What is Big Data, Where does Hadoop fit in, Hadoop Distributed File System – Replications, Block Size, Secondary Namenode, High Availability, Understanding YARN – ResourceManager, NodeManager, Difference between 1.x and 2.x
Hadoop 2.x Cluster Architecture , Federation and High Availability, A Typical Production Cluster setup , Hadoop Cluster Modes, Common Hadoop Shell Commands, Hadoop 2.x Configuration Files, Cloudera Single node cluster
How Mapreduce Works, How Reducer works, How Driver works, Combiners, Partitioners, Input Formats, Output Formats, Shuffle and Sort, Mapside Joins, Reduce Side Joins, MRUnit, Distributed Cache
Working with HDFS, Writing WordCount Program, Writing custom partitioner, Mapreduce with Combiner , Map Side Join, Reduce Side Joins, Unit Testing Mapreduce, Running Mapreduce in Local Job Runner Mode
What is Graph, Graph Representation, Breadth first Search Algorithm, Graph Representation of Map Reduce, How to do the Graph Algorithm, Example of Graph Map Reduce,
Exercise 1: Exercise 2:Exercise 3:
A. Introduction to Pig
Understanding Apache Pig, the features, various uses and learning to interact with Pig
B. Deploying Pig for data analysis
The syntax of Pig Latin, the various definitions, data sort and filter, data types, deploying Pig for ETL, data loading, schema viewing, field definitions, functions commonly used.
C. Pig for complex data processing
Various data types including nested and complex, processing data with Pig, grouped data iteration, practical exercise
D. Performing multi-dataset operations
Data set joining, data set splitting, various methods for data set combining, set operations, hands-on exercise
E. Extending Pig
Understanding user defined functions, performing data processing with other languages, imports and macros, using streaming and UDFs to extend Pig, practical exercises
F. Pig Jobs
Working with real data sets involving Walmart and Electronic Arts as case study
A. Hive Introduction
Understanding Hive, traditional database comparison with Hive, Pig and Hive comparison, storing data in Hive and Hive schema, Hive interaction and various use cases of Hive
B. Hive for relational data analysis
Understanding HiveQL, basic syntax, the various tables and databases, data types, data set joining, various built-in functions, deploying Hive queries on scripts, shell and Hue.
C. Data management with Hive
The various databases, creation of databases, data formats in Hive, data modeling, Hive-managed Tables, self-managed Tables, data loading, changing databases and Tables, query simplification with Views, result storing of queries, data access control, managing data with Hive, Hive Metastore and Thrift server.
D. Optimization of Hive
Learning performance of query, data indexing, partitioning and bucketing
E. Extending Hive
Deploying user defined functions for extending Hive
F. Hands on Exercises – working with large data sets and extensive querying
Deploying Hive for huge volumes of data sets and large amounts of querying
G. UDF, query optimization
Working extensively with User Defined Queries, learning how to optimize queries, various methods to do performance tuning.
Selecting a File Format, Tool Support for File Formats, Avro Schemas, Using Avro with Hive and Sqoop, Avro Schema Evolution, Compression
What is Hbase, Where does it fits, What is NOSQL
Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup, Running Map Reduce Jobs on Cluster
Delving Deeper Into The Hadoop API,More Advanced Map Reduce Programming, Joining Data Sets in Map Reduce,Graph Manipulation in Hadoop
1. 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.
2. Project – Hadoop Yarn Project – End to End PoC
Problem Statement – It includes:
Import Movie data,Append the data,How to use sqoop commands to bring the data into the hdfs,End to End flow of transaction data,How to process the real word data or huge amount of data using map reduce program in terms of movie etc.
Intellipaat is the pioneer of Hadoop training. This in-depth Hadoop developer training will help you master complete Hadoop development. You will trained in the domains of HDFS, MapReduce, working with various components of Hadoop like Pig, Hive, Sqoop, YARN and others. This training is in line with clearing the Hadoop component of CCA Spark and Hadoop Developer Certification (CCA175).
Intellipaat offers lifetime access to videos, course materials, 24/7 Support, and course material upgrades to latest version at no extra fees. For Hadoop and Spark training you get the Intellipaat Proprietary Virtual Machine for Lifetime and free cloud access for 6 months for performing training exercises. Hence it is clearly a one-time investment. We are also exclusively partnered with IBM for providing you IBM Certified Hadoop Professional training as well.
This course is designed for clearing the Hadoop component of the Cloudera Spark and Hadoop Developer Certification (CCA175) Exam. The entire training course content is in line with this certification program and helps you clear it with ease and get the best jobs in the top MNCs.
As part of this training you will be working on real time projects and assignments that have immense implications in the real world industry scenario thus helping you fast track your career effortlessly.
At the end of this training program there will be quizzes that perfectly reflect the type of questions asked in the respective certification exams and helps you score better marks in certification exam.
Intellipaat Course Completion Certification will be awarded on the completion of Project work (on expert review) and upon scoring of at least 60% marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.
A senior software architect at NextGen Healthcare who has previously worked with IBM Corporation. Suresh has worked on Big Data, Data Science, advanced analytics, Internet of Things, Azure along with AI domains like Machine Learning and Deep Learning. He has successfully implemented high impact projects in major corporations around the world.
An experienced blockchain professional who has been bringing integrated blockchain particularly Hyperledger and Ethereum and big data solutions to the cloud. David has previously worked on Hadoop, AWS Cloud, Big Data and Pentaho projects that have had major impact on revenues of marquee brands around the world.
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