Hadoop Training in Hyderabad

Kosmik is one of the best IT training institutes in Hyderabad, Provides Hadoop training in Hyderabad. we providing Online & Classroom training in Hyderabad. We are providing lab facilities with complete real-time training. Training is based on complete advance concepts. So that you can get easily "hands-on experience". We will give 100% job assistance.

Course Content

- - Introduction to Hadoop
  - Hadoop Availability
  - Advantages and disadvantages
  - Scaling
  - Introduction to Big Data
  - What is big data technology?
  - Big data opportunities and challenges
  - Characteristics of big data analytics

Introduction to Hadoop course

- - Hadoop Distributed File System (HDFS)
  - Difference between Hadoop and SQL database
  - Industrial applications of Hadoop
  - Data locality concept
  - Hadoop architecture tutorial
  - Map Reduce and HDFS.
  - Using the Hadoop single node image
  - Hadoop Distributed File System
  - HDFS is designed for streaming data access
  - Data nodes, Name nodes, and Blocks
  - What is Hadoop Federation?
  - Hadoop commands with examples
  - Basic file system operations in Hadoop
  - Anatomy of File Read & write
  - Hadoop custom block placement
  - Configuration settings file extension
  - Difference between f-image and edit log
  - How to add data nodes in Hadoop
  - How to decommission a Data Node dynamically
  - FSCK Utility
  - Overriding log back configurations
  - HDFS Federation
  - Zookeeper force leader election

Map Reduce

- - Functional programming examples
  - Map Reduce explained simply
  - Hadoop Map-Reduce architecture
  - Anatomy of a Map Reduce Job Run
  - Hadoop job status command line
  - Shuffling and Sorting
  - Splits, Partition, Record reader, Types of partitions and Combiner
  - Optimization Techniques Speculative Execution, Slots
  - Types of Counters and Schedules
  - Difference between Old API and New API at code and Architecture Level
  - Getting the data from RDBMS into HDFS using Custom data types
  - Distributed Cache and Hadoop Streaming

YARN

- - Sequential file and map file organization
  - Hadoop compression codec example
  - Map side Join with Distributed Cache
  - Types of Input and Output Formats
  - Handling small files using Combine file Input Format

Map or Reduce Programming – Java Programming

- - Sorting files using Hadoop Configuration API discussion
  - How to use grep command in Hadoop
  - DB input format example
  - Job dependency API discussion questions
  - Input Format & slip API discussion
  - The custom comparator in Hadoop

NoSQL

- - Acid vs base properties
  - Cap theorem example
  - No SQL database list
  - Columnar Databases in Detail
  - Bloom Filters and Compensation

HBase

- - Install HBase on Hadoop cluster
  - HBase basic concepts
  - HBase vs relational database
  - Master and Region Servers
  - HBase Operations through Shell and Programming and HBase overview
  - Catalog Tables
  - Block Cache and sharing
  - Splits
  - DATA Modeling
  - JAVA API and Rest Interface
  - HBASE Counters & filters
  - Large Loading and Coprocessors

Pre-requisites for Hadoop training in Hyderabad

1. 1. To learn Hadoop in any of the Hadoop training in Hyderabad, when we have sound knowledge in Core Java concepts, it must understand the foundations about Hadoop.
  2. Important concepts in Java will be provided by us to get into the Actual concepts of Hadoop training in Hyderabad's.
  3. Foundation of Java is very much important for effective Hadoop training institutes in Hyderabad technologies.
  4. Having a good idea about Pig programming will make Hadoop run easier. Also, Hive can be useful in performing Data warehousing.
  5. Basic knowledge on Unix Commands also needed for day to day execution of the Software.

Hive

- - Installation
  - Introduction to HIVE
  - Hive Services, Hive Shell, Hive Server and Hive Web Interface
  - Meta store
  - OLTP vs OLAP
  - Working with Tables
  - Complex data types and Primitive data types
  - Working with Partitions
  - User Defined Functions
  - Hive bucketing without partition
  - Dynamic Partition
  - Differences between sorts by distribute by and order by
  - Bucketing and Sorted Bucketing with Dynamic partition
  - RC file format
  - Views and indexes
  - Map side joins
  - Options for compressing data stored in the hive
  - Dynamic sub station of Hive and Different ways of running Hive
  - Hive update example
  - Log analysis using Hive
  - Accessing base tables using Hive

Pig

- - Installation
  - Different types of executions
  - Grunt Shell
  - Pig Latin commands
  - Data processing cycle
  - Schema on reading tools
  - MAP Schema, BAG Schema, and Tuple schema
  - Loading and Storing
  - Filtering
  - Grouping and Joining
  - Debugging commands
  - Validations and types of casting in Pig
  - Working with Functions
  - User Defined Functions
  - Splits and Multi query execution
  - Error handling, flatten and order by
  - Parameter Substitution
  - Nested For Each
  - User Defined Functions, Dynamic Invokers, and Macros
  - How to access HBASE use PIG.
  - Pig JSON loader example
  - Piggy Bank

SQOOP

- - Installation
  - Import Data.
  - Incremental Import
  - Free Form Query Import
  - Export data to HBASE, HIVE, and RDBMS

CATALOG

- - Installation.
  - Overview of CATALOG.
  - About Hcatalog with Map Reduce, HIVE and PIG.
  - Hands-on Exercises

FLUME

- - Installation
  - Introduction to Flume
  - Flume Agents like Sources, Channels, and Sinks
  - Concepts of Log User information using Java program into HDFS, HBASE
  - Flame Commands

- More ecosystems: HUE

Oozie

- - Workflow Schedulers, Coordinators, and Bundles.
  - Workflow to show how to schedule Sqoop Job, Hive, PIG, and Map Reduce
  - Zoo Keeper
  - HBASE Integration with HIVE and PIG.
  - Phoenix
  - Proof of concept

SPARK

- - Introduction
  - Linking with Spark
  - Initializing Spark
  - Using the Shell
  - Resilient Distributed Datasets
  - Parallelized Collections
  - External Datasets
  - RDD Operations
  - Basics, Passing Functions to Spark
  - Working with Key-Value Pairs
  - Transformations
  - Actions
  - RDD Persistence
  - Which Storage Level to Choose?
  - Removing Data
  - Shared Variables
  - Broadcast Variables
  - Accumulators
  - Deploying to a Cluster
  - Unit Testing
  - Migrating from pre1.0 Versions of Spark

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