Basically, In BigData environment Hadoop is a major role for storage and processing. Coming to MapR is distribution to provide services to Eco-System. Hadoop architecture and MapR architecture have some of the difference in Storage level and Naming convention wise.
For example in In Hadoop single storage unit is called Block. But in MapR it is called Container.
Hadoop VS MapR
Coming to Architecture wise somehow the differences in both:
In Hadoop Architecture based on the Master Node (Name node) and Slave (Data Node) Concept. For Storage purpose using HDFS and Processing for MapReduce.
In MapR Architecture is Native approach it means that SAN, NAS or HDFS approaches to store the metadata. It will directly approach to SAN no need to JVM. Sometimes Hypervisor, Virtual machines are crashed then data directly pushed into HardDisk it means that if a server goes down the entire cluster re-syncs the data node’s data. MapR has its own filesystem called MapR File System for storage purpose. For processing using MapReduce in background.
There is no Name node concept in MapR Architecture. It completely on CLDB ( Container Location Data Base). CLDB contains a lot of information about the cluster. CLDB installed one or more nodes for high availability.
It is very useful for failover mechanism to recovery time in just a few seconds.
In Hadoop Architecture Cluster Size will mention for Master and Slave machine nodes but in MapR CLDB default size is 32GB in a cluster.
In Hadoop Architecture: NameNode Blocksize Replication
In MapR Architecture: Container Location DataBase Containers Mirrors
Summary: The MapR Architecture is entirely on the same architecture of Apache Hadoop including all the core components distribution. In BigData environment have different types of distributions like Cloudera, Hortonworks. But coming to MapR is Enterprise edition. MapR is a stable distribution compare to remaining all. And provide default security for all services.