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course - Hadoop


Starting a new batch for Hadoop

  • June 2019
  • 2 hours
  • 50 seats

Hadoop Syllabus

Course Content for Hadoop and Spark Batch
Introduction to BIGDATA and HADOOP
 What is Big Data?
 What is Hadoop?
 Relation between Big Data and Hadoop.
 What is the need of going ahead with Hadoop?
 Scenarios to apt Hadoop Technology in REAL TIME Projects
 Challenges with Big Data
o Storage
o Processing
 How Hadoop is addressing Big Data Changes
 Comparison with Other Technologies
o Data Warehouse
o TeraData
 Different Components of Hadoop Echo System
o Storage Components
o Processing Components
 Importance of Hadoop Echo System Components
 Other solutions of Big Data
o Introduction to NO SQL
HDFS (Hadoop Distributed File System)
 What is a Cluster Environment?
 Cluster Vs Hadoop Cluster.
 Significance of HDFS in Hadoop
 Features of HDFS
 Storage aspects of HDFS
o Block
o How to Configure block size
o Default Vs Configurable Block size
o Why HDFS Block size so large?
o Design Principles of Block Size
HDFS Architecture - 5 Daemons of Hadoop
 NameNode and its functionality
 DataNode and its functionality
 JobTracker and its functionality
 TaskTrack and its functionality
 Secondary Name Node and its functionality.
Replication in Hadoop – Fail Over Mechanism
 Data Storage in Data Nodes
 Fail Over Mechanism in Hadoop – Replication
 Replication Configuration
 Custom Replication
 Design Constraints with Replication Factor
 Can we change the replication factor in Hadoop?
 Can we change the block size for a file or directory in Hadoop?
Accessing HDFS
 CLI (Command Line Interface) and HDFS Commands
 Java Based Approach
 Hadoop Archives
 Configuration files in Hadoop Installation and the Purpose
 How to & Where to Configure Hadoop Daemons in a Hadoop Cluster?
 Difference between Hadoop 1.X.X and Hadoop 2.X.X version
o Name Node HA (High Availability in Hadoop 2.X.X)
 Why Map Reduce is essential in Hadoop?
 Processing Daemons of Hadoop
 Job Tracker
o Roles Of Job Tracker
o Drawbacks w.r.to Job Tracker failure in Hadoop Cluster
o How to configure Job Tracker in Hadoop Cluster
 Task Tracker
o Roles of Task Tracker
o Drawbacks w.r.to Task Tracker Failure in Hadoop Cluster
Input Split
 InputSplit
 Need Of Input Split in Map Reduce
 InputSplit Size
 InputSplit Size Vs Block Size
 InputSplit Vs Mappers
Map Reduce Life Cycle
 Communication Mechanism of Job Tracker & Task Tracker
 Input Format Class
 Record Reader Class
 Success Case Scenarios
 Failure Case Scenarios
 Retry Mechanism in Map Reduce
MapReduce Programming Model
 Different phases of Map Reduce Algorithm
 Different Data types in Map Reduce
o Primitive Data types Vs Map Reduce Data types
How to write a basic Map Reduce Program
 Driver Code
 Mapper Code
 Reducer Code
Driver Code
 Importance of Driver Code in a Map Reduce program
 How to Identify the Driver Code in Map Reduce program
 Different sections of Driver code
Mapper Code
 Importance of Mapper Phase in Map Reduce
 How to Write a Mapper Class?
 Methods in Mapper Class
Reducer Code
 Importance of Reduce phase in Map Reduce
 How to Write Reducer Class?
 Methods in Reducer Class
Input Format’s in Map Reduce
 TextInputFormat
 KeyValueTextInputFormat
 NLineInputFormat
 DBInputFormat
 SequenceFileInputFormat.
 How to use the specific input format in Map Reduce
 How to write Custom Input Format Class and Custom Record Reader
Output Format’s in Map Reduce
 TextOutputFormat
 KeyValueTextOutputFormat
 NLineOutputFormat
 DBOutputFormat
 SequenceFileOutputFormat.
 How to use the specific Output format in Map Reduce
 How to write Custom Output Format Class and Custom Record Writer
Map Reduce API(Application Programming Interface)
o New API
o Deprecated API
 Combiner in Map Reduce
o Is combiner mandate in Map Reduce
o How to use the combiner class in Map Reduce
o Performance tradeoffs w.r.to Combiner
o Real Time Use Cases
o Where to Use & Where Not to Use Combiner
 Partitioner in Map Reduce
o Importance of Practitioner class in Map Reduce
o How to use the Partitioner class in Map Reduce
o Different types of Practitioners in Map Reducer
o Importance of hashPartitioner
o How to write a custom Practitioner
o Real Time Use Cases
 Compression Techniques in Map Reduce
o Importance of Compression in Map Reduce
o What is CODEC
o Compression Types
o GzipCodec
o BzipCodec
o LZOCodec
o SnappuCodec
o Configurations w.r.to Compression Techinques
o How to customize the Compression per one job Vs all the job.
