Hadoop

Hadoop online training

Hadoop online training in Hyderabad

Course Name : Hadoop Online Training

Faculty: Certified Big Data Hadoop trainer

way2onlinetraining offers Hadoop online training for students, developers, data analysts and administrators. Big Data Hadoop course designed in the format that meets your convenience and flexibility. Hadoop online training will help you to become certified Big Data Hadoop professional.

Highlights of Online Hadoop Training:

In-depth Big Data Hadoop curriculum: covers HDFS, MapReduce, HBase, MR(Pig), MR(Hive), Apache Drill and Hadoop Ecosystem.
Free 24x7x365 access: Take Big Data Hadoop online training course anytime, from anywhere in the world. Enrich your skills.
Certified as Big Data Hadoop expert: Apply your newly acquired skill into practice. Add value to your present and future employers.

Hadoop online Training course content:

Hadoop Online Training Course Prerequisites:

  • advanced unix commands
  • core java concepts (Exceptions, OOPS Concepts, Collections)
  • SQL Query basics

Hardware and Software of computer:

  • linux distributions list (Ex: RedHat Linux/Fedora/Cent OS/Ubuntu)
  • java 1.6.0
  • open SSH server windows
  • mysql community server
  • Eclipse IDE Definition
  • VMWare (Use widows OS with linux OS)

 

Online Hadoop Training Course Duration:

  • 45 days.

 

Hadoop Online training Course Content:

  • Overview of hadoop
  • course Availability of Hadoop
  • Benifis & contests
  • Scaling

 

Introduction to Big Data and Hadoop:

  • What is big data concept
  • Big Data opportunities & challenges
  • Architecture of big data

Introduction to Apache Hadoop:

  • Distributed file system supported by hadoop(HDFS)
  • Top hadoop companies
  • Data Locality concept
  • Hadoop Architecture overview
  • Map Reduce & HDFS
  • Using the Hadoop single node image
  • difference between Hadoop & SQL

 

Hadoop Distributed File System Commands (HDFS):

  • Basic hadoop concepts
  • what does namenode periodically expects from datanodes
  • hadoop namenode high availability
  • types of file system in operating system
  • anatomy of file write in hadoop
  • hadoop custom block placement
  • what is a configuration file
  • what is fsimage in hadoop
  • Different ways to add a datanode to a hadoop cluster
  • hadoop remove datanode (Cluster is running)
  • how to run fsck manually
  • How to override default configuration at system level and Programming level (10/03/2017)
  • HDFS Federation
  • Zookeeper leader election time
  • Benefits of HDFS

 

Mapreduce in Hadoop:

  • Functional programming concepts
  • map reduce explained simply
  • how map reduce works in hadoop
  • Mapreduce shuffle phase
  • Legacy systems examples ->Status Updates, Task Execution, Task Assignment, Job Submission,, Progress
  • and Job Initialization
  • Job completion and failures in MapReduce
  • Mapreduce shuffle algorithm
  • Partition, Record reader, Splits, Types of partitions & Combiner
  • Different types of optimization techniques -> No. Slots,JVM Reuse and Speculative Execution
  • Types of Counters and Schedulers
  • Difference between microservices and api
  • Moving data from RDBMS to hadoop
  • hadoop streaming multiple input files (R, Ruby and Python )
  • YARN
  • serial file organization
  • hadoop compression codec example
  • distributed cache in mapreduce example
  • Types of I/O Formats and Multiple outputs
  • combine file input format example

Mapreduce Programming Model – Java Programming:

  • Hands on “Word Count” in Map/Reduce in Pseudo distribution Mode and standalone
  • Sorting files using Hadoop Configuration API discussion
  • Emulating “grep” for searching file in Hadoop
  • mapreduce dbinputformat example
  • Job Dependency on API discussion
  • API writing format
  • input splits in mapreduce
  • hadoop writable comparable example

NOSQL Database:

  • acid vs base propertiesL
  • cap theorem nosql
  • nosql database lis
  • columnar database list
  • Bloom TTL Filters and Compensation

 

HBase in Hadoop:

