Name: HPE Data Fabric
Rating: 9.4 (16 reviews)
Author: Hewlett Packard Enterprise (HPE)

Help Desk

Web and Video Conferencing

Customer Support

Batch Management Software

Feature Management

Innovation Management

Product Lifecycle Management (PLM)

Development

Classroom Management

Education

Business Intelligence (BI)

Calendar

Collaboration

Digital Rights Management (DRM)

Enterprise Content Management

Statistical Analysis

Visitor Management

Enterprise

Accounting

Corporate Performance Management (CPM)

Digital Banking

Finance and Accounting

ACA Compliance

Applicant Tracking

Corporate Learning Management

HR Management

Leadership Development

Payroll

Talent Intelligence

Talent Management

Workforce Analytics

Workforce Management

Human Resources

Access Gateways

Archiving

Cloud Storage

Configuration Management Databases

Disk Cloning

Email Encryption

Fraud Detection

Infrastructure-as-a-Service

Integration Platform as a Service (iPaaS)

Log Management

Network Performance Monitoring

Office Migration and Repair

SIP Trunking

Software Asset Management

Telecom Expense Management (TEM)

Vendor Risk Management

Virtual Data Room

Web Scraping

WiFi Hotspot

Information Technology

A/B Testing

Ad Serving & Retargeting

All-in-One Marketing

Blogging

Content Management

Ecommerce Personalization

Email Marketing

Enterprise Marketing Management

Market Intelligence

Market Research

Marketing Automation

Social Media Management

Survey & Forms Building

Video Marketing

Web Analytics

Marketing

Job Site Management

Project Management

Waste Management

Writing and Proofreading Tools

Professional Services

Customer Onboarding

Customer Relationship Management (CRM)

Sales Performance Management

Sales

Fleet Management

Membership Management

Municipal

Rental Property Management

Roofing

Warranty Management

Vertical-Specific

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HPE Data Fabric (formerly MapR, acquired by HPE in 2019) is a software-defined datastore and file system that simplifies data management and analytics by unifying data across core, edge, and multicloud sources into a single platform.

HPE Data Fabric

Hadoop-Related

Likelihood to Recommend

At HP, we don't just believe in the power of technology, we believe in the power of people when technology works for you.  We believe in applying new thinking and ideas to improve the way our customers live and work. 
 If you are going to do something, Make it Matter.

Hewlett Packard Enterprise (HPE)

Cloudera Enterprise Data Hub

Apache HBase

Hortonworks Data Platform

Apache Spark

STORM

Apache Pig

Software Engineer

MapR 2016-06-13 06:08:16

DataStax Enterprise

MapR 2015-12-14 12:51:37

Cloudera Manager

MapR 2015-12-01 11:31:20

MapR 2015-12-01 20:50:44

TrustRadius is a technology and business research firm based in Austin, Texas. The company is known as a review platform for verified B2B technology and software reviews through a proprietary algorithm and human verification.

TrustRadius

Performance issues

Training needs

Slowing down

Overall system

Latest versions

Web ui

Hard to figure

File system

Versions are released

System performance

Initial deployment

Help new

Big data

New users

Allows easy

Easy to integrate

Help new users

Easy integration

Command line

Server setup

Home

Hadoop-Related Software

MapR is being used by our business research department to collect and evaluate media data. We've also used MapR in creating test data for testing performance with other technologies. It works well with other big data technologies like HBase, HIVE, and Spark. It does provide a performance advantage when used with HBase.

MapR allows easy integration with HBase and MapR DB.
Easy trial server setup for product testing.
Excellent training program to help new users get up-to-date with MapR and related products.

HBase training needs to have more materials that are questioned in the HBase certification.

We've been able to produce a solution with Hive and MapR

Cloudera Enterprise and Hortonworks Data Platform

We deploy MapR for large corporations deploying big data projects. Our software installs and configures MapR plain or fully configured with spreadsheets. The flexibility MapR has allows us to provision backend nodes with the proper networking and disk configuration automatically for optimized performance. It is being used by several corporations we support in large scale big data deployments for fraud detection and user behavior analysis.

MapR is fast. We were able to beat the Terasort in record in 2012 on 360 nodes during the initial deployment of a cluster that is now 4000 nodes.
MapR is reliable. We rarely if ever have problems deploying MapR. It's the kind of software that "just works."
MapR scales. We have a client using MapR in all their big data clusters, ranging from 50 to 630 machines. Test, development, and production all deploy MapR.

