Big data hadoop training course that deals with the implementation of various industry use cases is necessary Understand how the hadoop ecosystem works to master Apache Hadoop skills and gain in-depth knowledge of big data ecosystem and hadoop architecture.However, before you enroll for any big data hadoop training course it is necessary to get some basic idea on how the hadoop ecosystem works.Learn about the various hadoop components that constitute the Apache Hadoop architecture in this article.
All the components of the Hadoop ecosystem, as explicit entities are evident. The holistic view of Hadoop architecture gives prominence to Hadoop common, Hadoop YARN, Hadoop Distributed File Systems (HDFS) and Hadoop MapReduce of the Hadoop Ecosystem. Hadoop common provides all Java libraries, utilities, OS level abstraction, necessary Java files and script to run Hadoop, while Hadoop YARN is a framework for job scheduling and cluster resource management. HDFS in Hadoop architecture provides high throughput access to application data and Hadoop MapReduce provides YARN based parallel processing of large data sets.
In our earlier articles, we have defined What Is Apache Hadoop .To recap, Apache Hadoop is a distributed computing open source framework for storing and processing huge unstructured datasets distributed across different clusters. The basic principle of working behind Apache Hadoop is to break up unstructured data and distribute it into many parts for concurrent data analysis. Big data applications using Apache Hadoop continue to run even if any of the individual cluster or server fails owing to the robust and stable nature of Hadoop. More Additional Information At Hadoop Admin Training
Defining Architecture Components of the Big Data Ecosystem
The Hadoop Ecosystem comprises of 4 core components –
1) Hadoop Common-
Apache Foundation has pre-defined set of utilities and libraries that can be used by other modules within the Hadoop ecosystem. For example, if HBase and Hive want to access HDFS they need to make of Java archives (JAR files) that are stored in Hadoop Common.
2) Hadoop Distributed File System (HDFS) -
The default big data storage layer for Apache Hadoop is HDFS. HDFS is the “Secret Sauce” of Apache Hadoop components as users can dump huge datasets into HDFS and the data will sit there nicely until the user wants to leverage it for analysis. HDFS component creates several replicas of the data block to be distributed across different clusters for reliable and quick data access. HDFS comprises of 3 important components-NameNode, DataNode and Secondary NameNode. HDFS operates on a Master-Slave architecture model where the NameNode acts as the master node for keeping a track of the storage cluster and the DataNode acts as a slave node summing up to the various systems within a Hadoop cluster.
HDFS Use Case-
Nokia deals with more than 500 terabytes of unstructured data and close to 100 terabytes of structured data. Nokia uses HDFS for storing all the structured and unstructured data sets as it allows processing of the stored data at a petabyte scale.
MapReduce is a Java-based system created by Google where the actual data from the HDFS store gets processed efficiently. MapReduce breaks down a big data processing job into smaller tasks. MapReduce is responsible for the analysing large datasets in parallel before reducing it to find the results. In the Hadoop ecosystem, Hadoop MapReduce is a framework based on YARN architecture. YARN based Hadoop architecture, supports parallel processing of huge data sets and MapReduce provides the framework for easily writing applications on thousands of nodes, considering fault and failure management.
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The basic principle of operation behind MapReduce is that the “Map” job sends a query for processing to various nodes in a Hadoop cluster and the “Reduce” job collects all the results to output into a single value. Map Task in the Hadoop ecosystem takes input data and splits into independent chunks and output of this task will be the input for Reduce Task. In The same Hadoop ecosystem Reduce task combines Mapped data tuples into smaller set of tuples. Meanwhile, both input and output of tasks are stored in a file system. MapReduce takes care of scheduling jobs, monitoring jobs and re-executes the failed task.
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