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Learn Data Loading Techniques using Sqoop and Flume

This web-based training course on Learn Data Loading Techniques using Sqoop and Flume functionality, administration and development, is available online to all individuals, institutions, corporates and enterprises in India (New Delhi NCR, Bangalore, Chennai, Kolkatta), US, UK, Canada, Australia, Singapore, United Arab Emirates (UAE), China and South Africa. No matter where you are located, you can enroll for any training with us - because all our training sessions are delivered online by live instructors using interactive, intensive learning methods.


For big data manipulations, Apache Hadoop is probably the single most-preferred solution owing to its abilities of being scalable and also cost-effective. For loading the astronomical amounts of data that Hadoop manipulates and works with, different types of tools are used for the loading of such data. Sqoop and Flume are two such popular tools which are used for data loading into Hadoop. Flume is inherently used for loading huge amounts of streaming data on the HDFS framework. The tool can be used for various different purposes such as collecting logs from web servers and then integrating the different data for data analysis and manipulation. With Flume, big data analysts gain the advantage of a number of failover and recovery mechanisms along with the ability to fine-tune the reliability structure of Fluke. Apache Sqoop on the other hand is another amazing tool which can be used for transferring large quantities of data from databases such as Oracle and MySQL to Hadoop’s HDFS or Hive and even transfers data from HDFS to the databases. Sqoop is structured on the ‘command line interpreter’ methodology through which each command is executed one after the other through the interpreter.


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Course Details

Through this Data Loading Techniques using Sqoop and Flume online training course, the basic knowledge and information of both Flume and Sqoop as data transfer tools for Hadoop is provided in detail. Initially, Apache Flume and its architecture are explored in detail and the working of its three main events; the source, the sink and the channel is elaborated upon. In Apache Sqoop, its command line interpreter structure is explained along with the methods of reading metadata and creating class definitions as needed by the input. The collaboration of MapReduce function with Apache Sqoop will also be explained in detail in this Apache Sqoop and Fluke Big Data Hadoop online training course. The course is structured to provide not just the theoretical knowledge of the subject but also the applicative knowledge and understanding for effective data transferring and loading into HDFS. This Sqoop and Fluke Big Data online course has no prerequisites but it is advised that the trainees have basic understanding of database management systems and preferably Big Data too.


Introduction to big data loading in Hadoop

  • Overview to the various possible tools of data loading
  • Why Flume and Sqoop?
  • Introduction to Flume
  • The client
  • The event
  • Flow of event through various components
  • Sources
  • Listening or consuming events
  • Forwarding events to one or more channels
  • Channels
  • In memory queues
  • Disk-backed queues
  • Recovery options
  • Event replay options
  • Sinks
  • Writing to HDFS or Hbase
  • Remote procedure calls

Other external repositories

  • Agent
  • Interceptors
  • Channel selector
  • Sink processor
  • Introduction to Sqoop (SQL to Hadoop)
  • Understanding the command line interpreter
  • Sqoop Import and Export
  • Sqoop connectors

Connectivity to new external systems

  • Data transfer between Sqoop and external storage
  • Supporting plugins
  • Sqoop Workflow
  • Partitioning of data
  • Transferring parts of data
  • Using data in a type safe manner
  • Sqoop integration with Oozie
  • Sqoop integration with MapReduce

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