big data hadoop online training


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RK SOFT is one of the fastest growing big data and hadoop training institute in Hyderabad offering big data and hadoop online training, online big data hadoop training, online big data training, online hadoop training, online hadoop big data training, hadoop training, big data training, online big data and hadoop training, big data hadoop online training, big data online training from Hyderabad, Hadoop online training from Hyderabad, big data and hadoop online training from Hyderabad, big data training and placement assiatance, big data job assistance training, big data job assurance training, 100 % placement assistance training on big data, big data hands on training, big data project oriented training, hadoop hands on training, big data and hadoop online training from India, best big data online training from India, Best big data training from Hyderabad, Best hadoop online training from Hyderabad, job guarantee training, Apache hadoop training, hortonworks hadoop online training, hadoop administration training, big data admin training, data science online training, pentaho big data online training, pentaho hadoop online training, pentaho & bigdata training, pentaho & hadoop training, data scientist course, big data training course, hadoop training course, big data, hadoop, HIVE online training, Map Reduce, Data Visualization, Data analysis, introduction to Data science, introduction to big data, introduction to hadoop, Data Wrangling, big data and mapreduce, basics of mapreduce, basics of big data, basics of hadoop, Mapper, Reducer, big data tutorials, hadoop tutorials, big data and hadoop tutorials, hadoop training course content, big data and hadoop training course content, hadoop training course content, big data course curriculum, hadoop course curriculum, big data and hadoop course curriculum, big data course outline, hadoop course outline, hadoop, hadoop admin training, hadoop installation and configuration, hadoop jobs, Big data resumes, big data CV, big data projects, big data hands on exercises, hadoop hands on exercises, big data and hadoop hands on exercises, HDFS commands, hadoop architecture, hadoop interview questions, big data interview questions, What is YARN?
Big data and hadoop training and certification
big data and hadoop developer certification training
big data and hadoop administration training
big data and hadoop developer and admin training
Big data and hadoop architect training
big data and hadoop modeling online training
big data and hadoop testing
Data science
What is big data?
Why big data
what is general data
Difference between transaction data and big data
what is DWH
dif between DWH and big data
big data market analysis
big data and its sources
characteristics og big data and hadoop
limitations of big data
implement big data with your any tool
big data support
hadoop support
hadoop & big data features
hadoop eco system
what is HDFS?
why you are using HDFS?
Diff between File system and HDFS?
name node
NN operation
data block
replication factor
replication technique
Course code:Bigdata Hadoop
Duration:40 Hour
Total hours of lesson:40
Accreditation:big data and hadoop job assistance training, big data online training, hadoop online training
Requirements:The ideal students for this class are prepared individuals who have: Strong interest in big data and hadoop Strong interest in big data Strong interest in hadoop Strong interest in data base Strong interest in software job
Internship:• To identify, analyze and validate the right “business questions” that decision makers need answered to support tactical and strategic decision making • To specify clear, concise, unambiguous business intelligence information requirements that enables the design of effective big data solutions • To understand key enablers for delivering big data business intelligence • To map requirements to source data and to map and transform source data to enable effective, consistent high-quality solutions • To express the requirements in a clear, concise, unambiguous format that enables developers to design appropriate technical solutions to support big data requirements • Special considerations for social media monitoring, data mining, and meta data analytics • To integrate big data into your business processes and decision processes
Students per class:12

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Course program

big data hadoop online training
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 Big-Data and Hadoop Developer Certification Training
 19 August 2016  Online training and class room
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Hadoop Training
Big data training
Join Big Data Hadoop training course today and extend your area of knowledge to big data and hadoop world also become hadoop Certified Professional.
Big Data Analytics, Big Data, and Data Science Courses will make you SME on advance concepts of Hadoop 2.7 like HIVE, PIG, Hbase, Zookeeper, SPARK & Sqoop.
Enroll Big data and hadoop online courses today, from anywhere in the world. Refresh your knowledge, whenever or wherever you want.

RKSOFT designed high quality and In-depth online big data and Hadoop course content which covers HDFS, MapReduce, HBase, Hive and Apache Drill.
After completion of Big Data course, you will become a 3 to 4 years experiance bigdata consultant.

