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Lesson 1:  Getting started with Talend
o    Working of Talend,Introduction to Talend Open Studio and its Usability
o    What is Meta Data?    

Lesson 2: Jobs
o    Creating a new Job,Concept and creation of Delimited file,Using Meta Data and its Significance
o    What is propagation?
o    Data integration schema,Creating Jobs using t-filter row and string filter
o    Input delimation file creation.

Lesson 3: Overview of Schema and Aggregation
o    Job design and its features,What is a T map?
o    Data Aggregation,Introduction to triplicate and its Working
o    Significance and working of tlog,T map and its properties.     Lesson 4: Connectivity with Data Source
o    Extracting data from the source,Source and Target in Database (MySQL)
o    Creating a connection
o    Importing Schema or Metadata.

Lesson 5: Getting started with Routines/Functions
o    Calling and using Functions,What are Routines?
o    Use of XML file in Talend,Working of Format data functions
o    What is type casting?    

Lesson 6: Data Transformation
o    Defining Context variable
o    Learning Parameterization in ETL,Writing an example using trow generator
o    Define and Implement Sorting
o    What is Aggregator?
o    Using t flow for publishing data
o    Running Job in a loop.

Lesson 7: Connectivity with Hadoop
o    Learn to start Trish Server,Connectivity of ETL tool connect with Hadoop
o    Define ETL method
o    Implementation of Hive,Data Import into Hive with an example,An example of Partitioning in hive
o    Reason behind no customer table overwriting?,Component of ETL,Hive vs. Pig,Data Loading using demo customer
o    ETL Tool,Parallel Data Execution.     

Lesson 8: Introduction to Hadoop and its Ecosystem, Map Reduce and HDFS
o    Big Data, Factors constituting Big Data,Hadoop and Hadoop Ecosystem,Map Reduce -Concepts of Map, Reduce, Ordering, Concurrency, Shuffle, Reducing, Concurrency 
o    Hadoop Distributed File System (HDFS) Concepts and its Importance,Deep Dive in Map Reduce – Execution Framework, Partitioner Combiner
o    Data Types, Key pairs,HDFS Deep Dive – Architecture, Data Replication, Name Node, Data Node, Data Flow
o    Parallel Copying with DISTCP, Hadoop Archives.

Lesson 9: Hands on Exercises
o    Installing Hadoop in Pseudo Distributed Mode
o    Understanding Important configuration files
o     their Properties and Demon Threads,Accessing HDFS from Command Line
o    Map Reduce – Basic Exercises,Understanding Hadoop Eco-system,Introduction to Sqoop
o    use cases and Installation,Introduction to Hive
o    use cases and Installation,Introduction to Pig
o    use cases and Installation,Introduction to Oozie
o    use cases and Installation
o    Introduction to Flume, use cases and Installation
o    Introduction to Yarn
o    Mini Project – Importing Mysql Data using Sqoop and Querying it using Hive.    

Lesson 10: Deep Dive in Map Reduce
o    How to develop Map Reduce Application
o    writing unit test,Best Practices for developing and writing
o    Debugging Map Reduce applications
o    Joining Data sets in Map Reduce.

Lesson 11:  Hive
o    Introduction to Hive
o    What Is Hive?,Hive Schema and Data Storage,Comparing Hive to Traditional Databases,Hive vs. Pig,Hive Use Cases,Interacting with Hive
o    Relational Data Analysis with Hive
o    Hive 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 Hue
o     Hive Data Management:Hive 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 Hive
o    Hive Optimization: Understanding Query Performance,Partitioning,Bucketing,Indexing Data
o    Extending Hive: Topics : User-Defined Functions
o    Hands on Exercises – Playing with huge data and Querying extensively.
o    User defined Functions, Optimizing Queries, Tips and Tricks for performance tuning.     

Lesson 12:  Pig
o    Introduction to Pig:What Is Pig?,Pig’s Features,Pig Use Cases,Interacting with Pig
o    Basic Data Analysis with Pig: Pig 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 Processing
o    Processing Complex Data with Pig: Complex/Nested Data Types,Grouping,Iterating Grouped Data,Hands-On Exercise: Analyzing Data with Pig
o    Multi-Data set Operations with Pig: Techniques for Combining Data Sets,Joining Data Sets in Pig,Set Operations,Splitting Data Sets,Hands-On Exercise
o    Extending Pig: Macros and Imports,UDFs,Using Other Languages to Process Data with Pig,Hands-On Exercise: Extending Pig with Streaming and UDFs.

Lesson 13: Impala
o    A. Introduction to Impala
o    What is Impala?
o    How Impala Differs from Hive and Pig,How Impala Differs from Relational Databases,Limitations and Future Directions Using the Impala Shell
o    Choosing the best (Hive, Pig, Impala)
o    Major Project – Putting it all together and Connecting Dots:Putting it all together and Connecting Dots,Working with Large data sets
o    Steps involved in analyzing large data.    

Lesson 14: ETL Connectivity with Hadoop Ecosystem, Job and Certification Support
o    How ETL tools work in big data Industry,Connecting to HDFS from ETL tool and moving data from Local system to HDFS
o    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.
o    Major Project, Hadoop Development
o    cloudera Certification Tips and Guidance and Mock Interview Preparation
o    Practical Development Tips and Techniques
o    certification preparation.