Mastering AWS Glue, QuickSight, Athena Redshift Spectrum

Mastering AWS Glue, QuickSight, Athena & Redshift Spectrum, available at $79.99, has an average rating of 4.03, with 193 lectures, based on 3659 reviews, and has 26702 subscribers.

You will learn about Confidently work with AWS Serverless services to develop Data Catalogue, ETL, Analytics and Reporting on a Data Lake Develop deep knowledge in Glue, Athena, Redshift Spectrum and QuickSight Build a serverless data lake on AWS using structured and unstructured data Architect Serverless Analytics solutions on AWS cloud platform This course is ideal for individuals who are Anyone who wants to learn AWS Serverless technologies for data and analytics should take this course or Data Professionals seeking to learn Serverless Storage, Serverless ETL, Serverless Data Analysis and Serverless Reporting should take this course It is particularly useful for Anyone who wants to learn AWS Serverless technologies for data and analytics should take this course or Data Professionals seeking to learn Serverless Storage, Serverless ETL, Serverless Data Analysis and Serverless Reporting should take this course.

Enroll now: Mastering AWS Glue, QuickSight, Athena & Redshift Spectrum

Summary

Title: Mastering AWS Glue, QuickSight, Athena & Redshift Spectrum

Price: $79.99

Average Rating: 4.03

Number of Lectures: 193

Number of Published Lectures: 193

Number of Curriculum Items: 193

Number of Published Curriculum Objects: 193

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Confidently work with AWS Serverless services to develop Data Catalogue, ETL, Analytics and Reporting on a Data Lake

Develop deep knowledge in Glue, Athena, Redshift Spectrum and QuickSight

Build a serverless data lake on AWS using structured and unstructured data

Architect Serverless Analytics solutions on AWS cloud platform

Who Should Attend

Anyone who wants to learn AWS Serverless technologies for data and analytics should take this course

Data Professionals seeking to learn Serverless Storage, Serverless ETL, Serverless Data Analysis and Serverless Reporting should take this course

Target Audiences

Anyone who wants to learn AWS Serverless technologies for data and analytics should take this course

Data Professionals seeking to learn Serverless Storage, Serverless ETL, Serverless Data Analysis and Serverless Reporting should take this course

PS:

1. Please do NOT join the course if you do NOT have any basic working knowledge of AWS Console and AWS Services like S3, IAM, VPC, Security Groups etc. AWS Beginners may struggle understanding some of the topics.

2. Course explains all the labs. If you want to practice labs, it would require AWS Account and may cost $$.

3. Basic working knowledge of Redshift is recommended, but not a must.

4. This course has been designed for intermediate and expert AWS Developers / Architects / Administrators.

5. Course covers each and every feature that AWS has released since 2018 for AWS Glue, AWS QuickSight, AWS Athena, and Amazon Redshift Spectrum, and it regularly updated with every new feature released for these services.

Serverless is the future of cloud computing and AWS is continuously launching new services on Serverless paradigm. AWS launched Athena and QuickSight in Nov 2016, Redshift Spectrum in Apr 2017, and Glue in Aug 2017.Data and Analytics on AWS platform is evolving and gradually transforming to serverless mode.

Businesses have always wanted to manage less infrastructure and more solutions. Big data challenges are continuously challenging the infrastructure boundaries. Having Serverless Storage, Serverless ETL, Serverless Analytics, and Serverless Reporting, all on one cloud platform had sounded too good to be true for a very long time. But now its a reality on AWS platform. AWS is the only cloud provider that has all the native serverless components for a true Serverless Data Lake Analytics solution.

It’s not a secret that when a technology is new in the industry, professionals with expertise in new technologies command great salaries. Serverless is the future, Serverless is the industry demand, and Serverless is new. It’s the perfect time and opportunity to jump into Serverless Analytics on AWS Platform.

In this course, we would learn the following:

1) We will start with Basics on Serverless Computing and Basics of Data Lake Architecture on AWS.

