How Big Data Hadoop Works?

1
Hadoop is formally known as Apache Hadoop. It is
an open source framework developed within the
Apache Software Foundation. Hadoops framework is
used for storing data and running applications
on clusters of the commodity. The architecture
of Apache Hadoop framework consists of Hadoop
Distributed File System (HDFS) which is used for
storing data on commodity machines, MapReduce
programming model which is used for processing,
Hadoop Common which is used to store libraries
and utilities for the use of other Hadoop modules
and Hadoop YARN which is a resource management
platform and is used for scheduling users
applications and managing resources in clusters.
Hadoop works on the divide and solves policy as
it divides files into large blocks and disperses
them into nodes of clusters, then packaged codes
are sent to the clusters to process the data in
parallel. This approach ensures the fast and
efficient processing of dataset as compared to
conventional supercomputer architecture. A few
drawbacks of Apache Hadoop are that MapReduce
programming is not a good match for all the
problems, data security issues and does not have
full-featured tools for data management. The
term big data refers to enormous and complex data
sets that are hard to process by traditional
data processing application software. In the
1990's, even one terabyte was considered as big
data and to store it, the data warehouses were
created. Characteristics of Big data are Volume
i.e. the quantity of generated and stored data
Variety i.e. the type and nature of the generated
and stored data Velocity i.e. the speed at
which data is generated and processed and
Veracity i.e. the quality of generated and
stored data. The challenges that are faced while
dealing with big data includes visualization,
data sharing, data search, data transfer,
capturing data, data analysis, data storage,
data updating, data source, querying and
information privacy. Whenever someone is
talking about Big Data management or analytics,
Hadoop is always mentioned as Hadoop is
considered the best way to process a huge amount
of data faster and efficiently. Hadoop puts right
Big Data workloads in systems and optimizes data
structure in an organization. Apache Hadoop is
majorly considered by organizations to process
and manage Big Data because of its
cost-effectiveness, systematic and scalability
architecture. Lately, firms are realizing that
analyzing and categorizing Big Data helps in
making business predictions. Big Data Hadoop
works by using MapReduce programming model of
Apache Hadoop as it is used for processing
different types of data.
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Various Big Data tools that has been built around
Apache Hadoop to extend its basic capabilities
and to increase the efficiency of data analysis
includes Apache ZooKeeper which is a
synchronization, naming registry and
configuration service for distributed systems,
Apache Pig which is a high level platform for
creating programs, Apache HBase which is
distributed database that is paired with Hadoop,
Apache Oozie which is a server-based workflow
scheduling system to manage Hadoop jobs, Apache
Sqoop tool helps in transferring bulk data
between Hadoop and relational databases, Apache
Phoenix is an SQL based parallel processing
database engine which uses HBase as its data
store and Apache Hive which is an SQL on Hadoop
tool that provides data query, data summarization
and data analysis. Learn Big Data Hadoop by
taking Big Data Hadoop Training in Delhi from
Madrid Software Training Solutions.