Hadoop Development in Nal Stop

Big Data Hadoop Training in Nal Stop is available in different training formats. In our first format we provide hadoop training in classroom. In second format we offer hadoop online training, with the help of webinars, with high definition video and audio capability servers. We have many training institutes distributed through the globe. In India we are offering big data and hadoop training in various areas of pune such as Pimple Saudagar,Kharadi,Vimannagar,Hadapsar,Nal Stop.

1 on 1 Sessions

VM For Practice

Video Lectures

Free Cloud Access

Free Cloud Storage

Complete Modules

24X7 Call Support

CCDH Preparation

Multiple Batches

What if I don't know Java & Linux?

We will provide you complete support for java and linux.

Would this course help me for CloudEra certification?

We have designed our syllabus in such a way that it fits the suitability for CloudEra certified developer certification.

What if I miss a lecture?

We have multiple batches running simultaneously, so if u misses a lecture u can attend it in next lecture.

Would this course help me for HortonWorks Certification?

We have designed our syllabus in such a way that it fits the suitability for HortonWorks certified developer certification.

Do you have multiple centers?

Yes, we have multiple centers.

Would you provide job assistance?

Your profile would be evaluated by experts, your resume would be rated and you would start getting calls after completion of your module.

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Hadoop Fundamentals

What is Bigdata?
Evolution of Bigdata
Types of Data and their Significance
Need for Bigdata Analytics
Why Bigdata with Hadoop?
History of Hadoop
Why Hadoop is in demand in market nowadays?
Limitations of SQL based Tools
Hadoop Nodes
Hadoop Rack
Hadoop Cluster
Architecture of Hadoop
Characteristics of Namenode
Workaround with Datanodes
Significance of JobTracker and Tasktrackers
Hase co-ordination with JobTracker
Secondary Namenode usage and Workaround
Hadoop Releases and their Significance
Introduction to Hadoop Release-1
Hadoop Daemons in Hadoop Release-1
Introduction to Hadoop Release-2
Hadoop Daemons in Hadoop Release-2
Hadoop Cluster Demo
Hadoop 2.x Cluster Architecture
A Typical Production Hadoop Cluster
Hadoop Cluster Modes
Hadoop 2.x Configuration Files
Single node cluster and Multi node cluster setup
Hadoop installation
Introduction to Hadoop FS and Processing Environment’s UIs
How to read and write files
Basic Unix commands for Hadoop
Hadoop FS shell
Hadoop releases practical
Hadoop daemons practical
Common Hadoop Shell Commands
An Overview of Hadoop Administration
How Hadoop is getting two categories Projects
New projects on Hadoop
Hadoop Storage – HDFS (Hadoop Distributed file system)
Hadoop Processing Framework (Map Reduce / YARN)
Alternates of Map Reduce
Why NOSQL is in much demand instead of SQL
Distributed warehouse for HDFS
YARN Architecture
Significance of Scalability of Operation
Use cases where not to use Hadoop
Use cases where Hadoop Is used
Facebook,Twitter,Snapdeal, Flipkart

Hadoop JAVA API

Hadoop Classes
What is MapReduceBase?
Mapper Class and its Methods
What is Partitioner and types
MapReduce Use Cases
Traditional way VS MapReduce way
Significance of MapReduce
Hadoop 2. X MapReduce Architecture
Hadoop 2. MapReduce Program
Understanding Input Splits
Relationship between Input Splits and HDFS Blocks
MapReduce: Combiner & Partitioner
Hadoop specific Data types
Working on Unstructured Data Analytics
What is an Iterator and its usage techniques
Types of Mappers and Reducers
What is Output collector and its Significance
Workaround with Joining of datasets
Complications with MapReduce
Mapreduce Anatomy
Anagram example,Teragen Example,Terasort Example
WordCount Example
Working with multiple mappers
Working with weather data on multiple Data nodes in a Fully distributed Architecture
Use Cases where MapReduce anatomy fails
Advanced MapReduce
Counters
Distributed Cache
MRunit
Joins in MapReduce
Reduce Side Join
Replicated Join
Composite Join
Cartesian Product
Custom Input Format
Sequence Input Format
XML File Parsing using MapReduce
Interview questions based on JAVA MapReduce

Working with Pig Latin - (Fundamentals)

