Big data Training in Pune

ICONIC Offers Big data Hadoop training in course library is a framework that uses basic programming concepts to distribute the processing of massive data sets across clusters of machines. It is built to grow from a single server to thousands of machines and is capable of addressing all of the Big Data Technology’s complicated data management concerns. Big Data and Hadoop Certification Training from Iconic can help you obtain knowledge in these areas. Let’s get your Big Data career started.

Big Data Hadoop Training in pune offers a professional Hadoop technology course. Traditional technologies will be unable to meet the expanding data demands, necessitating the development of highly organized and automated technology. Big data and Hadoop are two types of potential data analysis, curation, and management technologies. The goal of the Hadoop and Big Data course is to give students the information and technical skills they need to become effective Hadoop developers. Along with learning, there is the virtual implementation of the subject’s main concepts on real-world applications. Large clusters of data can be broken down into simpler versions for easier access and management using simple programming modules. Iconic is the most qualified provider of Hadoop training in Pune

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Course Introduction
• Introduction
Introduction to Big Data and Hadoop
• Introduction to Big Data
• Big Data Analytics
• What is Big Data
• Four Vs Of Big Data
• Case Study
• Challenges of Traditional System
• Distributed Systems
• Introduction to Hadoop
• Components of Hadoop Ecosystem: Part One
• Components of Hadoop Ecosystem: Part Two
• Components of Hadoop Ecosystem: Part Three
• Commercial Hadoop Distributions
Hadoop Architecture,Distributed Storage(HDFS) and YARN
• What Is HDFS
• Need for HDFS
• Regular File System vs HDFS
• Characteristics of HDFS
• HDFS Architecture and Components
• High Availability Cluster Implementations
• HDFS Component File System Namespace
• Data Block Split
• Data Replication Topology
• HDFS Command Line
• YARN Introduction
• YARN and Its Architecture
• Resource Manager
• How Resource Manager Operates
• Application Master
• How YARN Runs an Application
• Tools for YARN Developers
Data Ingestion into Big Data Systems and ETL
• Data Ingestion Overview Part One
• Data Ingestion
• Apache Sqoop
• Sqoop and Its Uses
• Sqoop Processing
• Sqoop Import Process
• Sqoop Connectors
• Apache Flume
• Flume Model
• Scalability in Flume
• Components in Flume’s Architecture
• Configuring Flume Components
• Apache Kafka
• Aggregating User Activity Using Kafka
• Partitions
• Apache Kafka Architecture
• Producer Side API Example
• Consumer Side API
• Consumer Side API Example
• Kafka Connect
Distributed Processing MapReduce Framework and Pig
• Distributed Processing in MapReduce
• Word Count Example
• Map Execution Phases
• Map Execution Distributed Two Node Environment
• MapReduce Jobs
• Hadoop MapReduce Job Work Interaction
• Setting Up the Environment for MapReduce Development
• Set of Classes
• Advanced MapReduce
• Data Types in Hadoop
• OutputFormats in MapReduce
• Using Distributed Cache
• Joins in MapReduce
• Replicated Join
• Introduction to Pig
• Components of Pig
• Pig Data Model
• Pig Interactive Modes
• Pig Operations
• Relations Performed by Developers
• Apache Pig
Apache Hive
• Hive SQL over Hadoop MapReduce
• Hive Architecture
• Interfaces to Run Hive Queries
• Running Beeline from Command Line
• Hive Metastore
• Hive DDL and DML
• Creating New Table
• Data Types
• Validation of Data
• File Format Types
• Data Serialization
• Hive Table and Avro Schema
• Hive Optimization Partitioning Bucketing and Sampling
• Non Partitioned Table
• Data Insertion
• Dynamic Partitioning in Hive
• Bucketing
• What Do Buckets Do
• Hive Analytics UDF and UDAF
• Assisted Practice: Synchronization
• Other Functions of Hive
NoSQL Databases HBase
• NoSQL Introduction
• HBase Overview
• HBase Architecture
• Data Model
• Connecting to HBase
• HBase Shell
Basics of Functional Programming and Scala
• Introduction to Scala
• Scala Installation
• Functional Programming
• Programming with Scala
• Type Inference Classes Objects and Functions in Scala
• Collections
• Types of Collections
• Scala REPL
Apache Spark Next Generation Big Data Framework
• History of Spark
• Limitations of MapReduce in Hadoop
• Introduction to Apache Spark
• Components of Spark
• Application of In-Memory Processing
• Hadoop Ecosystem vs Spark
• Advantages of Spark
• Spark Architecture
• Spark Cluster in Real World
Spark Core Processing RDD
• Processing RDD
• Introduction to Spark RDD
• RDD in Spark
• Creating Spark RDD
• Pair RDD
• RDD Operations
• Caching and Persistence
• Storage Levels
• Lineage and DAG
• Need for DAG
• Debugging in Spark
• Partitioning in Spark
• Scheduling in Spark
• Shuffling in Spark
• Sort Shuffle
• Aggregating Data with Pair RDD
Spark SQL Processing DataFrames
• Spark SQL Introduction
• Spark SQL Architecture
• DataFrames
• Interoperating with RDDs
• RDD vs DataFrame vs Dataset
• Processing DataFrames
Spark MLlib Modeling Big Data with Spark
• Role of Data Scientist and Data Analyst in Big Data
• Analytics in Spark
• Machine Learning
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Semi-Supervised Learning
• Overview of MLlib
• MLlib Pipelines
Stream Processing Frameworks and Spark Streaming
• Streaming Overview
• Real-Time Processing of Big Data
• Data Processing Architectures
• Spark Streaming
• Introduction to DStreams
• Transformations on DStreams
• Design Patterns for Using ForeachRDD
• State Operations
• Windowing Operations
• Join Operations stream-dataset Join
• Streaming Sources
• Structured Spark Streaming
• Structured Streaming Architecture Model and Its Components
• Output Sinks
• Structured Streaming APIs
• Constructing Columns in Structured Streaming
• Windowed Operations on Event-Time
Spark GraphX
• Introduction to Graph
• Graphx in Spark
• Graph Operators
• Join Operators
• Graph Parallel System
• Algorithms in Spark
• Pregel API
• Use Case of GraphX