Hadoop Training Course Duration
50 Hours, daily 1:30 Hours

Hadoop Training Course Content

Introduction to Hadoop

High Availability
Advantages and Challenges
Introduction to Big Data
What is Big data
Big Data opportunities
Big Data Challenges
Characteristics of Big data
Introduction to Hadoop
Hadoop Distributed File System
Comparing Hadoop & SQL.
Industries using Hadoop.
Data Locality.
Hadoop Architecture.
Map Reduce & HDFS.
Using the Hadoop single node image (Clone).
The Hadoop Distributed File System (HDFS)
HDFS Design & Concepts
Blocks, Name nodes and Data nodes
HDFS High-Availability and HDFS Federation.
Hadoop DFS The Command-Line Interface
Basic File System Operations
Anatomy of File Read
Anatomy of File Write
Block Placement Policy and Modes
More detailed explanation about Configuration files.
Metadata, FS image, Edit log, Secondary Name Node and Safe Mode.
How to add New Data Node dynamically.
How to decommission a Data Node dynamically (Without stopping cluster).
FSCK Utility. (Block report).
How to override default configuration at system level and Programming level.
HDFS Federation.
ZOOKEEPER Leader Election Algorithm.
Exercise and small use case on HDFS.
Map Reduce
Functional Programming Basics.
Map and Reduce Basics
How Map Reduce Works
Anatomy of a Map Reduce Job Run
Legacy Architecture ->Job Submission, Job Initialization, Task Assignment, Task Execution, Progress and Status Updates
Job Completion, Failures
Shuffling and Sorting
Splits, Record reader, Partition, Types of partitions & Combiner
Optimization Techniques -> Speculative Execution, JVM Reuse and No. Slots.
Types of Schedulers and Counters.
Comparisons between Old and New API at code and Architecture Level.
Getting the data from RDBMS into HDFS using Custom data types.
Distributed Cache and Hadoop Streaming (Python, Ruby and R).
Sequential Files and Map Files.
Enabling Compression Codec’s.
Map side Join with distributed Cache.
Types of I/O Formats: Multiple outputs, NLINEinputformat.
Handling small files using CombineFileInputFormat.
Map/Reduce Programming – Java Programming
Hands on “Word Count” in Map/Reduce in standalone and Pseudo distribution Mode.
Sorting files using Hadoop Configuration API discussion
Emulating “grep” for searching inside a file in Hadoop
DBInput Format
Job Dependency API discussion
Input Format API discussion
Input Split API discussion
Custom Data type creation in Hadoop.
CAP Theorem and Types of Consistency.
Types of NoSQL Databases in detail.
Columnar Databases in Detail (HBASE and CASSANDRA).
TTL, Bloom Filters and Compensation.
HBase Installation
HBase concepts
HBase Data Model and Comparison between RDBMS and NOSQL.
Master & Region Servers.
HBase Operations (DDL and DML) through Shell and Programming and HBase Architecture.
Catalog Tables.
Block Cache and sharding.
DATA Modeling (Sequential, Salted, Promoted and Random Keys).
JAVA API’s and Rest Interface.
Client Side Buffering and Process 1 million records using Client side Buffering.
HBASE Counters.
Enabling Replication and HBASE RAW Scans.
HBASE Filters.
Bulk Loading and Coprocessors (Endpoints and Observers with programs).
Real world use case consisting of HDFS,MR and HBASE.
Introduction and Architecture.
Hive Services, Hive Shell, Hive Server and Hive Web Interface (HWI)
Meta store
Hive QL
Working with Tables.
Primitive data types and complex data types.
Working with Partitions.
User Defined Functions
Hive Bucketed Tables and Sampling.
External partitioned tables, Map the data to the partition in the table, Writing the output of one query to another table, Multiple inserts
Dynamic Partition
Differences between ORDER BY, DISTRIBUTE BY and SORT BY.
Bucketing and Sorted Bucketing with Dynamic partition.
RC File.
Compression on hive tables and Migrating Hive tables.
Dynamic substation of Hive and Different ways of running Hive
How to enable Update in HIVE.
Log Analysis on Hive.
Access HBASE tables using Hive.
Hands on Exercises
Execution Types
Grunt Shell
Pig Latin
Data Processing
Schema on read
Primitive data types and complex data types.
Tuple schema, BAG Schema and MAP Schema.
Loading and Storing
Grouping & Joining
Debugging commands (Illustrate and Explain).
Validations in PIG.
Type casting in PIG.
Working with Functions
User Defined Functions
Types of JOINS in pig and Replicated Join in detail.
SPLITS and Multiquery execution.
Error Handling, FLATTEN and ORDER BY.
Parameter Substitution.
Nested For Each.
User Defined Functions, Dynamic Invokers and Macros.
How to access HBASE using PIG.
How to Load and Write JSON DATA using PIG.
Piggy Bank.
Hands on Exercises
Import Data.(Full table, Only Subset, Target Directory, protecting Password, file format other than CSV,Compressing,Control Parallelism, All tables Import)
Incremental Import(Import only New data, Last Imported data, storing Password in Metastore, Sharing Metastore between Sqoop Clients)
Free Form Query Import
Export data to RDBMS,HIVE and HBASE
Hands on Exercises.
Introduction to HCATALOG.
About Hcatalog with PIG,HIVE and MR.
Hands on Exercises.
Introduction to Flume
Flume Agents: Sources, Channels and Sinks
Log User information using Java program in to HDFS using LOG4J and Avro Source
Log User information using Java program in to HDFS using Tail Source
Log User information using Java program in to HBASE using LOG4J and Avro Source
Log User information using Java program in to HBASE using Tail Source
Flume Commands
Use case of Flume: Flume the data from twitter in to HDFS and HBASE. Do some analysis using HIVE and PIG
More Ecosystems
HUE.(Hortonworks and Cloudera).
Workflow (Action, Start, Action, End, Kill, Join and Fork), Schedulers, Coordinators and Bundles.
Workflow to show how to schedule Sqoop Job, Hive, MR and PIG.
Real world Use case which will find the top websites used by users of certain ages and will be scheduled to run for every one hour.
Zoo Keeper
HBASE Integration with HIVE and PIG.
Proof of concept (POC).
Linking with Spark
Initializing Spark
Using the Shell
Resilient Distributed Datasets (RDDs)
Parallelized Collections
External Datasets
RDD Operations
Basics, Passing Functions to Spark
Working with Key-Value Pairs
RDD Persistence
Which Storage Level to Choose?
Removing Data
Shared Variables
Broadcast Variables
Deploying to a Cluster
Unit Testing
Migrating from pre-1.0 Versions of Spark