Introduction to Pig Latin
History and Evolution of Pig Latin
Why Pig is used only with Bigdata
MapReduce VS Pig
Pig Architecture and Overview of Compiler and Execution Engine
Programming Structure in Pig
Pig Running Modes
Pig Components
Pig Execution
Pig Release and Significance of Bugfixes
Pig Specific Datatypes
Complex Datatypes
Bags, Tuples, Fields
Pig Specific Methods
Comparison between Yahoo Pig & Facebook Hive
Shell and Utility Commands
Working with Grunt Shell
Grunt commands: 17 in number
Pig Latin: Relational Operators
Pig Latin: File Loaders
Pig Latin: Group Operator
Cogroup Operator
Joins and Cogroup
Union
Understanding Diagnostic Operators
Specialized Joins in Pig
Built in Functions
Eval Function
Load and Store Functions
Math Function
String Function
Date Function
Pig UDF
Piggybank
Parameter Substitution
Pig Streaming
Pig Use Cases: Aviation and Healthcare
Pig Data Input Techniques for flatfiles
Flatfiles: Comma separated, Tab delimited, and fixed width
Working with Schemaless Approach
How to attach Schema to a file/table in Pig
Schema referencing for similar Tables and Files
Working with Delimiters

Working with Pig Latin - II (Advanced)

Working with Binary Storage and Text Loader
Bigdata Operations and Read write Analogy
Filtering Datasets
Filtering rows with specific condition
Filtering rows with multiple conditions
Filtering rows with String Based Conditions
Sorting DataSets
Sorting rows with Specific column or columns
Multi level Sort
Analogy of a Sort Operation
Grouping Datasets and Co-grouping data
Joining DataSets
Types of Joins supported by Pig Latin
Aggregate Operations like average, sum, min, max, count
Flatten Operator
Creating a UDF (USER DEFINED FUNCTION) using java
Calling UDF from a Pig Script
Data validation Scripts

Working with Hive

Overview of Hive
Background of Hive
Hive VS Pig
Installation and Configuration
Interacting HDFS using HIVE
Map Reduce Programs through HIVE
Hive Architecture and Components
Hive Commands
Loading, Filtering, Grouping
What is Meta Storage and Meta Store
Derby Database
HQL
DDL, DML, and other Sub Languages of Hive
Data types in Hive
Partitions and Buckets
Hive Tables: Managed and External
Importing Data
Querying Data
Managing Outputs
Hive Scipts
Hive UDF
Hive Operators
Hive Joins, Unions, and Groups
Sample Programs in Hive
Alter and Delete in Hive
Partition in Hive
Indexing
Industry Specific Configuration of Hive Parameters
Authentication & Authorization
Statistics with Hive
Archiving in Hive
Hands-on exercise

Advanced Hive

Understanding Hive Releases
Hive and OLTP
OLAP in Hive
Hive QL: Joining Tables
Dynamic Partitioning & Bucketing
Serialization and Deserialization
Custom Map/Reduce Scripts
Hive Indexes and Views
Hive Query Optimizers
Hive Architecture
Understanding Thrift Server
User-Defined Functions
Hue Interface for Hive
Analyzing Data with Hive Script
Difference between Hive and Impala
UDFs in Hive
Complex Use cases in Hive

Hadoop On Amazon Cloud

Introduction to Cloud Infrastructure
Amazon SaaS, Paas and IaaS
Creating EC2 Instance for Processing
Creating S3 Buckets
Deploying Data on to the Cloud
Choosing size of our instance
Configuration of EMR Instance
Creating a virtual cluster on Amazon
Deploying project and getting stats

Sqoop

An Overview of Sqoop
Sqoop Real-life Connect
Sqoop and its Uses
Advantages of Sqoop
Sqoop Processing
Sqoop Execution Process
Importing Data Using Sqoop
Sqoop Import Process
How to Import data to Hive and HBase?
How to Export Data from Hadoop using Sqoop?
Sqoop Alternative
Sqoop Connector