  • Download Hbase
  • Hbase data model design
  • difference between rdbms and nosql database
  • Region server Hbaseshell command to insert data into an hbase table
  • Catalog Tables in HBase
  • Block Cache and sharding
  • HBase SPLITS.
  • Data modeling examples(Promoted, Salted, lRandom Keys and Sequentia)
  • Java API for beginners
  • Hbase counters example.
  • Enabling HBASE RAW Scans and Replication
  • HBASE Filter
  • Bulk Loading and Coprocessors (programs along with Endpoints and Observer)
  • Hadoop use cases examples

Hive Commands:

  • Download hive
  • definitions of architecture by famous architects
  • hive user interface
  • hive metastore architecture
  • Hive QL(10/03/2017)
  • OLTP vs OLAP
  • hive create external table example
  • Primitive data types and complex data types
  • open source partition recovery
  • user defined function in hive example
  • Bucketing in hive example.
  • dynamic partition hive distribute by hive example
  • static and dynamic partition in hive
  • RC File.
  • VIEWS and INDEXES .
  • MAPSIDE JOINS.
  • difference between the migration hive tables andhive tables
  • Dynamic substation of Hive and various ways of running Hive
  • hive update table example
  • log analysis using hive
  • hive hbase integration steps

Pig Hadoop:

  • Installation
  • Types of execution
  • Grunt Shell
  • Pig Latin
  • processing data in pig
  • Schema on read
  • types of primitives
  • MAP Schema,BAG Schema and Tuple schema
  • Loading and Storing
  • Filtering
  • Grouping & Joining
  • Debugging commands (Illustrate and Explain).
  • PIG validations
  • PIG Type casting
  • Working Functions
  • User Defined Functions in PIG
  • Types of JOINS in pig and Replicated Join in detail.
  • Multiquery and SPLITS execution.
  • FLATTEN and ORDER for Error Handling.
  • Parameter Substitution in pig
  • Nested For Each.
  • Dynamic Invokers, Macros and User Defined Functions
  • read hbase table using pig
  • using PIG Load and Write JSON DATA
  • Piggy Bank
  • Hands on Exercises

SQOOP Commands:

  • SQOOP Installation
  • Import Data in SQOOP
  • Incremental Import(storing Password in Metastore, Last Imported data,Import only New data, Sharing

Metastore between Sqoop Clients):

  • Free Form Query Import
  • Export data to HBASE,HIVE and RDBMS
  • Hands on Exercises.

 

HCATALOG:

  • hadoop hcatalog installation
  • overview of HCATALOG.
  • pig load hive table
  • Hands on Exercises.

 

FLUME:

  • Hadoop Flume Installation
  • Architecture of flume
  • Flume Agents: Sources, Channels Sinks and Sinks Channels
  • Flume log4j appender sample
  • Using Java program Log User information in to HDFS using Tail Source
  • Using Java program Log User information in to HBASE using LOG4J and Avro Source
  • Using Java program Log User information in to HBASE using Tail Source
  • Apache flume use cases

More Ecosystems:

  • (Hortonworks and Cloudera).

 

Oozie Hadoop:

  • Workflow (Kill, Fork, Action, End, Action, Join and Start), Bundles, Coordinators and Schedulers.
  • oozie scheduler example
  • Real world Use case that find the top websites used by users of certain ages and will be scheduled to.
  • Run for every two hour.
  • Oozie Zoo Keeper
  • HBASE Integration with HIVE and PIG.
  • Phoenix
  • proof of concept(POC) in a sentence

 

SPARK:

  • Introduction to SPARK
  • Spark repartition
  • Spark Initializing
  • Using the Shell in SPARK
  • Resilient Distributed Datasets
  • SPARK Parallelized Collections
  • Data set example
  • RDD Operations
  • Passing, Basics Functions to Spark
  • Key-Value Pairs for Working
  • Transformations
  • Actions
  • RDD Persistence
  • Which Storage Level to Choose?
  • Clear Data
  • Accumulators
  • Broadcast Variables
  • Shared Variables
  • Unit Testing
  • Deploying to a Cluster
  • Spark 1.5.2 documentation
  • Where to Go from Here

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