Interface And Open Database Connectivity

1550 Words7 Pages
Functionality: MapR is a third-party application offering an open, enterprise-grade distribution that makes Hadoop easier to use and more dependable. For ease of use, MapR provides network file system (NFS) and open database connectivity (ODBC) interfaces, a comprehensive management suite, and automatic compression. For dependability, MapR provides high availability with a self-healing no-NameNode architecture, and data protection with snapshots, disaster recovery, and with cross-cluster mirroring. Storage Modeling: MapR has distributed namenode architecture, which removes the single point of failure that plagues HDFS. MapR’s Lockless Storage Services layer results in higher MapReduce throughput than competing distributions. It has ability…show more content…
In a production MapR cluster, some nodes are typically dedicated to cluster coordination and management, and other nodes are tasked with data storage and processing duties. An edge node provides user access to the cluster, concentrating open user privileges on a single host. In smaller clusters, the work is not so specialized and a single node may perform data processing as well as cluster management. Cluster services often change over time, particularly as clusters scale up by adding nodes. Balancing resources to maximize cluster utilization is the goal, and it will require flexibility. Real Time-Low Latency: Unlike other distributions for Hadoop, the MapR architecture is optimized for deployments that depend on high throughput, low latency, high reliability, and no additional administration to ensure production success and significantly lower enterprise data architecture costs. Get faster results on larger data sets to respond more quickly to more complete data. Achieve quicker application responsiveness for an enhanced user experience. Easily load and process high volumes and high velocities of incoming data. Get low 95th and 99th percentile latencies to ensure consistent performance without bottlenecks due to compactions/defragmentation. And get extreme database scalability with millions of columns across billions of rows on one trillion tables. Strategic business: With MapR,
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