HBase & Zookeeper

Introduction to HBase
HBase VS RDBMS
HBase Components
Hbase Architecture
HBase Shell
HBase Client API
Data Loading Techniques
Run Modes & Configuration
HBase Cluster Deployment
Regionservers and their implementation
Client API’s and their features
How messaging system works
Columns and column families
Configuring hbase-site.xml
Available Client
Loading Hbase with semi-structured data
Internal data storage in hbase
Timestamps
Creating table with column families
MapReduce Integration.
HBase: Advanced Usage, Schema Design
Load data from pig to hbase
Zookeeper Data Model
Zookeeper Service
Challenges faced in Distributed Applications
Coordination
Znode
Client API Functions
Bulk Loading
Receiving and Inserting Data
Filters in HBase
Sqoop architecture
Data Import and export in SQOOP
Deploying quorum and configuration throughout the Cluster

Flume

Introduction to Flume
Introduction to Apache Flume
Flume Model
Flume Goals
Scalability in Flume
Flume Data Integration
Flume Installation on Single Node and Multinode Cluster
Flume Architecture and various Components
Data Sources: Types and Variants
Data Target: Types and Variants
Deploying an agent onto a single node cluster
Problems associated with Flume
Interview questions based on Flume

Oozie and Hue

Introduction to Apache Oozie
Oozie: Components
Oozie: Workflow
Scheduling with Oozie
Hands-on Training on Oozie Workflow
Oozie Coordinator
Oozie Commands
Oozie Web Console
Oozie for MapReduce
Hive in Oozie
An Overview of Hue
Hue in Real-time Scenarios
Use Cases in Hue

MongoDB

Understanding MongoDB
NoSQL Databases
JSON and BSON
Vertical and Horizontal Scaling
Data Types
MongoDB Tools
Collection and Database
Schema Design and Modeling
CRUD Operations in MongoDB
Indexing and Aggregation
Replication and Sharding
MongoDB Cluster Operations

Yarn Architecture

Introduction to YARN and MR2 daemons
Active and Standby Namenodes
Resource Manager and Application Master
Node Manager
Container Objects and Container
Namenode Federation
Cloudera Manager and Impala
Load balancing in cluster with namenode federation
Architectural differences between Hadoop 1.0 and 2.0

Basics of JAVA for Hadoop

The Java Virtual Machine
Variables
Data types
Constructs: Conditional and Looping
Types: Wrapper classes
Object-Oriented JAVA
Fields and Methods
Constructors
Overloading methods
Garbage collection
Nested classes
Overriding methods
Polymorphism
Making methods and classes final
Abstract classes and methods
Interfaces
Threads
Classes
The I/O Package
JAVA Security

Spark Ecosystem and BigData

What and How of Distributed Systems
What are New Generation Distributed Systems
What is Big Data and its Limitations
What are the Limitations of MapReduce in Hadoop

Processing: Batch and Real-time Big Data Analytics
Hadoop Ecosystem Overview
Understanding Apache Spark
Evolution and Features of Spark
Spark Ecosystem
Modes of Spark
Overview of Spark on a cluster
Spark Standalone Cluster
Spark Web User Interface.
Language Flexibility in Spark
Architecture of Spark
Spark and Big Data
APIs in Spark
Additional Benefits of Spark
Various Tasks of Spark on a Cluster
Apache Spark and Hadoop Ecosystem

Scala and Spark

Defining Scala
Features of Scala
Scala and Spark interdependency
How to use Scala in other Frameworks
What is Scala REPL
Various Scala Operations
Basic Data Types in Scala
Understanding Basic Literals
What are Operators
Various Types of Operators
What is Arithmetic Operator
How to use Logical Operator
Control Structures in Scala
Functions and Procedures
Anonymous Functions
Objects and Classes
Collections in Scala: Mutable vs Immutable Collection
Array
Array Buffer
Map and Maps Operations
Pattern Matching
Tuples
Lists
Streams

Programming Techniques in Scala

What is a Class in Scala
Understanding Getters and Setters
What are Custom Getters and Setters
General Properties of Getters
What is an Auxiliary Constructor
What is a Primary Constructor
Defining Singletons
What are Companion Objects
How to extend a Class
Overriding Methods
Traits as Interfaces and Layered Traits

Spark Shell and PySpark

What is Spark Shell?
How to create a Spark Context?
How to load a file in Shell?
Operations on files in Spark Shell
Understanding SBT
How to build a Spark Project with SBT?
How to run Spark Project with SBT?
What is a Local Mode?
Spark Mode
Distributed Persistence
Built-in Libraries for Spark
The PySpark Shell
The PySpark Shell – Advanced
Spark Tools
PySpark Integration with Jupyter Notebook
Case Study: Analyzing Airlines Data with PySpark