 Map Reduce Job Chaining
o What is Map Reduce Job Chaining?
o Use of MR Chaining in Real Time Hadoop Projects
o Real Time Use case
o Performance trade off’s using MR Chaining
 Joins - in Map Reduce
o Map Side Join
o Reduce Side Join
o Performance Trade Off
o Distributed cache
 How to debug MapReduce Jobs in Local and Pseudo cluster Mode.
o Introduction to MapReduce Streaming
o Data locality in Map Reduce
o Secondary Sorting Using Map Reduce
Apache PIG
 Introduction to Apache Pig
 Map Reduce Vs Apache Pig
 SQL Vs Apache Pig
 Different datat ypes in Pig
 Where to Use Map Reduce and PIG in REAL Time Hadoop Projects
 Modes Of Execution in Pig
o Local Mode
o Map Reduce OR Distributed Mode
 Execution Mechanism
o Grunt Shell
o Script
o Embedded
 Transformations in Pig
 How to write a simple pig script
 Parameter substitution in PIG Scripts
 How to develop the Complex Pig Script
 Bags , Tuples and fields in PIG
 UDFs in Pig
o Need of using UDFs in PIG
o How to use UDFs
o REGISTER Key word in PIG
 Techniques to improve the performance and efficiency of Pig Latin
 Hive Introduction
 Need of Apache HIVE in Hadoop
 When to choose PIG & HIVE in REAL Time Project
 Hive Architecture
o Driver
o Compiler
o Executor(Semantic Analyzer)
 Meta Store in Hive
o Importance Of Hive Meta Store
o Embedded metastore configuration
o External metastore configuration
o Communication mechanism with Metastore
 Hive Integration with Hadoop
 Hive Query Language(Hive QL)
 Configuring Hive with MySQL MetaStore
 SQL VS Hive QL
 Data Slicing Mechanisms
o Partitions In Hive
o Buckets In Hive
o Partitioning Vs Bucketing
o Real Time Use Cases
 Collection Data Types in HIVE
o Array
o Struct
o Map
o Real Time Use Cases
 User Defined Functions(UDFs) in HIVE
o UDFs
o Need of UDFs in HIVE
 Hive Serializer/Deserializer - SerDe
 Semi Structured Data Processing Using Hive
 HIVE – HBASE Integration
 Introduction to Sqoop.
 MySQL client and Server Installation
 How to connect to Relational Database using Sqoop
 Different Sqoop Commands
o Different flavors of Imports
o Export
o Hive-Imports
 Hbase introduction
 HDFS Vs Hbase
 Hbase Vs RDBMS
 Hbase Vs NO SQL
 Hbase usecases
 Hbase Data modeling Elements
o Column families
o Column Qualifier Name
o Row Key
 Hbase Architecture
 Clients
o Thrift
o Java Based
o Avro
 Map Reduce Integration
 Map Reduce over Hbase
 Hbase Admin
o Schema Definition
o Basic CRUD Operations
o Client Side Buffering in Hbase
 Flume Introduction
 Flume Architecture
 Flume Master , Flume Collector and Flume Agent
 Flume Configurations
 Real Time Use Case using Apache Flume
 Oozie Introduction
 Oozie Architectrure
 Oozie Configuration Files
 Oozie Job Submission
o Workflow.xml
o Coordinator.xml
o job.coordinator.properties
o Transit parameters in workflow.xml
YARN (Yet another Resource Negotiator) – Next Gen. MapReduce
 What is YARN?
 Difference between Map Reduce & YARN
 YARN Architecture
o Resource Manager
o Application Master
o Node Manager
 When should we go ahead with YARN
 YARN Process flow
 Different Configuration Files for YARN
 Examples on YARN
 What is Impala?
 How can we use Impala for Query Processing?
