Hadoop is an open-source architecture parallel file system. Hadoop’s contributors work for some of the world’s biggest technology companies. That diverse, motivated community has produced a genuinely innovative platform for consolidating, combining and understanding large-scale data in order to better comprehend the data deluge.
Technically, Hadoop consists of two key services: reliable data storage using the Hadoop Distributed File System (HDFS) and high-performance parallel data processing.
The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware. HDFS provides high throughput access to application data and is suitable for applications that have large data sets. HDFS relaxes a few POSIX requirements to enable streaming access to file system data.
Ensure Availability
Hardware failure is the norm rather than the exception. An HDFS instance may consist of hundreds or thousands of server machines, each storing part of the file system’s data. The fact that there are a huge number of components and that each component has a non-trivial probability of failure means that some component of HDFS is always non-functional. Therefore, detection of faults and quick, automatic recovery from them is a core architectural goal of HDFS.
Cluster Rebalancing
The HDFS architecture is compatible with data rebalancing schemes. A scheme might automatically move data from one DataNode to another if the free space on a DataNode falls below a certain threshold. In the event of a sudden high demand for a particular file, a scheme might dynamically create additional replicas and rebalance other data in the cluster.
Alethe Consulting is an active contributor to the HDFS projects & provides implementation & support for Hadoop. We provide end to end support for bug fixes and important new features from the public development repository and apply all this to a stable version of the source code. We manage Hadoop environment for sustenance of implemented solution for different organizations.