Taming Big Data using Spark & Scala

Taming Big Data using Spark & Scala, available at $89.99, has an average rating of 3.85, with 129 lectures, 2 quizzes, based on 10 reviews, and has 39 subscribers.

You will learn about Big Data and its EcoSystem like Hadoop , Sqoop, Hive, Flume, Kafka, Spark using Scala, Spark SQL & Spark Streaming Both the Concepts (Theories & Architectures) + Practicals Assignments & Projects Scenarios for Real Projects Build, deploy, and run Spark scripts on Hadoop clusters Transform structured data using SparkSQL and DataFrames Process continual streams of data with Spark Streaming Working on intellij and executing the JAR through scripts Practice questions for CCA 175 Certification This course is ideal for individuals who are The course is designed to be used for all who want to learn and move to Big Data Technologies. or Those who want to get a real feel of project like scenarios along with learning the concepts or ONE Stop Shop for Required Big Data Tools with Theories, Concepts, Practicals, Practice Scenarios & Project Scenarios using Scala Programming Langiage It is particularly useful for The course is designed to be used for all who want to learn and move to Big Data Technologies. or Those who want to get a real feel of project like scenarios along with learning the concepts or ONE Stop Shop for Required Big Data Tools with Theories, Concepts, Practicals, Practice Scenarios & Project Scenarios using Scala Programming Langiage.

Enroll now: Taming Big Data using Spark & Scala

Summary

Title: Taming Big Data using Spark & Scala

Price: $89.99

Average Rating: 3.85

Number of Lectures: 129

Number of Quizzes: 2

Number of Published Lectures: 129

Number of Published Quizzes: 2

Number of Curriculum Items: 131

Number of Published Curriculum Objects: 131

Number of Practice Tests: 2

Number of Published Practice Tests: 2

Original Price: £99.99

Quality Status: approved

Status: Live

What You Will Learn

Big Data and its EcoSystem like Hadoop , Sqoop, Hive, Flume, Kafka, Spark using Scala, Spark SQL & Spark Streaming
Both the Concepts (Theories & Architectures) + Practicals
Assignments & Projects Scenarios for Real Projects
Build, deploy, and run Spark scripts on Hadoop clusters
Transform structured data using SparkSQL and DataFrames
Process continual streams of data with Spark Streaming
Working on intellij and executing the JAR through scripts
Practice questions for CCA 175 Certification

Who Should Attend

The course is designed to be used for all who want to learn and move to Big Data Technologies.
Those who want to get a real feel of project like scenarios along with learning the concepts
ONE Stop Shop for Required Big Data Tools with Theories, Concepts, Practicals, Practice Scenarios & Project Scenarios using Scala Programming Langiage

Target Audiences

The course is designed to be used for all who want to learn and move to Big Data Technologies.
Those who want to get a real feel of project like scenarios along with learning the concepts
ONE Stop Shop for Required Big Data Tools with Theories, Concepts, Practicals, Practice Scenarios & Project Scenarios using Scala Programming Langiage

The Course is for those who do not know even ABC of Big Data and tools, want to learn them and be in a comfortable situation to implement them in projects. The course is also for those, who have some knowledge on Big Data tools, but want to enhance them further and be comfortable working in Projects. Due to the extensive scenario implementation, the course is also suitable for people interested to write Big Data Certifications like CCA 175. The course contains Practice Test for CCA 175.

Because the course is focused on setting up the entire Hadoop Platform on your windows (for those having less than 6GB RAM) and providing or working on fully configured VM’s, you need not to buy clustervery often to practice the tools. Hence, the Course is ONE TIME INVESTMENT for secure future.

In the course, we will learn how to utilize Big Data tools like Hadoop, Flume, Kafka, Spark, Scala (the most valuable tech skills on the market today).

