HPE Ezmeral Data Fabric Overview

HPE Ezmeral Data Fabric is the #6 ranked solution of our top Hadoop tools. It's rated 4.0 out of 5 stars, and is most commonly compared to Cloudera Distribution for Hadoop: HPE Ezmeral Data Fabric vs Cloudera Distribution for Hadoop

What is HPE Ezmeral Data Fabric?

Forward-leaning companies win market share because they leverage data more effectively than their competitors. Unlock the potential of your data assets with HPE Ezmeral Data Fabric (formerly MapR Data Platform). Empower your data science, analytics, and business teams by simplifying data management on a globally distributed scale. All with enterprise-grade reliability, security, and performance.

HPE Ezmeral Data Fabric is also known as MapR, MapR Data Platform.

HPE Ezmeral Data Fabric Buyer's Guide

Download the HPE Ezmeral Data Fabric Buyer's Guide including reviews and more. Updated: November 2020

HPE Ezmeral Data Fabric Customers

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

HPE Ezmeral Data Fabric Reviews

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Alexandre Akrour
CEO at Inosense
Real User
Top 5Leaderboard
Jul 17, 2019
Enables us to create preview models and has good scalability and stability

What is our primary use case?

Our primary use case is for creating preview models.

Pros and Cons

  • The model creation was very interesting, especially with the libraries provided by the platform.
  • Having the ability to extend the services provided by the platform to an API architecture, a micro-services architecture, could be very helpful.

What other advice do I have?

I used to work with the Map Plus form, for two years, in 2016/2017, and I went back to the product to see what kind of new improvements they made to the platform. I'm not currently using it at the moment. I'm planning to get back to it soon. We were, at that time, creating another product to be sold on the market and we wanted something that could educate different sources of data. Their connector list wasn't long enough. We stopped after one year, maybe less, because we started in September 2016, working with that platform, and then we switched in 2017. I think less than a year. Today, I…