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Bigdata Hadoop Course Content:

Hadoop Basic Concepts
  • What is Hadoop?
  • The Hadoop Distributed File System
  • How Hadoop Map Reduce Works
  • Anatomy of a Hadoop Cluster
  • Setting up Hadoop Cluster
  • Make a fully distributed Hadoop cluster
  • Cluster Specification
  • Network Topology
  • Cluster Specification and installation
  • Hadoop configuration
  • Hadoop Daemons
    Master Daemons
  • Name node
  • Job Tracker
  • Secondary name node
  • Slave Daemons
  • Data Node
  • Task tracker
  • Writing Map Reduce Program
  • Examining a Sample MapReduce Program With several examples
  • Basic API Concepts
  • The Driver Code
  • The Mapper
  • The Reducer
  • The configure and close Methods
  • Sequence Files
  • Record Reader
  • Record Writer
  • Role of Reporter
  • Output Collector
  • Processing XML files
  • Counters Directly Accessing HDFS
  • ToolRunner
  • Using The Distributed Cache
  • Common Map Reduce Alogorithms
  • Sorting, Searching and Indexing
  • Word Co-Occurrence Word Co-Occurrence
  • Identity Mapper
  • Identity Reducer
  • Exploring well known problems using MapReduce applications
  • HDFS(Hadoop Distributed File System)
  • Blocks and Splits
  • Input Splits
  • HDFS Splits
  • Methods of accessing HDFS
  • JAVA Approach
  • CLI Approach
  • Cluster architecture and block placement
  • Data Replication
  • Hadoop Rack Awareness
  • High data availability
  • Data Integrity
  • Programming Practices
  • Developing MapReduce Programs in
  • Local Mode Running without HDFS and Mapreduce
  • Pseudo-distributed Mode
  • Running all daemons in a single node
  • Fully distributed mode
  • Running daemons on dedicated nodesApps
  • Debugging Map Reduce Program
  • Testing with MRUnit
  • Logging
  • Other Debugging Strategies
  • Advanced Map Reduce Program
  • A Recap of the MapReduce Flow
  • The Secondary Sort
  • Customized Input Formats and Output Formats
  • Introduction to YARN
  • What is YARN?
  • Why YARN?
  • Advantages of YARN
  • YARN Daemons
  • Resource Manager
  • Node Manager
  • Application Master
  • Classic Mapreduce vs YARN
  • Anatomy of a YARN application run
  • Scheduling in YARN
  • Fair Scheduler
  • Capacity Scheduler
  • YARN as a platform for multiple applications
  • Supported YARNapplications
  • Overview of Spark
  • What is Spark?
  • Hadoop & Spark
  • Features of Spark
  • Spark Ecosystems
  • Spark Streaming
  • Spark SQL
  • Spark MLib
  • Spark Architecture
  • Resilient Distributed Datasets
  • How to Install Spark
  • How to Run Spark
  • How to Interact with
  • Spark Spark Web Console
  • Shared Variables
  • Spark Applications
  • Word Count Application
  • HIVE
  • Hive concepts
  • Hive architecture
  • Create database, access it from java client
  • Buckets
  • Partition
  • Joins in hive
  • Inner joins
  • Outer Joins
  • Hive UDF
  • Impala
  • Introducing Cloudera Impala
  • Impala Benefits
  • How Cloudera Impala Works with CDH
  • Primary Impala Features
  • Impala Concepts and Architecture
  • Components of the Impala Server
  • The Impala Daemon
  • The Impala Statestore
  • The Impala Catalog Service
  • Overview of the Impala SQL Dialect
  • How Impala Fits Into the Hadoop Ecosystem
  • How Impala Works with Hive
  • Overview of Impala Metadata and the Metastore
  • How Impala Uses HDFS
  • FLUME
  • Flume concepts
  • Create a sample application to capture logs from Apache using flume
  • SQOOP
  • Getting Sqoop
  • A Sample Import
  • Database Imports
  • Controlling the import
  • Imports and consistency
  • Direct-mode imports
  • Performing an Export
  • Overview of services in Android
  • Implementing a Service
  • Service lifecycle
  • Bound versus unbound services
  • PIG
  • Pig basics
  • PIG Vs MapReduce and SQL
  • Pig Vs Hive
  • Pig Vs Hive
  • Write sample Pig Latin scripts
  • Modes of running PIG
  • Running in Grunt shell
  • PIG UDFs
  • Pig Macros
  • CDH Enhancement
  • Name Node High – Availability
  • Name Node federation
  • Fencing
  • Interview Preparation
  • Personal Interview
  • Group Discussion
  • Our Courses

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