Hadoop originated conceptually from the Google File System paper that was published in October 2003. This paper spawned another research paper from Google – MapReduce: Simplified Data Processing on Large Clusters. Hadoop today helps process avalanches of big data with the help of the MapReduce framework. It is the go-to framework for large-scale, data-intensive deployments.
Today’s organizations are looking for big data platforms that allow them to harness their data in a scalable, flexible and reliable manner while meeting the requirements of their software developers. Hadoop , as a single platform for the computation, analytics and storage of very large unstructured and structured datasets provides a fast and economic way to leverage the massive amounts of data produced, including that by new sources such as industrial sensors and Internet of Things (IoT).
MapR, the company whose name is a homage to the Google white paper on the MapReduce framework, provides the industry’s only integrated data platform that inculcates the processing power of the top-ranked Hadoop with web-scale enterprise storage and real-time database capabilities, enabling customers to access the enormous potential of their data.
MapR built their Hadoop distribution data platform from the scratch with business-centric production applications in mind. MapR’s platform has integrated Apache Hadoop with architectural innovations focused on operational excellence in the data centre allowing customers to do more than just organize their data. The MapR Distribution for Hadoop integrates numerous open source packages such as Apache Mahout, Spark, Hive, Pig and ZooKeeper, with MapR innovations to provide a unique Hadoop platform to tend to enterprise customer applications and uses. The MapR platform ensures its customers high availability, disaster recovery, security, and full data protection. It also allows Hadoop to be easily accessed as a traditional network attached storage (NAS) with read-write features. Engineered for 24X7 zero data loss operations and immediate data recovery from site and node collapses ,the platform is ideal for data centre needs and IT operations.
MapR is famous as the top-ranked Hadoop, NoSQL and SQL-on-Hadoop solution and as the only integrated data platform, including Hadoop and Spark that supports a broad set of mission-critical and real-time production uses cases. Their customers, across industries, use MapR for a multitude of applications and benefit from increased revenue, reduced costs, and risk mitigation.The built-in MapR high availability (HA) feature eliminates single points of failure at the node, file system metadata, NFS access, resource management (YARN), and job tracking levels. Rolling Upgrades let upgrades live clusters, one node at a time to minimize planned downtime. MapR’s disaster recovery (DR) features helps customers develop a true business continuity strategy to overcome a site-wide disaster. MapR Mirroring creates a consistent remote replica or “mirror” for disaster recovery, as well as for load balancing and geographic distribution. Scheduled mirroring sends only block-level differentials to minimize both synchronization time and bandwidth utilization, and defines an appropriate recovery point objective (RPO) as per requirements.
MapR performs over 100 billion ad auctions a day, studies 96% of the U.S internet traffic and analyses over a trillion dollars in retail purchases. MapR is used by Samsung, Beats Music, HP and Cisco across different industries like financial services, retail, media, healthcare, manufacturing, telecommunications, government organizations as well as Fortune 100 and Web2.0 companies. MapR’s unique architecture enables it to scale to handle continuous data feeds and manage more files than competing technologies. Their platform can also combine database operations and transactions that enable businesses to make rapid decisions based on the data collected and immediate, real-time analytics.
MapR continues to innovate their platform to enable multiple computing and resource management applications on top of the same data to emerge as the choice platform for big data management and analytics.
