DataStage job run time architecture on Hadoop

hadoop-logo In my earlier blog, I explored why enterprises are using Hadoop. In summary, scalable data platforms such as Hadoop offers unparalleled cost benefits and analytical opportunities (including content analytics) to enterprises. In this blog, I will mention some of the enhancements in  IBM‘s InfoSphere Informaiton Server 11.5 that helps leverage the scale and promise of Hadoop.

Data integration in Hadoop:
In this release, Information Server can execute directly inside a Hadoop cluster. This means that all of the data connectivity, transformation, cleansing, enhancement, and data delivery features that thousands of enterprises have relied on for years, can be immediately available to run within the Hadoop platform! Information Server is market leading product  in terms of it’s data integration and governance capability. Now the same product can be used to solve some of the industry’s most complex data challenges inside a Hadoop cluster directly. Imagine the time saved in moving the data back and forth from HDFS!

Even more, these new features for Hadoop use the same simple graphical design environment that IBM clients have previously been accustomed to build integration applications with. In other words, organizations can build new Hadoop-based information intensive applications without the need to retrain their development team on newly emerging languages that require manual hand coding and lack governance support.

How is this accomplished? YARN! 
Apache Hadoop YARN is the framework for job scheduling and cluster resource management. Information Server  can communicate with YARN to run a job on the data nodes on a Hadoop cluster using following steps.

Here is more detail on how Information Server uses YARN

DataStageOnHadoopArchitecture

  1. A job is submitted to run in the Information Server engine.
  2. The ‘Conductor’ (the process responsible for coordinating the job) asks YARN to instantiate the YARN version of the Conductor: The Application Master.
  3. The YARN Client is responsible for starting and stopping Application Masters
  4. Now that the Application Master is ready, ‘Section Leaders’ (responsible for work on a datanode) are prepared
  5. Section Leaders are created and managed by YARN Node Managers.  This is the point where the BigIntegrate/BigQuality binaries will be copied to the Hadoop DataNode if they do not already exist there.
  6. Now the real work can begin – the ‘players’ (that actually run the process) are started.

All of this is automatic and behind the scenes.  The actual user interface will look and feel identical to when a job is run on Windows, AIX, or Linux.

 

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