Data Science Skool

Course Objectives

Target Audience

Course Prerequisites

Value to the Professionals

Value to the Professionals- 2

Value to the Professionals- 3

Lessons Covered

Conclusion

Objectives

Need of New Generation Distributed Systems

Limitations of Map reduce in Hadoop

Limitations of Map reduce in Hadoop-2

Batch vs Real-Time Processing

Application of Stream Processing

Application of In-Memory Processing

Introduction to Apache Spark

History of Spark

Language Flexibility in Spark

Spark Execution Architecture

Automatic Parallelization of Complex Flows

Automatic Parallelization of Complex Flows-Important Points

Apis That Match User Goals

Apache Spark- A Unified Platform of Big Data Apps

More Benefits of Apache Spark

Running Spark in Different Modes

Installing Spark as a Standalone Cluster - Configuration

Demo - Install Apache Spark

Overview of Spark on a Cluster

Demo-Install Apache Spark-1

Tasks of Spark on a Cluster

Companies Using Spark - Use Cases

Hadoop Ecosystem vs Apache Spark

Hadoop Ecosystem vs Apache Spark-2

Summary

Summary-2

Conclusion

Objectives

Introduction to Scala

Basic Data Types

Basic Literals

Basic Literals-2

Basic Literals-3

Introduction to Operators

Use Basic Literals and the Arithmetic Operator

Demo Use Basic Literals and the Arithmetic Operator

Use the Logical Operator

Demo Use the Logical Operator

Introduction to Type Inference

Type Inference for Recursive Methods

Type Inference for Polymorphic Methods and Generic Classes

Unreliability on Type Inference Mechanism

Mutable Collection vs Immutable Collection

Functions

Anonymous Functions

Objects

Classes

Use Type Inference, Functions, Anonymous Function and Class

Demo Use Type Inference, Functions, Anonymous Function and Class

Traits as Interfaces

Traits - Example

Collections

Types of Collections

Types of Collections-2

Lists

Perform Operations on Lists

Demo Use Data Structures

Maps

Pattern Matching

Implicits

3.34 Implicits-2

Streams

Use Data Structures

Demo Perform Operations on Lists

Summary

Summary-2

Conclusion

Objectives

RDDS API

Creating RDDS

Creating RDDS Referencing an External Dataset

Referencing an External Dataset Text Files

Referencing an External Dataset Text Files-2

Referencing an External Dataset Sequence Files

Referencing an External Dataset other Hadoop Input Formats

Creating RDDS - Important Points

RDDS Operations

RDD Operations - Transformations

Features of RDD Persistence

Storage Levels of RDD Persistence

Invoking the Spark Shell

Importing Spark Classes

Creating the Spark context

Creating the Spark Context

Loading a File in Shell

Performing Some Basic Operations on Files in Spark Shell RDDS

Packaging a Spark Project With SBT

Running a Spark Project with SBT

Demo - Build a Scala Project

Build A Scala Project-1

Demo - Build a Spark Java Project

Build A Spark Java Project-1

Shared Variables - Broadcast

Shared Variables - Accumulators

Writing a Scala Application

Demo - Run a Scala Application

Run a Scala Application

Write a Scala Application Reading the Hadoop Data

Write a Scala Application Reading the Hadoop Data

Demo - Run a Scala Application Reading the Hadoop Data

Run a Scala Application Reading the Hadoop Data

DoubleRDD Methods

PairRDD Methods- Join

PairRDD Methods- Others

JavaPairRDD Methods

JavaPairRDD Methods-2

General RDD Methods

General RDD Methods-2

Java RDD Methods

Common Java RDD Methods

Spark Java Function Classes

Method for Combining JavaPairRDD Functions

Transformations in RDD

Other Methods

Actions in RDD

Key-value Pair RDD in Scala

Key-value Pair RDD in Java

Using Mapreduce and Pair RDD Operations

Reading Text File from HDFS

Reading Sequence File from HDFS

Writing Text Data to HDFS.mp4

Writing Sequence File to HDFS

Using Groupby

Using Groupby-2

Demo - Run a Scala Application Performing Groupby Operation

Run A Scala Application Performing Groupby Operation-1

Demo - Write and Run a Java Application

Write and Run a Java Application

Summary

Summary-2

Conclusion

Objectives

Importance of Spark SQL

Benefits of Spark SQL

Dataframes

SQLContext

SQL Context-2

Creating a Dataframe

Using Dataframe Operations

Using Dataframe Operations-2

Demo - Run SparkSQL with a Dataframe

Run Spark SQL Programmatically-1

Save Modes

Saving to Persistent Tables

Parquet Files

Partition Discovery

Schema Merging

JSON Data

Hive Table

DML Operation - Hive Queries

Demo - Run Hive Queries Using Spark SQL

JDBC to other Databases

Supported Hive Features

Supported Hive Features-2

Supported Hive Data Types

Case Classes

Case Classes-2

Summary

Summary-2

Conclusion

Objectives

Introduction to Spark Streaming

Working of Spark Streaming

Streaming Word Count

Micro Batch

DStreams

DStreams-2

Input DStreams and Receivers

Input DStreams and Receivers-2

Basic Sources

Advanced Sources

Transformations on DStreams

Output Operations on DStreams

Design Patterns for Using ForeachRDD

Dataframe and SQL Operations

Dataframe and SQL Operations-2

Checkpointing

Enabling Checkpointing

Socket Stream

File Stream

Stateful Operations

Window Operations

Types of Window Operations

Types of Window Operations-2

Join Operations - Stream - Dataset Joins

Monitoring Spark Streaming Application

Performance Tuning - High Level

Demo - Capture and Process the Netcat Data

Capture and Process the Flume Data

Demo - Capture the Twitter Data

Capture the Twitter Data

Summary

Summary-2

Conclusion