Killer Combo: Hadoop HDFS and MR

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Menace in novel Hadoop norm

Data is spanned over many hardware and software hubs. In Hadoop environmental setup, the chances of network complications are more. It is vital important to understand good deed and practical value in expanding business horizon. Establishing distributed filesystems become more conglomerate and laborious than normal disk filesystems.
Hadoop Distributed File System (HDFS) contain few to several thousands of server nodes. Big data set is chopped and distributed evenly across each node filesystem reserved areas. Each hardware machine has more probability of fault and failure over a period of time. Handling computer hardware and software components are complicated. Driver failure, node and network failure, disk failure occurs now and then. HDFS architectural design should able to automatically recovery from such failover and gracefully lead forward.
Processing data in distributed cluster environment always need insight deeper understanding to maximize technical knowledge base opening up the new doors for better and quick services. At the target of speeding up data transfer rate, some network associated problems were faced. Some are like hard to split and aggregation end result, synchronization and co-ordination problem, timing issues, deadlock, bounded bandwidth. Some other common struggle are resource sharing and effective way of CPU resource utilization across system, allowing correlated concurrent access and modification without consequence, maintaining transparency to project collective machines as single whole image, making layers of abstraction to hide the complexity and functional details, reliable service supporting anytime anywhere access to data, ensuring portability across different heterogeneous operating system and hardware, achieving high throughput available service, scaling up and down with data and load with requisite, provisioning fault tolerance, redundant and recovery services at all time. To address most of these disrupt, many Apache Hadoop subprojects were incorporated.