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Best Best Big data Hadoop and Spark Developer Online Training & Best Big data Hadoop and Spark Developer Online Classes

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BIG DATA HADOOP AND SPARK DEVELOPER ONLINE TRAINING  COURSE

We cover all the topics from basics to advanced level. Every topic covered under the Big data hadoop and spark developer Online Training  will be explained in practical ways with examples. Our Big data hadoop and spark developer trainers are Big data hadoop and spark developer experts and experienced working professionals with real time Big data hadoop and spark developer projects knowledge.

 

Big data Hadoop and Spark Developer Course Content

Scala, Spark & Kafka Course Contents

 

Ø Understand the difference between Apache Spark and Hadoop

Ø Learn Scala and its programming implementation

ü Why Scala

ü Scala Installation

ü Get deep insights into the functioning of Scala

ü Execute Pattern Matching in Scala

ü Functional Programming in Scala – Closures, Currying, Expressions, Anonymous Functions

ü Know the concepts of classes in Scala

ü Object Orientation in Scala – Primary, Auxiliary Constructors, Singleton & Companion Objects

ü Traits and Abstract classes in Scala

ü Scala Simple Build Tool – SBT

ü Building with Maven

Ø Spark Basics

ü What is Apache Spark?

ü Spark Installation

ü Spark Configuration

ü Spark Context

ü Using Spark Shell

ü Resilient Distributed Datasets (RDDs) – Features, Partitions, Tuning Parallelism

ü Functional Programming with Spark

Ø Working with RDDs

ü RDD Operations - Transformations and Actions

ü Types of RDDs

ü Key-Value Pair RDDs – Transformations and Actions

ü MapReduce and Pair RDD Operations

ü Serialization

Ø Spark on a cluster

ü Overview

ü A Spark Standalone Cluster

ü The Spark Standalone Web UI

ü Executors & Cluster Manager

ü Spark on YARN Framework

Ø Writing Spark Applications

ü Spark Applications vs. Spark Shell

ü Creating the SparkContext

ü Configuring Spark Properties

ü Building and Running a Spark Application

ü Logging

ü Spark Job Anatomy

Ø Caching and Persistence

ü RDD Lineage

ü Caching Overview

ü Distributed Persistence

Ø Improving Spark Performance

ü Shared Variables: Broadcast Variables

ü Shared Variables: Accumulators

ü Per Partition Processing

ü Common Performance Issues

Ø Spark API for different File Formats & Compression Codecs

ü Text

ü CSV

ü Sequence

ü Parquet

ü ORC

ü Compression Techniques – Snappy, Zlib, Gzip

 

Ø Spark SQL

ü Spark SQL Overview

ü HiveContext

ü SQL Datatypes

ü Dataframes vs RDDs

ü Operations on DFs

ü Parquet Files with Spark Sql – Read, Write, Partitioning, Merging Schema

ü ORC Files

ü JSON Files

ü Inferring Schema programmatically

ü Custom Case Classes

ü Temp Tables vs Persistent Tables

ü Writing UDFs

ü Hive Support

ü JDBC Support - Examples

ü HBase Support - Examples

Ø Spark Streaming

ü Spark Streaming Overview

ü Example: Streaming Word Count

ü Other Streaming Operations

ü Sliding Window Operations

ü Developing Spark Streaming Applications – Integration with Kafka and Hbase

Kafka

Ø Kafka Ecosystem

ü Overview

ü Producer

ü Consumer

ü Broker

ü Topics

ü Partitions

Ø Kafka Twitter Data Setup

ü Writing Producer in Scala

ü Writing Consumer in Scala & Java

Ø Kafka Integration with Spark Streaming

ü Real use case – Integration of Kafka with Spark Streaming for processing Streaming Log files and Storing results into Hbase