Hadoop has HDFS, which is the default built in FileSystem. Cloudera use this built-in default Java...

Hadoop has HDFS, which is the default built in FileSystem. Cloudera use this built-in default Java implementation. MapR has taken a different approach. What approach has MapR taken in its FileSystem implementation, and what may be the advantages and disadvantages of MapR's approach versus other vendors? If there are disadvantages, how can they be addressed? Look at the advantages and disadvantages from user, developer, administrator and risk perspective.

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MapR is a complete enterprise-grade distribution for Apache Hadoop. The MapR Converged Data Platform has been engineered to improve Hadoop's reliability, performance, and ease of use. You can use MapR with Apache Hadoop, HDFS, and MapReduce APIs.

The main functions of the MapR Data Platform include storage, management, processing, and analysis of data for AI and analytics applications. It also provides increased reliability and ensured security over mission critical information. MapR is built for organizations with demanding production needs.

Advantages:

It is one of the fastest hadoop distribution with multi node direct access.
CDH is comparatively slower than MapR Hadoop Distribution.

Disadvantages:

MapR does not have a good interface console as Cloudera.
It's more expensive
MapR basically rewrote HDFS and HBase to be more performant, but some companies prefer the apache code base which is open source and used in the all other distributions. It can make integration with other tools easier, as there is more documentation and support from a broader community available.

MapR Advantages from user,developer,administrator and risk perspective:

Easy data ingestion: Copying data to and from the MapR cluster is as simple as copying data to a standard file system using the Direct Access NFS™ capabilities of the MapR Converged Data Platform. Applications can therefore ingest data directly into the MapR cluster in real time without the need for staging areas or redundant clusters just to ingest data.
Existing applications work: Due to the POSIX-compliant MapR Distributed File and Object Store integrated into the MapR Converge Data Platform, any application works directly on MapR without undergoing code changes. Existing tools, scripts, custom utilities and applications are good to go on day one.
Multi-tenancy: Support multiple user groups, any and all enterprise data sets, and multiple applications in the same cluster. Data modelers, developers and analysts can all work in unison on the same cluster without stepping on each other's toes.
Business continuity: The MapR Converged Data Platform provides integrated high availability (HA), data protection, and disaster recovery (DR) capabilities to protect against both hardware failure as well as site-wide failure.
Global scale: Scalability is key to the MapR Converged Data Platform so the analytics can operate at both data-at-rest and data-in-motion. MapR provides the only data platform that scales to trillions of files, millions of event streams and petabytes of raw data without compromising performance.
High performance: The MapR Converged Data Platform was designed for high performance with respect to both high throughput and low latency for Apache Hadoop and Apache Spark applications. In addition, the MapR Platform requires significantly fewer servers versus other big data platforms, leading to architectural simplicity and lower capital and operational expenses.

venereology answered 9 months ago

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