Compatibility with POSIX standards for ease of integration.
NFS Functionality: Facilitates network-based file sharing.
Unified Analytics: Provides integrated data analysis capabilities.

Why consider HPE Data Fabric in reviews?

Reliability: No single point of failure, ensuring consistent performance.
Cost Efficiency: Offers cost-effective data management solutions.
High Availability: Provides resilient storage and service availability.
Ease of Configuration: Streamlines administrative tasks with UI-driven tools.

Organizations in sectors such as finance, healthcare, and logistics use HPE Data Fabric to manage large volumes of data efficiently. Its role in supporting distributed processing and acting as a NoSQL storage solution enables these industries to leverage big data for enhanced operational insights and decision-making capabilities. The inclusion of AI tools further expands its utility, facilitating advanced data environments that are cost-effective and scalable for growing organizational demands.

Hewlett Packard Enterprise

IBM BigInsights delivers a rich set of advanced analytics capabilities that allows enterprises to analyze massive volumes of structured and unstructured data in its native format. The software combines open source Apache Hadoop with IBM innovations including sophisticated text analytics, IBM BigSheets for data exploration, IBM Big SQL for SQL access to data in Hadoop, and a range of performance, security and administrative features. The result is a cost-effective and user-friendly solution for complex, big data analytics.

IBM

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

Coherent Path Inc., Optibus, Delhaize America, Diyotta Inc., Ernst & Young, Teikoku Databank Ltd., NCSU, Vestas

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop. Updated: October 2026.

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