2) We will learn Schema Discovery, ETL, Scheduling, and Tools integration using Serverless AWS Glue Engine built on Spark environment.

3) We will learn to develop a centralized Data Catalogue too using Serverless AWS Glue Engine.

4) We will learn to query data lake using Serverless Athena Engine build on the top of Presto and Hive.

5) We will learn to bridge the data warehouse and data lake using Serverless Amazon Redshift Spectrum Engine built on the top of Amazon Redshift platform.

6) We will learn to develop reports and dashboards, with a powerpoint like slideshow feature, and mobile support, without building any report server, by using Serverless Amazon QuickSight Reporting Engines.

7) We will finally learn how to source data from data warehouse, data lake, join data, apply row security, drill-down, drill-through and other data functions using the Serverless Amazon QuickSight Reporting Engines.

This course understands your time is important, and so the course is designed to be laser-sharp on lecture timings, where all the trivial details are kept at a minimum and focus is kept on core content for experienced AWS Developers / Architects / Administrators. By the end of this course, you can feel assured and confident that you are future-proof for the next change and disruption sweeping the cloud industry.

I am very passionate about AWS Serverless computing on Data and Analytics platform, and am covering A-to-Z of all the topics discussed in this course.

So if you are excited and ready to get trained on AWS Serverless Analytics platform, I am ready to welcome you in my class !

Course Curriculum

Chapter 1: Introduction

Lecture 1: Instructor and Course Introduction

Lecture 2: Pre-requisites – What youll need for this course

Lecture 3: Course Objectives

Lecture 4: Course Content, Convention and Resources

Chapter 2: AWS Serverless Analytics and Data Lake Basics

Lecture 1: Section Agenda

Lecture 2: What is Serverless Computing ?