In this course I will show you how to –

Use Scala and Spark to analyze Big Data.
Practice Test for writing CCA 175 Exam is available at the end of the course.
Extensive and Real time project scenarios with solutions as you will write in REAL PROJECTS
Use Sqoop to import data from Traditional Relational Databases to HDFS & Hive.
Use Flume and Kafka to process streaming data
Use Hive to view and store data & Partition the tables
Use Spark Streaming to fetch the streaming data from Kafka & Flume
The VM’s in the course are configured to work synchronously together and also have Spark 2.2.0 Version Installed. (Standard Cloudera VM has Spark 1.6 Installed with NO KAFKA and requires an upgrade for Spark, while the VM’s provided in the course has Spark 2.2 configured and working along with Kafka.)

Big Data is the most in demand skills right now, and with this course you can learn them quickly and easily! You can also learn the components in the basic setup in files like “hdfs-site.xml”, “core-site.xml” etc They are good to know if working for a projet.

The course is focused on upskilling someone who do not know Big Data tools and target is to bring them up-to the mark to be able to work in Big Data projects seamlessly without issues.

This course comes with some project scenarios and multiple datasets to work on with.

After completing this course you will feel comfortable putting Big Data, Scala and Spark on your resume and also will be easily able to work and implement in projects!

Thanks and I will see you inside the course!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Practice Test Added for CCA 175 Certification

Chapter 2: Big Data Platform Setup

Lecture 1: Different forms of Big Data Platforms

Lecture 2: Installation on Windows or Cloudera

Lecture 3: Browse through Shared Course content

Lecture 4: Course -Additional Section Info

Chapter 3: Use Windows/Cloudera VM provided in the course

Lecture 1: Setup VM

Lecture 2: Setup IntelliJ on VM

Lecture 3: WIndows HDFS Error & Fix

Chapter 4: Simply setup IntelliJ and Spark and Practice only these two

Lecture 1: Setup Mysql & Basics

Lecture 2: Setup Spark

Lecture 3: Setup IntelliJ – Part 1

Lecture 4: Setup IntelliJ – Part 2

Lecture 5: Possible Issue in IntelliJ

Lecture 6: SBT Setup forScala CLI/REPL

Lecture 7: Winutil Setup in Windows for Hadoop like implementation

Chapter 5: Learning Hadoop – Architecture, Concepts & Implementation

Lecture 1: Hadoop Architecture – Part 1 – Basics of Hadoop

Lecture 2: Hadoop Architecture – Part 2 – Understanding NameNode and DataNode

Lecture 3: Hadoop Architecture – Part 3 – Understanding Job Tracker & Task Tracker

Lecture 4: Hadoop Refresh & File Systems

Lecture 5: Hadoop Terminologies & Configurations in XML Files

Lecture 6: Hadoop Commands on Windows or Windows VM – Part 1

Lecture 7: Hadoop Commands on Windows or Windows VM – Part 2

Lecture 8: Hadoop Commands on Cloudera Quick Start VM

Chapter 6: Learning Sqoop – Architecture, Concepts & Implementation

Lecture 1: Sqoop Architecture

Lecture 2: Sqoop Eval on Windows/ Windows VM

Lecture 3: Sqoop Eval on Windows – Using -e & –query options

Lecture 4: Sqoop List Database and List Tables – Used for creating Generic Code

Lecture 5: Sqoop Import Command – Understanding and Analysing the Map-Reduce Functionality

Lecture 6: Sqoop Import – Append Mode of Execution

Lecture 7: Sqoop Import – Overwrite option & Different File Formats supported

Lecture 8: Sqoop Import – Using Where & Columns Options to filter the data import