I think MapR's main problem is name recognition. Hortonworks and Cloudera both are big names in the industry, but their deployment mechanisms are a little more difficult to use, especially when trying to fully automate it's deployment.
Documentation could always be better. But really, if that's your main weakness, it's everybody's weakness.

Increased employee efficiency for sure. Our clients have various levels of expertise in their deployment and user teams, and we never receive complaints about MapR.
MapR is used by one of our financial services clients who uses it for fraud detection and user pattern analysis. They are able to turn around data much faster than they previously had with in-house applications

Cloudera Enterprise, Hortonworks Data Platform, DataStax

We were implementing a fully relational database on top of HBase. This was not a "SQL on Hadoop" project where we supported a subset of SQL. We developed a fully SQL-92 (partially SQL-99) compliant database on top of HBase. We supported all three major commercial distributions of HBase (Cloudera, Hortonworks, and MapR).

MapR had very fast I/O throughput. The write speed was several times faster than what we could achieve with the other Hadoop vendors (Cloudera and Hortonworks). This is because MapR does not use HDFS, which is essentially a "meta filesystem". HDFS is built on top of the filesystem provided by the OS. MapR has their filesystem called MapR-FS, which is a true filesystem and accesses the raw disk drives.
The MapR filesystem is very easy to integrate with other Linux filesystems. When working with HDFS from Apache Hadoop, you usually have to use either the HDFS API or various Hadoop/HDFS command line utilities to interact with HDFS. You cannot use command line utilities native to the host operation system, which is usually Linux. At least, it is not easily done without setting up NFS, gateways, etc. With MapR-FS, you can mount the filesystem within Linux and use the standard Unix commands to manipulate files.
The HBase distribution provided by MapR is very similar to the Apache HBase distribution. Cloudera and Hortonworks add GUIs and other various tools on top of their HBase distributions. The MapR HBase distribution is very similar to the Apache distribution, which is nice if you are more accustomed to using Apache HBase.

The MapR web UI console is pretty basic. When you compare it to Cloudera Manager and Apache Ambari (ships with Hortonworks), it is definitely in third place. MapR has definitely invested heavily in file system performance with their MapR-FS, but they should invest a bit more in making it easier to administer and manage a MapR cluster.
MapR should tune their MapR-FS to work better with HBase. Once again, MapR-FS has invested heavily in their own proprietary technology such as the MapR-DB in this case. MapR-DB is a "wire compatible" version of HBase, but it is a bit of a different beast from HBase. What this means is that we ran into performance issues when running vanilla HBase on MapR-FS. Basically, the write throughput was so amazingly fast for the MapR-FS that it caused compaction storms with HBase. Slowing down the HBase flushes actually improved overall system throughput for HBase on MapR-FS.

We were selling our Hadoop RDBMS as a traditional software product and there were several prospects who were interested because we supported MapR since MapR has a strong reputation for performance. I cannot comment on whether those deals were closed or not.

Cloudera Enterprise, Hortonworks Data Platform

My team was maintaining multiple Hadoop clusters on a high UCS hardware configuration powered by MapR. We were also maintaining a big cluster in a production environment and other clusters for development, QA, disaster recovery and POC. All clusters were configured with high availability. Multiple internal teams used to run their application jobs on our cluster. My team was responsible for managing and maintaining these clusters. We evaluated and implemented new big data and related tools introduced by Mapr. The goal was to make sure application customers using our cluster stay happy. Day to day jobs on our cluster include traditional Java MapReduce, Streaming, Pig, Hive, Mapr-Tables and other in-memory application jobs like spark for analytics on our company internal data. Nearly a total of 50 different use cases using Hadoop were implemented in our various clusters. At times we required getting support from Mapr on complex issues which could not be resolved by my team.

Out of the box high availability on multiple Hadoop services, which will really bring enterprise standards. High availability of JobTracker, CLDB in Hadoop 1.x, HA for Impala services etc. Less headache for my team when it comes to service failure.
Performance enhancements when migrated from Hbase to Mapr Tables.
HDFS-NFS integration pioneer.
Volume concept of HDFS storage allocation which could be controlled from MCS GUI was great.

It takes time to get latest versions of Apache ecosystem tools released as it has to be adapted.
When you have issues related to Mapr-FS or Mapr Tables, its hard to figure them out by ourselves.
Sometime new ecosystem tools versions are released without proper QA.

Less manual intervention for maintaining a cluster.

Hortonworks Data Platform, Cloudera Enterprise

HPE Data Fabric
Formerly MapR

Overview

What is HPE Data Fabric?