 When should we go ahead with Impala
 HIVE Vs Impala
 REAL TIME Use Cases with Impala
MongoDB ( As part of NoSQL Databases )
 Need of NoSQL Databases
 Relational VS Non-Relational Databases
 Introduction to MongoDB
 Features of MongoDB
 Installation of MongoDB
 Mongo DB Basic operations
 REAL Time Use Cases on Hadoop & MongoDB Use Cases
Apache Cassandra
 Introduction to Cassandra
 Mongo DB Vs Cassandra
 Basic Operation using Cassandra
Apache Kafka (A Distributed Message Queuing System)
 Introduction to Kafka
 Installation of Kafka
 Difference between MQ Vs Kafka
 Basic Operation using Kafka
Mahout (As a part of BIGDATA ANALYTICS)
 Introduction to Machine Learning (ML) Languages
 Types of Machine Learning
 Introduction to Apache MAHOUT
 Categories of Mahout Algorithms
Real Time Use case using Classifier Algorithm of Mahout
– Naives Bayes
SCALA (Object Oriented and Functional Programming)
 Getting started With Scala.
 Scala Background, Scala Vs Java and Basics.
 Interactive Scala – REPL, data types, variables,expressions, simple
 Running the program with Scala Compiler.
 Explore the type lattice and use type inference
 Define Methodsand Pattern Matching.
Scala Environment Set up.
 Scala set up on Windows.
 Scala set up on UNIX.
Functional Programming.
 What is Functional Programming.
 Differences between OOPS and FPP.
Collections (Very Important for Spark)
 Iterating, mapping, filtering and counting
 Regular expressions and matching with them.
 Maps, Sets, group By, Options, flatten, flat Map
 Word count, IO operations,file access, flatMap
Object Oriented Programming.
 Classes and Properties.
 Objects, Packaging and Imports.
 Traits.
 Objects, classes, inheritance, Lists with multiple related types, apply
 What is SBT?
 Integration of Scala in Eclipse IDE.
 Integration of SBT with Eclipse.
 Batch versus real-time data processing
 Introduction to Spark, Spark versus Hadoop
 Architecture of Spark.
 Coding Spark jobs in Scala
 Exploring the Spark shell -> Creating Spark Context.
 RDD Programming
 Operations on RDD.
 Transformations
 Actions
 Loading Data and Saving Data.
 Key Value Pair RDD.
 Broadcast variables.
 Configuring and running the Spark cluster.
 Exploring to Multi Node Spark Cluster.
 Cluster management
 Submitting Spark jobs and running in the cluster mode.
 Developing Spark applications in Eclipse
 Tuning and Debugging Spark.
 Learning Cassandra
 Getting started with architecture
 Installing Cassandra.
 Communicating with Cassandra.
 Creating a database.
 Create a table
 Inserting Data
 Modelling Data.
 Creating an Application with Web.
 Updating and Deleting Data.
 Introduction to Spark and Cassandra Connectors.
 Spark With Cassandra -> Set up.
 Creating Spark Context to connect the Cassandra.
 Creating Spark RDD on the Cassandra Data base.
 Performing Transformation and Actions on the Cassandra RDD.
 Running Spark Application in Eclipse to access the data in the Cassandra.
 Introduction to Amazon Web Services.
 Building 4 Node Spark Multi Node Cluster in Amazon Web Services.
 Deploying in Production with Mesos and YARN.
 Introduction of Spark Streaming.
 Architecture of Spark Streaming
 Processing Distributed Log Files in Real Time
 Discretized streams RDD.
 Applying Transformations and Actions on Streaming Data
 Integration with Flume and Kafka.
 Integration with Cassandra
 Monitoring streaming jobs.
 Introduction to Apache Spark SQL
 The SQL context
 Importing and saving data
 Processing the Text files,JSON and Parquet Files
 DataFrames
 user-defined functions
 Using Hive
 Local Hive Metastore server
 Introduction to Machine Learning
Types of Machine Learning.
 Introduction to Apache Spark MLLib Algorithms.
 Machine Learning Data Types and working with MLLib.
 Regression and Classification Algorithms.
 Decision Trees in depth.
 Classification with SVM, Naive Bayes
 Clustering with K-Means
 Building the Spark server
What we are offering as part of this Course?
 3 REAL TIME Hadoop Projects End-to-End Explanation with architecture.
 Mock Interviews will be conducted on a one-to-one basis after the
course duration.
 Hard Copy & Soft Copy Materials for all the Components.
 Detailed Assistance in RESUME Preparation on a one-to-one basis with
Real Time Projects based on your technical back ground.
 All the Real time interview questions and answers will be provided.
 Discussing the new happenings in Hadoop
 Discussing the Interview Questions on a daily basis
 Discussing Certification (CCA 175 – Spark and Hadoop Certification)
Related topics on a daily basis.
 Proof Of Concept using complex architectures to give a real time idea