Who should attend
- Business Intelligence professionals, System administrators and integrators
- Software developers, Solution architects, Business analysts,System integrators
- Those aspiring for a career in Big Data Analytics
What are the Prerequisites for taking this Course?
You can take this Training Course without any specific skills. Prior knowledge of SQL can be helpful.
About the course
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.
About Talend For Hadoop Course
This is a Combo Course of Talend and Hadoop that is specifically designed to meet industry needs. It is an in-depth Course that includes all the features of the Talend Course and the Hadoop Course offering you a clear advantage.
What you will learn in this Training Course?
- Learn about Hadoop fundamentals and architecture
- Advanced concepts of MapReduce and HDFS
- Hadoop ecosystem – Hive, Pig, Scoop, Flume
- Install, maintain, monitor, troubleshoot Hadoop clusters
- Introduction to Talend and Talend Open Studio
- Learn about data integration and concept of propagation
- Deploy XML files and format data functions in Talend
- Use ETL functions to connect Talend with Hadoop
- Import MySQL data using Sqoop and query it using Hive
- Deploy configuration information for use in multiple components
Why should you take this Training Course?
- Global Hadoop Market to Reach $84.6 Billion by 2021 – Allied Market Research
- 845 new companies are using Talend in the last 12 months – HG Data
- Average US Salary for a Talend Professional is $ 110,000 – indeed.com
Hadoop is the preferred framework for Big Data and Talend is an ETL tool that can work with Hadoop to provide you easy business insights without the need for programming. A lot of enterprises around the world are deploying Talend for Hadoop and this Training provides you all the skills needed to work in the data analytics and Business Intelligence domains. Upon successful completion of the Training you can apply for the best jobs in the industry.
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?
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 Line
Map 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 Yarn
Mini 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 Hive
What Is Hive?,Hive Schema and Data Storage,Comparing Hive to Traditional Databases,Hive vs. Pig,Hive Use Cases,Interacting with Hive
B. Relational Data Analysis with Hive
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
C. 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
D. Hive Optimization
Understanding Query Performance,Partitioning,Bucketing,Indexing Data
E. Extending Hive
Topics : User-Defined Functions
F. 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 Pig
What Is Pig?,Pig’s Features,Pig Use Cases,Interacting with Pig
B. 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
C. Processing Complex Data with Pig
Complex/Nested Data Types,Grouping,Iterating Grouped Data,Hands-On Exercise: Analyzing Data with Pig
D. Multi-Data set Operations with Pig
Techniques for Combining Data Sets,Joining Data Sets in Pig,Set Operations,Splitting Data Sets,Hands-On Exercise
E. Extending Pig
Macros and Imports,UDFs,Using Other Languages to Process Data with Pig,Hands-On Exercise: Extending Pig with Streaming and UDFs
F. Pig Jobs
A. Introduction to Impala
What is Impala?,How Impala Differs from Hive and Pig,How Impala Differs from Relational Databases,Limitations and Future Directions Using the Impala Shell
B. 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 Projects
What projects I will be working on this Talend For Hadoop training?
1. Project – Jobs
Problem 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 Connection
2. Hadoop Projects
A. Project – Working with Map Reduce, Hive, Sqoop
Problem 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-system
Problem 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
An experienced Blockchain Professional who has been bringing integrated Blockchain, particularly Hyperledger and Ethereum, and Big Data solutions to the cloud, David Callaghan has previously worked on Hadoop, AWS Cloud, Big Data and Pentaho projects that have had major impact on revenues of marqu...
A Senior Software Architect at NextGen Healthcare who has previously worked with IBM Corporation, Suresh Paritala has worked on Big Data, Data Science, Advanced Analytics, Internet of Things and Azure, along with AI domains like Machine Learning and Deep Learning. He has successfully implemented ...
Videos and materials
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