One of the biggest open source platforms, Apache Hadoop, is used for the distributed processing and storage of massive data sets. These data sets are found on computer clusters that are built with commodity hardware. The different types of services offered by Hadoop include data processing, storage, access, governance, operations and security.
The main parts of Apache Hadoop is the storage section, which is also called the Hadoop Distributed File System or HDFS and the MapReduce, which is the processing model. What Hadoop does is basically split massive blocks of data and distribute them among different nodes present inside a cluster. The packaged code is then transferred into nodes which also process this data in parallel. By taking advantage of data locality, where nodes can be manipulated, Hadoop processes datasets more efficiently than an otherwise conventional supercomputer architecture.
Hadoop started out after a Google File System paper was published back in 2003. This led to another research paper titled MapReduce: Simplified Data Processing on Large Clusters. It showed the possibility of reducing large datasets to make them easier to access. Hadoop 0.1.0 released back in April ’06 and was named after one of the founder’s toy elephants.
There are different modules for Hadoop that are used for various purposes. These include the likes of:
- Hadoop Common – This contains all the utilities and libraries which are required by other Hadoop modules.
- HDFS – The Hadoop Distributed File System is a file system which stores and distributes data on commodity machines, thus providing a high aggregate for bandwidth across the cluster.
- Hadoop YARN – This is a resource management system which manages and computes resources in different clusters and uses it to schedule user applications.
- Hadoop MapReduce – This is a programming model that can be used for any large-scale processing of data.
The modules in Hadoop are all designed with the assumption that any hardware failures are common. They should thus be handled automatically taken care of within the scope of the software in the system. The Hadoop MapReduce and HDFS parts were derived originally from Google’s MapReduce and the GFS papers.
What are the key benefits?
There are multiple benefits to using Apache Hadoop owing to its ease of use and scalability. Other benefits include:
- Scalability: By distributing data that is local to each node, Hadoop can be used to manage, process, store and analyse data at even a petabyte scale.
- Flexibility: There isn’t a need for structured schemas with Hadoop. This is unlike any other relational database system, and the data can be stored in different formats
- Low cost – Hadoop is an open source software and thus runs with hardware that is inexpensive as well.
- Reliability – There are large computing clusters which are also prone to failure. Hadoop is generally resilient fundamentally. When a certain node fails to process, it is then redirected to the other nodes in the cluster. The data is then replicated to prepare for any other node failures in the future.
There are other applications which run using the Apache Hadoop framework platform as well. These include:
- Ambari – A tool that helps in monitoring, managing and provisioning any Apache Hadoop clusters. This also includes support for HDFS, MapReduce, HCatalog, HBase, Hive and more!
- Avro – This is a type of system that does data serialisation.
- Cassandra – This is a multi-master database that doesn’t have one single point of failure.
- Chukwa – This can be used to manage bigger distributed systems.
- Pig – Pig is a type of data-flow language written in high-level code with an execution framework used for parallel computing.
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