Big data and Hadoop online training:

Big data hadoop course outline

Introduction to Big Data and Hadoop
What is Big Data?
What are the challenges for processing big data?
What technologies support big data?
3V’s of BigData and Growing.
What is Hadoop?
Why Hadoop and its Use cases
History of Hadoop
Different Ecosystems of Hadoop.
Advantages and Disadvantages of Hadoop
Real Life Use Cases
HDFS (Hadoop Distributed File System)
HDFS architecture
Features of HDFS
Where does it fit and Where doesn't fit?
HDFS daemons and its functionalities
Name Node and its functionality
Data Node and its functionality
Secondary Name Node and its functionality
Data Storage in HDFS
Introduction about Blocks
Data replication
Accessing HDFS
CLI(Command Line Interface) and admin commands
Java Based Approach
Hadoop Administration
Hadoop Configuration Files
Configuring Hadoop Domains
Precedence of Hadoop Configuration
Diving into Hadoop Configuration
Cluster Administration Utilities
Rebalancing HDFS DATA
Copy Large amount of data from HDFS
FSImage and Edit.log file theoretically and practically.
Map Reduce architecture
JobTracker , TaskTracker and its functionality
Job execution flow
Configuring development environment using Eclipse
Map Reduce Programming Model
How to write a basic Map Reduce jobs
Running the Map Reduce jobs in local mode and distributed mode
Different Data types in Map Reduce
How to use Input Formatters and Output Formatters in Map Reduce Jobs
Input formatters and its associated Record Readers with examples
Text Input Formatter
Key Value Text Input Formatter
Sequence File Input Formatter
How to write custom Input Formatters and its Record Readers
Output formatters and its associated Record Writers with examples
Text Output Formatter
Sequence File Output Formatter
How to write custom Output Formatters and its Record Writers
How to write Combiners, Partitioners and use of these
Importance of Distributed Cache
Importance Counters and how to use Counters
Advance MapReduce Programming
Joins - Map Side and Reduce Side
Use of Secondary Sorting
Importance of Writable and Writable Comparable Api's
How to write Map Reduce Keys and Values
Use of Compression techniques
Snappy, LZO and Zip
How to debug Map Reduce Jobs in Local and Pseudo Mode.
Introduction to Map Reduce Streaming and Pipes with examples
Job Submission
Job Initialization
Task Assignment
Task Execution
Progress and status bar
Job Completion
Task Failure
Tasktracker failure
JobTracker failure
Job Scheduling
Shuffle & Sort in depth
Diving into Shuffle and Sort
Dive into Input Splits
Dive into Buffer Concepts
Dive into Configuration Tuning
Dive into Task Execution
The Task assignment Environment
Speculative Execution
Output Committers
Task JVM Reuse
Multiple Inputs & Multiple Outputs
Build In Counters
Dive into Counters – Job Counters & User Defined Counters
Sql operations using Java MapReduce
Introduction to YARN (Next Generation Map Reduce)
Apache HIVE
Hive Introduction
Hive architecture
Semantic Analyzer
Hive Integration with Hadoop
Hive Query Language(Hive QL)
Hive Installation and Configuration
Hive, Map-Reduce and Local-Mode
Hive DLL and DML Operations
Hive Services
Schema Design
embedded metastore configuration
external metastore configuration
Transformations in Hive
UDFs in Hive
How to write a simple hive queries
Hive with HBASE Integration
Apache PIG
Introduction to Apache Pig
Map Reduce Vs Apache Pig
SQL Vs Apache Pig
Different data types in Pig
Modes Of Execution in Pig
Local Mode
Map Reduce Mode