Lecture 9: Sqoop Import – Executing User Specific Query with Where Clause

Lecture 10: Sqoop Import – Incremental Load Execution

Lecture 11: Sqoop Jobs – Create, List & Execute Sqoop Jobs

Lecture 12: Sqoop Import All Option to Import all tables from Mysql to HDFS

Lecture 13: Sqoop Import – Import from MySQL To Hive – Basic Import

Lecture 14: Sqoop Import – Import from MySQL To Hive – More Options

Lecture 15: Sqoop Import All – Import from MySQL to Hive using Import All

Lecture 16: Sqoop Import – from Mainframe – A basic know how

Lecture 17: Sqoop Export – Bring Data from HDFS to MySQL

Lecture 18: Sqoop Assignment for Practice

Chapter 7: Learning Hive – Architecture, Concepts & Implementation

Lecture 1: Hive – Introduction & Features

Lecture 2: Hive – Architecture & Map-Reduce Execution

Lecture 3: Hive Tables

Lecture 4: Hive Partitioning & Bucketing – Concepts and Difference

Lecture 5: Hive Query Language – Overview and Syntax

Lecture 6: Hive QL – Practicals – Create Database & Tables & load sample data

Lecture 7: Hive QL – Practicals – Load Huge Data to Managed Tables

Lecture 8: Hive QL – Practicals – Creating and Loading Manged & External Tables

Lecture 9: Hive QL – Practicals – Partitioning in Hive

Lecture 10: Hive QL – Practicals – Bucketing in Hive

Lecture 11: Hive User Defined Functions

Lecture 12: Hive Performance Tuning Methods

Chapter 8: Learning Flume – Architecture, Concepts & Implementation

Lecture 1: Flume – Concepts, Usage, Features & Advantages

Lecture 2: Flume Architecture

Lecture 3: Flume Data Flows , Contextual Routing & Other Concepts

Lecture 4: Basics of Flume Configurations

Lecture 5: Setup of Telnet in Windows

Lecture 6: Flume Practicals – Simple Flume Job using NetCat

Lecture 7: Flume Practicals – Flume Job using EXEC

Lecture 8: Flume Practicals – Flume Job using Sequence Generator

Lecture 9: Flume Practicals – Flume Job using Sequence Generator on HDFS

Lecture 10: Flume Practicals – Flume Job using Twitter on Windows

Lecture 11: Flume Practicals – Flume Job using Twitter on Cloudera

Lecture 12: Flume Practicals – Flume Job using Twitter on File Channel

Lecture 13: Flume Practicals – Flume Job using Twitter to Hive Sink

Lecture 14: Flume Multiplexing – One Source, One Channel & Two Sink – Logger and HDFS Sinks

Lecture 15: Industry Usage of Flume

Chapter 9: Learning Kafka – Architecture, Concepts & Implementation

Lecture 1: Kafka Concepts and Architecture 1

Lecture 2: Kafka Concepts and Architecture 2

Lecture 3: Kafka Concepts and Architecture 3

Lecture 4: Kafka Sample Execution on Cloudera

Lecture 5: Flume and Kafka Together

Chapter 10: Learning Scala in Command Line Interface (REPL) & IntelliJ

Lecture 1: Scala CLI/REPL on Windows & Cloudera with Mutable and Immutable Variables

Lecture 2: Scala – Session 2 – Data Types Used & Applicable Functions

Lecture 3: Scala – Session 3 – Range

Lecture 4: Scala – Session 4 – For Loops

Lecture 5: Scala While loops

Lecture 6: Functions in Scala

Lecture 7: Functions in Scala 2

Lecture 8: Functions and Function Overloading in Scala

Lecture 9: Object Oriented Programming in Scala using Classes & Objects

Lecture 10: Scala Collections

Lecture 11: Scala Input Output Files

Chapter 11: Learning Spark – Architecture & Concepts

Lecture 1: Spark Architecture

Lecture 2: Spark Components, Lazy Executions, DAG, SparkSQL ,Performance Tuning etc

Lecture 3: Spark – Shuffles ,Coalesce, Repartition & Shared Variables

Chapter 12: Spark RDD – Implementations

Instructors

Anshul Roy
Machine Learning Engineer @ Adastra Germany

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 1 votes
4 stars: 3 votes
5 stars: 4 votes

Frequently Asked Questions

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