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Talend For Hadoop Training

Course Summary

Our Talend Hadoop certification master program lets you gain proficiency in Hadoop data integration for high speed processing. You will work on real world projects in Talend ETL, Talend Open Studio, Hadoop MapReduce, HDFS, deploying XML files, formatting data functions.

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    Course Syllabus

    Talend For Hadoop Course Content

    Getting started with Talend
    Working of Talend,Introduction to Talend Open Studio and its Usability,What is Meta Data?
    Creating a new Job,Concept and creation of Delimited file,Using Meta Data and its Significance,What is propagation?,Data integration schema,Creating Jobs using t-filter row and string filter,Input delimation file creation
    Overview of Schema and Aggregation
    Job design and its features,What is a T map?,Data Aggregation,Introduction to triplicate and its Working,Significance and working of tlog,T map and its properties
    Connectivity with Data Source
    Extracting data from the source,Source and Target in Database (MySQL),Creating a connection, Importing Schema or Metadata
    Getting started with Routines/Functions
    Calling and using Functions,What are Routines?,Use of XML file in Talend,Working of Format data functions,What is type casting?
    Data Transformation
    Defining Context variable,Learning Parameterization in ETL,Writing an example using trow generator,Define and Implement Sorting,What is Aggregator?,Using t flow for publishing data,Running Job in a loop
    Connectivity with Hadoop
    Learn to start Trish Server,Connectivity of ETL tool connect with Hadoop,Define ETL method,Implementation of Hive,Data Import into Hive with an example,An example of Partitioning in hive,Reason behind no customer table overwriting?,Component of ETL,Hive vs. Pig,Data Loading using demo customer,ETL Tool,Parallel Data Execution
    Introduction to Hadoop and its Ecosystem, Map Reduce and HDFS
    Big Data, Factors constituting Big Data,Hadoop and Hadoop Ecosystem,Map Reduce -Concepts of Map, Reduce, Ordering, Concurrency, Shuffle, Reducing, Concurrency ,Hadoop Distributed File System (HDFS) Concepts and its Importance,Deep Dive in Map Reduce – Execution Framework, Partitioner Combiner, Data Types, Key pairs,HDFS Deep Dive – Architecture, Data Replication, Name Node, Data Node, Data Flow, Parallel Copying with DISTCP, Hadoop Archives
    Hands on Exercises
    Installing Hadoop in Pseudo Distributed Mode, Understanding Important configuration files, their Properties and Demon Threads,Accessing HDFS from Command LineMap Reduce – Basic Exercises,Understanding Hadoop Eco-system,Introduction to Sqoop, use cases and Installation,Introduction to Hive, use cases and Installation,Introduction to Pig, use cases and Installation,Introduction to Oozie, use cases and Installation,Introduction to Flume, use cases and Installation,Introduction to YarnMini Project – Importing Mysql Data using Sqoop and Querying it using Hive
    Deep Dive in Map Reduce
    How to develop Map Reduce Application, writing unit test,Best Practices for developing and writing, Debugging Map Reduce applications,Joining Data sets in Map Reduce
    A. Introduction to HiveWhat Is Hive?,Hive Schema and Data Storage,Comparing Hive to Traditional Databases,Hive vs. Pig,Hive Use Cases,Interacting with HiveB. Relational Data Analysis with HiveHive Databases and Tables,Basic HiveQL Syntax,Data Types ,Joining Data Sets,Common Built-in Functions,Hands-On Exercise: Running Hive Queries on the Shell, Scripts, and HueC. Hive Data ManagementHive Data Formats,Creating Databases and Hive-Managed Tables,Loading Data into Hive,Altering Databases and Tables,Self-Managed Tables,Simplifying Queries with Views,Storing Query Results,Controlling Access to Data,Hands-On Exercise: Data Management with HiveD. Hive OptimizationUnderstanding Query Performance,Partitioning,Bucketing,Indexing DataE. Extending HiveTopics : User-Defined FunctionsF. Hands on Exercises – Playing with huge data and Querying extensively.G. User defined Functions, Optimizing Queries, Tips and Tricks for performance tuning
    A. Introduction to PigWhat Is Pig?,Pig’s Features,Pig Use Cases,Interacting with PigB. Basic Data Analysis with PigPig Latin Syntax, Loading Data,Simple Data Types,Field Definitions,Data Output,Viewing the Schema,Filtering and Sorting Data,Commonly-Used Functions,Hands-On
    Exercise: Using Pig for ETL ProcessingC. Processing Complex Data with PigComplex/Nested Data Types,Grouping,Iterating Grouped Data,Hands-On Exercise: Analyzing Data with PigD. Multi-Data set Operations with PigTechniques for Combining Data Sets,Joining Data Sets in Pig,Set Operations,Splitting Data Sets,Hands-On ExerciseE. Extending PigMacros and Imports,UDFs,Using Other Languages to Process Data with Pig,Hands-On Exercise: Extending Pig with Streaming and UDFsF. Pig Jobs
    A. Introduction to ImpalaWhat is Impala?,How Impala Differs from Hive and Pig,How Impala Differs from Relational Databases,Limitations and Future Directions Using the Impala ShellB. Choosing the best (Hive, Pig, Impala)
    Major Project – Putting it all together and Connecting Dots
    Putting it all together and Connecting Dots,Working with Large data sets, Steps involved in analyzing large data
    ETL Connectivity with Hadoop Ecosystem
    How ETL tools work in big data Industry,Connecting to HDFS from ETL tool and moving data from Local system to HDFS,Moving Data from DBMS to HDFS,Working with Hive with ETL Tool,Creating Map Reduce job in ETL tool,End to End ETL PoC showing Hadoop integration with ETL tool.
    Job and Certification Support
    Major Project, Hadoop Development, cloudera Certification Tips and Guidance and Mock Interview Preparation, Practical Development Tips and Techniques, certification preparation
    Talend For Hadoop Project
    Project Work1. Project – JobsProblem Statement – It describes that how to create a job using metadata. For this it includes following actions:Create XML File,Create Delimited File,Create Excel File,Create Database Connection2. Hadoop ProjectsA. Project – Working with Map Reduce, Hive, SqoopProblem Statement – It describes that how to import mysql data using sqoop and querying it using hive and also describes that how to run the word count mapreduce job.B. Project – Connecting Pentaho with Hadoop Eco-systemProblem Statement – It includes:Quick Overview of ETL and BI,Configuring Pentaho to work with Hadoop Distribution,Loading data into Hadoop cluster,Transforming data into Hadoop cluster
    Extracting data from Hadoop Cluster

Course Fee:
USD 300

Course Type:


Course Status:



1 - 4 hours / week

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