Apache Hadoop - Taking a Big Leap In Big Data
Published on 04 December 14
Sara0
Data has been piling up in organizations since a number of years but since some time, because of the prevailing fervor behind ‘Big Data’ and ‘Business Intelligence’, there is awareness and availability of valued information and accurate storage of data to organizations, which is why they are happily storing their heaps of data and extracting desired information in required format.
One such open source software for distributed processing of large chunks of data is Apache Hadoop, primarily targeted towards reliable and robust computing. It is a framework which uses a simplistic approach to target storage and computation needs ranging from a single server to multiple machines. Apache Hadoop is famously used for research as well as production. It has been widely popular as the standard for storage, processing and analysis of hundreds of terabytes of data. It offers distributed parallel processing across servers, which are normally prevalent in the industry.
An apt solution to capture and reveal the valuable information from the otherwise useless information bulks, Apache Hadoop is the helping hand to enterprises to increase their business efficiency and ROI through instant availability of useful information.
One such open source software for distributed processing of large chunks of data is Apache Hadoop, primarily targeted towards reliable and robust computing. It is a framework which uses a simplistic approach to target storage and computation needs ranging from a single server to multiple machines. Apache Hadoop is famously used for research as well as production. It has been widely popular as the standard for storage, processing and analysis of hundreds of terabytes of data. It offers distributed parallel processing across servers, which are normally prevalent in the industry.
An apt solution to capture and reveal the valuable information from the otherwise useless information bulks, Apache Hadoop is the helping hand to enterprises to increase their business efficiency and ROI through instant availability of useful information.
Modules Handled by Hadoop
• Hadoop Common
A set of common utilities which assist the remaining Hadoop modules and supports the Hadoop subprojects. It includes FileSystem, RPC and serialization libraries.
• Hadoop Distributed File System (HDFS)
It is a distributed file system which gives access to application data and spans across all the nodes in a Hadoop cluster for data storage, to link them into one big file system. It gets reliability by data replication across multiple nodes. It is a Java based file system that gives scalable and reliable data storage.
• Hadoop YARN
A framework utilized for job scheduling and resource management of clusters, its basic motto is to split up the two roles of the JobTracker, namely, resource management and job scheduling into separate areas.
• Hadoop MapReduce
A system for parallel processing of large data sets. It acts as a framework that gets into the work assignment to the nodes in a particular cluster. It is a software framework to write applications processing large amounts of data, easily, on multiple nodes of hardware with utmost reliability and scalability.
Why Hadoop?
With n number of Big Data frameworks available in the industry today, there are certain pointers why Hadoop has been widely accepted and is gaining popularity. Let us have a glance through.
Scalability: Addition of new nodes is much easier without making much changes in the data formats. Hence, the computing solution becomes quite scalable.
Reduced cost of ownership: Since Hadoop brings parallel computing onto commodity servers, there is a remarkable decrease in the cost and hence it becomes affordable to organizations.
Flexibility: Any kind of data can be absorbed, be it structured or unstructured, be it from a single source or multiple. Since there is no schema in Hadoop, data sources can be combined as required to give out necessary output.
Fault tolerance: The framework of Hadoop is built in such a way that whenever you lose a node, the system redirects the assigned work onto another location of the data and the processing does not stop and there is no loss of data.
SPEC INDIA has been involved with a variety of BI and Big Data services and has served a large clientele all around the world. We have been working with Hadoop, MongoDB for Big Data services and with Pentaho, Jaspersoft, Tableau as BI tools. We are global certified partners with Pentaho. As for Hadoop, we provide services like Hadoop Cluster Setup, Sqoop Integration with Hadoop Cluster to Export HDFS Data to MySQL, Analysis of website back links using Apache Hadoop and Map-Reduce. We would glad to serve any of your BI and Big Data requirement.
A set of common utilities which assist the remaining Hadoop modules and supports the Hadoop subprojects. It includes FileSystem, RPC and serialization libraries.
• Hadoop Distributed File System (HDFS)
It is a distributed file system which gives access to application data and spans across all the nodes in a Hadoop cluster for data storage, to link them into one big file system. It gets reliability by data replication across multiple nodes. It is a Java based file system that gives scalable and reliable data storage.
• Hadoop YARN
A framework utilized for job scheduling and resource management of clusters, its basic motto is to split up the two roles of the JobTracker, namely, resource management and job scheduling into separate areas.
• Hadoop MapReduce
A system for parallel processing of large data sets. It acts as a framework that gets into the work assignment to the nodes in a particular cluster. It is a software framework to write applications processing large amounts of data, easily, on multiple nodes of hardware with utmost reliability and scalability.
Why Hadoop?
With n number of Big Data frameworks available in the industry today, there are certain pointers why Hadoop has been widely accepted and is gaining popularity. Let us have a glance through.
Scalability: Addition of new nodes is much easier without making much changes in the data formats. Hence, the computing solution becomes quite scalable.
Reduced cost of ownership: Since Hadoop brings parallel computing onto commodity servers, there is a remarkable decrease in the cost and hence it becomes affordable to organizations.
Flexibility: Any kind of data can be absorbed, be it structured or unstructured, be it from a single source or multiple. Since there is no schema in Hadoop, data sources can be combined as required to give out necessary output.
Fault tolerance: The framework of Hadoop is built in such a way that whenever you lose a node, the system redirects the assigned work onto another location of the data and the processing does not stop and there is no loss of data.
SPEC INDIA has been involved with a variety of BI and Big Data services and has served a large clientele all around the world. We have been working with Hadoop, MongoDB for Big Data services and with Pentaho, Jaspersoft, Tableau as BI tools. We are global certified partners with Pentaho. As for Hadoop, we provide services like Hadoop Cluster Setup, Sqoop Integration with Hadoop Cluster to Export HDFS Data to MySQL, Analysis of website back links using Apache Hadoop and Map-Reduce. We would glad to serve any of your BI and Big Data requirement.
http://blog.spec-india.com/apache-hadoop-taking-a-big-leap-in-big-data
Data has been piling up in organizations since a number of years but since some time, because of the prevailing fervour behind ‘Big Data’ and ‘Business
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