Execution Mechanism
Grunt Shell
Transformations in Pig
How to write a simple pig script
UDFs in Pig
Pig with HBASE Integration
Need to add some more R&D done by myself
Apache SQOOP
Introduction to Sqoop
MySQL client and Server Installation
How to connect to Relational Database using Sqoop
Sqoop Commands and Examples on Import and Export commands.
Transferring an Entire Table
Specifying a Target Directory
Importing only a Subset of data
Protecting your password
Using a file format other than CSV
Compressing Imported Data
Speeding up Transfers
Overriding Type Mapping
Controlling Parallelism
Encoding Null Values
Importing all your tables
Incremental Import
Importing only new data
Incrementing Importing Mutable data
Preserving the last imported value
Storing Password in the Metastore
Overriding arguments to a saved job
Sharing the MetaStore between sqoop client
Importing data from two tables
Using Custom Boundary Queries
Renaming Sqoop Job instances
Importing Queries with duplicate columns
Transferring data from Hadoop
Inserting Data in Batches
Exporting with All or Nothing Semantics
Updating an Existing Data Set
Updating or Inserting at the same time
Using Stored Procedures
Exporting into a subset of columns
Encoding the Null Value
Encoding the Null Value Differently
Exporting Corrupted Data
Apache FLUME
Introduction to flume
Flume agent usage
Apache Hbase
Hbase introduction
Hbase basics
Column families
Hbase installation
Hbase Architecture
WriteAhead Log
Log Structured MergeTrees
Mapreduce integration
Mapreduce over Hbase
Hbase Usage
Key design
Bloom Filters
Hbase Clients
Web Based UI
Hbase Admin
Schema definition
Basic CRUD operations
Apache OOZIE
Introduction to Oozie
Executing workflow jobs
Hadoop Installation on Linux, All other ecosystems installations on Linux.
Cluster setup
Installation & configuration of Cloudera & Hortonworks

Data Science Online Training
RKSOFT is one of the fastest growing IT Training Institute in Hyderabad offering IBM Cognos TM1 training, Oracle Hyperion Training, Qlikview, Bigdata and Hadoop, Data WareHouse, Informatica, Data Stage, Tableau, Cognos BI, Microstrategy,ETL testing, HIVE, Data Sciense, R, MSBI, SSIS, SSRS, SSAS, OBIEE, SAS, HFM, ESSBASE, Hyperion Planning, HFR, DRM, ODI, HPCM, TIBCO, DAC,Oracle, DBA, MS SQL Server DBA,ERWIN,Pentaho PDI and Certification
Data Science Course Content
RK SOFT's ​Data science Course has been designed for you to become as a Data Scientist. This course will fullfill your data scientist dream.
This course will cover required statistical concepts and tools.

Key Features of Data Science course:

​✓ 80 Hrs of Live Instructor Led Online sessions
✓ Real time Project Scenarios
✓ Concept oriented Training
✓ World-Class Expertise trainer

At the end of Data Science training you will be able to take up an exciting job opportunity in the field of Data Science.

An Overview of Data Analytics and Data Science
An overview of Analytics
Why is Analytics Becoming more popular?
Applications of Analytics
Need of Business Analytics
Business Decisions
Introduction to Business Analytics
Features of Business Analytics
Types of Business Analytics
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Supply Chain Analytics
Health Care Analytics
Marketing Analytics
Human Resource Analytics
Web Analytics
Business Decisions
Business Intelligence (BI)
Data Science
Importance of Data Science
Data Science as a Strategic Asset
Big Data
Analytical Tools

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