HPE Ezmeral Data Fabric Reviews

We've got a variety of use cases. We use Ezmeral for large file data storage and distributed processing with Spark and other utilities. We also use it as a NoSQL. The HPE Ezmeral Data Fabric database lets you store tons of data in NoSQL format. You can also use it as a messaging layer. 

It's a combination of five or six use cases, but our primary use case is to store big data and make it easily accessible across locations and namespaces. More than a hundred people use it across multiple teams. 

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

The main purpose of HPE Ezmeral Data Fabric for me is that it acts as a database. In my company, we store our data with the help of HPE Ezmeral Data Fabric. It is possible to use Spark engine with HPE Ezmeral Data Fabric. HPE Ezmeral Data Fabric is also useful for data processing and data ingestion.

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.

Our primary use case is for creating preview models.

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

As an independent consultant, I support multiple use cases depending on the client's need(s).

To be clear, all of the main three vendors are capable of supporting multiple big data use cases. Where they differ is going to be in terms of scalability, supported tools, and the stability of the tools. 

MapR has MapR-DB, which supports most of HBase’s APIs except for coprocessors. It was a conscious decision and it helped to improve the security and stability. MapR also has written most of their tools in C/C++ instead of Java. Again, since MapR supports external tools, solutions are somewhat portable, except for those that rely on things like Kerberos and HBase coprocessors. 

Note: MapR does support Kerberos, however there are solutions which rely on a combination of products which are still not very well supported. 

We use HPE Ezmeral Data Fabric to consolidate data into the data lake. It's our primary use case. We are using HPE as a data lake.

I like the administration part.

• 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.

The features it comes with we leverage, like MapR NFS, for example. This allows our Docker environment to write directly to HDFS and NoSQL on the MapR cluster without the need for other tools, or for clients who need to connect to secured volumes for data ingestion, or even some resources to crunch some numbers.

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.

We have a couple of Hadoop development projects actively going on. Some of them are time series data and others involve customer interaction and marketing data. A NoSQL data storage is ideal for such applications.

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. 

The product is not user-friendly.