Working with Key/Value Pairs

Creating Pair RDDs
Transformations on Pair RDDs
Aggregations, Grouping Data, Joins, Sorting Data
Data Partitioning
Determining an RDDs Partitioner
Operations that Benefit from Partitioning
Operations that Affect Partitioning
Loading and Saving Data
File Formats: JSON, Comma-Separated and Tab-Separated Values
File Formats: Sequence Files, Object Files
Hadoop Input and Output Formats
Filesystems: Local/Regular FS
Amazon S3 and HDFS
Databases: Java Database Connectivity
Cassandra, HBase, ElasticSearch

RDD and Spark

Defining RDDs
Transformations in RDD
Various Actions in RDD
Lazy Evaluations
Passing Functions to Spark: Python, Scala, Java
How to load data in RDD?
How to save data through RDD?
Scala RDD Extensions 00:00
What are Double RDD Methods?
RDD Methods
Java Pair RDD Methods
General RDD Methods
Java RDD Methods
Common Java RDD Methods
Spark Java Function Classes
Understanding Key-Value Pair RDD in Scala and Java
MapReduce and RDD
Spark and Hadoop Integration
HDFS and Yarn

Spark Streaming

What is Spark Streaming?
Architecture and Abstraction of Spark Streaming
Streaming Word Count
What is a Micro Batch?
Understanding DStreams
Input DStreams and Receivers
Basic and Advanced Sources
Input Sources: Core Sources and Cluster Sizing
Transformations in Spark Streaming
Stateless and Stateful Transformations
Transformations on DStreams
Spark Streaming and Fault Tolerance
Driver Fault Tolerance
Worker Fault Tolerance
Receiver Fault Tolerance
Enabling Checkpointing
Socket Stream and File Stream
Stateful Operations
Window Operations and its Types
Join Operations-Stream-Dataset Joins
Join Operations-Stream-Stream Joins
Parallelism Level

Machine Learning with MLlib

What is Machine Learning?
System Requirements
Machine Learning with Spark
Spam Classification
Data Types: Understand and working with Vectors
Algorithms: Statistics, Classification, and Regression
Mllib Clustering
Mllib Collaborative Filtering
Dimensionality Reduction
Model Evaluation
Configuring Algorithms
Caching RDDs to Reuse
Recognizing Sparsity
Level of Parallelism
Pipeline API

Spark SQL and GraphX

Initializing Spark SQL
SchemaRDDs
Caching
Loading and Saving Data
Apache Hive, Parquet, JSON
JDBC/ODBC Server
Working with Beeline
Spark SQL UDFs
Hive UDFs
What is a Graph-Parallel System?
What are the Limitations of Graph-Parallel System?
What is GraphX?
What is Property Graph?
What are Graph Operators?
List of Operators
Property and Structural Operators
Subgraphs
Join Operators
How to create a Graph using GraphX
Understanding Hive and Spark SQL
An Insight into Spark SQL and SQL Context
Hive and Spark SQL Integration
Hive Queries through Spark
Various Testing Tips in Scala
Shared Variables
Broadcast Variables
Accumulators

On-time notifications

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On-site and remote support

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On-time notifications

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Good teaching And Practical training labs are well equipped. And advance training in spark is also good. The course has given good conceptual understanding of the subject Good overall knowledge of Hadoop Ecosystem.

Best training institute in Pune for Big Data Hadoop trainers are very helpful and Management is very supportive. It was a nice experience. Did a many real time projects

Etlhive is an excellent training institute where they put emphasis on hands on practise. The trainers are really awesome. They would answer each and every query. In particular, Mr. Virendra did a good job of teaching our batch of hadoop and spark/scala. All the best Mr. Virendra and Etlhive.

The Course Gave Good Understanding of Hadoop Ecosystem The Overall Training was Good The Trainer Manu Sir Has A Good Knowledge Of The Subject Overall The Course Has given good conceptual understanding Of The Subject..

Hadoop Development in Nal Stop

1 on 1 Sessions

VM For Practice

Video Lectures

Free Cloud Access

Free Cloud Storage

Complete Modules

24X7 Call Support

CCDH Preparation

Multiple Batches

Sqoop

Our Centers

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