HPE Ezmeral Data Fabric can be accessed from any namespace globally as you would access it from a machine using an NFS.

HPE Ezmeral Data Fabric can be described as a complete platform or as an end-to-end solution. I think it is one of the best platforms with all the required capabilities at the moment.

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.

MapR’s strength comes from their file system. Because they start with the raw disk, they are able to expose the storage through various APIs and have the ability to lockdown and secure the file system better than the Apache derivatives, which store the file blocks above the Linux file system. 

Because of MapR’s POSIX compliant file system, they can support read/write files over the WORM storage of their competitors. 

In addition, they remodeled how to track and store the blocks. So the NameNode isn’t a single point of failure and you can store a magnitude  of multiple orders of small files before you can cause a volume to have issues.

Note: This is a cluster volume, not the entire cluster. Fill up the NameNode with lots of small files upon an Apache release, and you lose the entire cluster. 

MapR-DB is a NoSQl datastore on top of MaprFS. The data can be updated and random data can be picked very fast.  MapR-DB stores data in MapR-FS and it does not have region server like in HBase.

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

• Multi-tenancy
• Security
• Ease of configuration
• Bundled ecosystem support
• MapR NFS - especially since we use Docker containers in every public-facing app and a few internal logging ones, too
• Reliability - I feel like I will likely never lose my data, with replication and simple backup methods, but that last one is hard to validate and I hope I never have to.

It has several valuable features, among them being the file system, High-Availability services, and NFS.

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. Of course the same concept is available across any solution based on the Hadoop architecture, but MapR provides bundled services and UI driven configurations which would otherwise take a long time to understand and implement.

The most valuable features for us are--

• MapR-DB
• Multi-tenancy

MapR is one of three leading Hadoop distributions. The key differentiators I found the most valuable are:

• Lack of namenode (no SPOF)
• Performance – the base components are implemented in C
• NFS functionality – copying to MapR FS is as easy as copying files to a directory