Altiscale vs. HPE Ezmeral Data Fabric (MapR)

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Altiscale
Score 0.0 out of 10
N/A
Altiscale is a hosting platform for Hadoop deploymentsN/A
HPE Ezmeral Data Fabric (MapR)
Score 9.4 out of 10
N/A
HPE Ezmeral 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. Just as a loom weaves multiple threads into a single piece of fabric, HPE Ezmeral Data Fabric weaves distributed data into a single enterprise-wide data layer that ingests, processes, and stores data once and then makes it available for reuse across multiple…N/A
Pricing
AltiscaleHPE Ezmeral Data Fabric (MapR)
Editions & Modules
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Offerings
Pricing Offerings
AltiscaleHPE Ezmeral Data Fabric (MapR)
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details——
More Pricing Information
Community Pulse
AltiscaleHPE Ezmeral Data Fabric (MapR)
Top Pros

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Top Cons

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Best Alternatives
AltiscaleHPE Ezmeral Data Fabric (MapR)
Small Businesses

No answers on this topic

No answers on this topic

Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 8.8 out of 10
IBM Analytics Engine
IBM Analytics Engine
Score 8.8 out of 10
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User Ratings
AltiscaleHPE Ezmeral Data Fabric (MapR)
Likelihood to Recommend
-
(0 ratings)
7.2
(4 ratings)
User Testimonials
AltiscaleHPE Ezmeral Data Fabric (MapR)
Likelihood to Recommend
Altiscale
No answers on this topic
Hewlett Packard Enterprise
MapR is more well-suited for people who know what they are doing. I consider MapR the Hadoop distribution professionals use.
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Pros
Altiscale
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Hewlett Packard Enterprise
  • 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.
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Cons
Altiscale
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Hewlett Packard Enterprise
  • 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.
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Alternatives Considered
Altiscale
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Hewlett Packard Enterprise
I don't believe there is as much support for MapR yet compared to other more widely known products.
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Return on Investment
Altiscale
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Hewlett Packard Enterprise
  • 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
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