Big Data - Its Components And Supporting Technologies Part 3 Of 5
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Big Data   -   Its Components And Supporting Technologies Part 3 Of 5

Oozie

Hadoop ecosystem often require recurring and conditional job execution. Oozie is Hadoop’s job scheduler. It is a workflow letting users define a series of actions in the form of jobs, each of these jobs itself could be writen in any of languages such as Pig, Hive, Java MapReduce. It supports these features

  • Conditional job execution
  • Trigger/event based job execution
  • Recurring execution of job chain based on date and time

Oozie component are:

  • Workflows specify a sequence of jobs to execute as on logical unit of work
  • Coordinators specify the schedule, dependence and trigger criteria of a Workflow job
  • Bundle are the packages of coordinator and workflow jobs

Ambari

Ambari is a system management utility for deploying, administering and monitoring clusters. It has a simple to use Web interface including:

  • Wizard for installing and configuring Hadoop clusters.
  • Allows for central management of Hadoop services across entire cluster.
  • Dashboard for monitoring Hadoop Clusters

Mahout

Mahout is the component which provides the business insights from the deluge of machine data stored in HDFS, it helps find meaningful patterns in the Big Data. Mahout consists of a library of machine-learning algorithms to facilitate data mining and statistical modeling using MapReduce paradigm.

Sqoop

Sqoop is Hadoop’s data load utility. It allows for efficiently transferring data between Hadoop and traditional datastores like relational databases leveraging parallel capabilities of Hadoop clusters. Sqoop uses a command-line interface which is capable of diverse data load needs

Incremental data load

Transfer entire databases, a subset of tables or a single table at a time

Transfer results of a single SQL query

Transfer data between HDFS, HBase, RDBMS, Hive, and all other traditional databases like Teradata, Netezza

HCatalog

HCatalog provides the semantic layer for Hadoop ecosystem. HCatalog is implemented as a table and provide data abstraction leading to the processing flexibility. It presents unified metadata of data to other components, like Hive and Pig. As a result of this Hadoop components can process the data without having to worry about actual format and physical location of the data on the grid. HCatalog also notifies the components when ever new data is introduced to the grid.

ZooKeeper

Zookeeper provides critical service to bind Hadoop’s distributed processing environment. Zookeeper is responsible for maintaining cluster level common objects like cluster configuration and name space. This service makes Hadoop distributed processing possible for the applications. This service buffers the application code from changes to the cluster specific details so that the same code can run across different nodes of the cluster without having to adjust to every cluster level change.


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