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 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.

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.

This product enabled us opening up endless possibilities in data analytics, IOE/IOT, and predictive analysis.

We implemented this for our client where data from sensors was to be analyzed and sense to be made of this data. Sensor data is huge, and earlier there was no meaningful early warning raised against the discrepancies observed on Sensor’s Data. With the help of our Big Data solution, we have been consistently raising alerts as required.

It's allowed us to use Twitter as a data source to monitor the effectiveness of our marketing campaigns.

I haven't yet used it in production environments, but I've used it extensively in two proofs-of-concept.