4 Things to Sniff MapR Opportunity !

Mon, May 23 2016 | Author: PT. Central Data Technology

1. Born to be hero of processing an avalanche of big data

The web was generating more and more information on a daily basis, and it was becoming very difficult to index over one billion pages of content. In order to cope, Google invented a new style of data processing known as MapReduce. A year after Google published a white paper describing the MapReduce framework, Doug Cutting and Mike Cafarella, inspired by the white paper, created Hadoop to apply these concepts to an open-source software framework to support distribution for the Nutch search engine project. Given the original case, Hadoop was designed with a simple write-once storage infrastructure.


2. Offloading Data warehouse

The MapR Data Warehouse Optimization and Analytics Quick Start Solution provides the following critical capabilities that organizations require:

A data management platform that helps store large volumes of data at a lower cost than alternatives. Improved responsiveness of the data warehouse by performing ETL transformations on MapR. The ability to store, process and analyze new types of data such as clickstream, social, mobile and machine data. The ability to restore data warehouse CPU and storage capacity.

You have the flexibility to use Hadoop with your data warehouse to reduce overall system cost by performing transformations on Hadoop and freeing up previously used storage and capacity.  In addition, you can add more types and sources of data into the MapR Distribution for more granular and richer analytics across the combined Hadoop and data warehouse solution.


3. Increase  Performance SAP Business Object

MapR and SAP have partnered to provide you with fast, real-time analytics on a unified enterprise architecture. SAP and MapR are delivering the first real-time platform for structured, unstructured, and streaming data that allows you to analyze larger windows of data for more accurate insights. The optimized SAP-MapR data platform provides tiered analytics for instant access with massive scalability.


4. When Streaming Becomes Strategic

The old system of record and data warehouse arrangement was founded on the idea that data lived in specific locations, and where there was a need, it was replicated to other locations. However, with the explosion of data in a variety of formats and the acceleration of data flows, it's become apparent that a streaming architecture that focuses equally on data flow and data storage is necessary.

The MapR Converged Data Platform was built with the idea of data movement in mind, with a real-time data transport capability embedded in the data platform. With the addition of MapR Streams, MapR becomes a truly global data platform able to support any type of distributed workload, ranging from the bulk processing of Hadoop applications (MapReduce, Hive, HBase, etc.) to real-time stream processing using Spark, Storm, or any other data streaming capability.