Hadoop is an open-source processing framework for distributed storage systems specific to managing data processing and data storage for big data applications. Part of the Hadoop ecosystem of technologies is its HDFS. The Hadoop Distributed File System (HDFS) is a scalable data storage system designed to provide high-performance data access within the Hadoop ecosystem
The main benefit of Hadoop is to efficiently store and process large data sets with significant advantages, listed here.
HDFS is specifically designed to scale data storage across multiple physical devices organized into clusters. At the heart of HDFS is MapReduce, a tool that ingests data, breaks it down into separate blocks, and then distributes them accordingly to different nodes in various clusters. HDFS uses a primary NameNode, which tracks where those blocks are placed.
The nodes where these blocks are stored, called DataNodes, are built of inexpensive commodity hardware organized into a cluster. Each cluster usually is a single node. As well, blocks are also replicated across multiple nodes to improve redundancy and resilience using parallel processing.
The conjoined inner workings of the NameNode and DataNodes enables dynamic adaptation in server capacity. In real time, Hadoop can grow or shrink the number of nodes based on demand. This also creates a “self-healing” property within the system—as the NameNode discovers DataNode faults, it can assign tasks to other DataNodes with duplicate blocks.
The ability to split data amongst multiple nodes, and to replicate that data grants several features over traditional data centers.
As with new technology, issues arise when similar terminology is used with seemingly similar technologies. HDFS vs. Cloud Object Storage is one of those cases, but HDFS and Cloud Object Storage are not necessarily comparable.
HDFS is a distributed file system for storing large data, within a file hierarchy. Object Storage, like Amazon’s S3, is a strategy for storing data as objects, and managing those objects with referent metadata, typically to store voluminous unstructured data. While they both deal with storing data in different storage architectures, the functionality of Hadoop makes it far more complex.
Another significant difference is that HDFS follows the premise of moving compute power to where data is, such as the DataNode, where processing can occur in order to reduce network congestion instead of sending data away to be computed. Computation is strictly not included in object storage, which only deals with storing data as objects.
Hadoop is a powerful platform for collecting, processing, analyzing and management storage and data. For scenarios involving big data sets, Hadoop has many applications.
Hadoop applications are those designed to work within the Hadoop ecosystem, using the various services to solve problems using big data. Within the Hadoop ecosystem are four main components:
To support these main components, several other components have also been included in the Hadoop ecosystem.
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