Lecture 3: Basics of AWS Serverless Data Lake Architecture

Chapter 3: Amazon S3 – Test-Data Setup

Lecture 1: Section Agenda

Lecture 2: Lab: Sample Data Setup on Amazon S3

Lecture 3: Lab: Amazon S3 – Analytics Configuration

Chapter 4: Amazon Redshift – Cluster and Sample Data Setup

Lecture 1: Section Agenda

Lecture 2: Amazon Redshift – Introduction and Pre-requisites

Lecture 3: Amazon Redshift – Developing a Redshift Cluster

Lecture 4: Amazon Redshift – Installing Client Tools

Lecture 5: Amazon Redshift – Installing Sample Data

Chapter 5: AWS Glue – Architecture and Setup

Lecture 1: Section Agenda

Lecture 2: AWS Glue – Architecture

Lecture 3: AWS Glue – Terminology

Lecture 4: AWS Glue – Applications

Lecture 5: AWS Glue – Internals

Lecture 6: AWS Glue – Cost

Lecture 7: Lab: AWS Glue – Security and Privileges Setup

Lecture 8: AWS Glue – Advance Network Configuration

Lecture 9: Lab: AWS Glue – Advance Network Configuration

Chapter 6: AWS Glue – Database Objects

Lecture 1: Section Agenda

Lecture 2: AWS Glue – Data Catalog

Lecture 3: Lab: AWS Glue – Databases

Lecture 4: AWS Glue – Tables

Lecture 5: AWS Glue – Designing Tables

Chapter 7: AWS Glue – Crawlers

Lecture 1: Section Agenda

Lecture 2: AWS Glue – Introduction to Crawlers

Lecture 3: Lab – Introduction to AWS Glue Classifiers

Lecture 4: Lab 1 – AWS Glue – Developing Data Catalog with Crawlers

Lecture 5: Lab 2 – AWS Glue – Developing Data Catalog with Crawlers

Lecture 6: Lab 3 – AWS Glue – Developing Data Catalog with Crawlers

Lecture 7: Lab 4 – AWS Glue – Developing Data Catalog with Crawlers

Lecture 8: Lab 5 – AWS Glue – Developing Data Catalog with Crawlers

Lecture 9: Lab 6 – AWS Glue – Developing Data Catalog with Crawlers

Lecture 10: Lab 7 – AWS Glue – Developing Data Catalog with Crawlers

Chapter 8: AWS Glue – ETL Jobs

Lecture 1: Section Agenda

Lecture 2: Introduction to AWS Glue Jobs

Lecture 3: Lab 1 – Developing AWS Glue Jobs

Lecture 4: AWS Glue Job Properties

Lecture 5: Lab 2 – Developing AWS Glue Jobs

Lecture 6: Lab 3 – Assignment : Importing Data from Redshift

Lecture 7: Lab 4 – Developing AWS Glue Jobs

Lecture 8: AWS Glue Job Scripts and Properties

Lecture 9: Lab 5 – Developing AWS Glue Jobs

Lecture 10: AWS Glue – Built-in ETL Transformations and Job Bookmarks

Chapter 9: AWS Glue – Triggers

Lecture 1: Section Agenda

Lecture 2: Introduction to AWS Glue Triggers

Lecture 3: Lab 1 – Developing AWS Glue Triggers

Lecture 4: Lab 2 – Developing AWS Glue Triggers

Chapter 10: AWS Glue – Dev Ops Setup

Lecture 1: Section Agenda

Lecture 2: Lab: Creating a AWS Glue Development Endpoint

Lecture 3: Lab: Installing and configuring Apache Zeppelin

Lecture 4: Lab: Port Forwarding Configuration

Lecture 5: Lab: Integrating AWS Glue Development Endpoint with Apache Zeppelin

Lecture 6: AWS Glue Monitoring

Chapter 11: AWS Glue New Features and Releases : 2018, 2019, 2020

Lecture 1: 10-Apr-2018 : AWS Glue supports timeout values for ETL Jobs

Lecture 2: 10-Jul-2018 : AWS Glue supports reading from Amazon DynamoDB Tables

Lecture 3: 13-Jul-2018 : AWS Glue provides additional ETL Job metrics

Lecture 4: 04-Sep-2018 : AWS Glue supports data encryption at rest

Lecture 5: 05-Oct-2018 : AWS Glue supports connecting Sagemaker notebooks to dev endpoints

Lecture 6: 15-Oct-2018 : AWS Glue supports resource based policies and permissions

Lecture 7: 22-Jan-2019 : AWS Glue introduces Python Shell Jobs

Lecture 8: 04-Feb-2019 : Download Source code AWS Glue Data Catalog Client – Hive Metastore

Lecture 9: 14-Mar-2019 : AWS Glue enables running Apache Spark SQL Queries

Lecture 10: 20-Mar-2019 : AWS Glue supports resource tagging

Lecture 11: 05-Apr-2019 : AWS Glue supports additional options for memory-intensive jobs

Lecture 12: 10-May-2019 : AWS Glue crawlers support existing Data Catalog tables as sources

Lecture 13: 28-May-2019 : AWS Glue enables continuous logging for Spark ETL Jobs

Lecture 14: 06-Jun-2019 : AWS Glue supports scripts compatible with Python 3.6 in Shell Jobs

Lecture 15: 20-Jun-2019 : AWS Glue provides workflows to orchestrate ETL workloads

Lecture 16: 25-Jul-2019 : AWS Glue supports running ETL Jobs on Spark 2.4.3 with Python 3

Lecture 17: 25-Jul-2019 : AWS Glue supports additional options for memory intensive jobs

Lecture 18: 26-Jul-2019 : AWS Glue supports bookmarking Parquet and ORC Files using ETL Jobs

Lecture 19: 06-Aug-2019 : Launch AWS Glue, EMR and Aurora Serverless Clusters in Shared VPCs

Lecture 20: 09-Aug-2019 : AWS Glue provides FindMatches ML Transform

Lecture 21: 28-Aug-2019 : AWS Glue releases binaries of Glue ETL libraries for Glue Jobs

Lecture 22: 19-Sep-2019 : AWS Glue provides Apache Spark UI to monitor Glue ETL Jobs

Lecture 23: 22-Oct-2019 : AWS Glue provides ability to rewind Spark ETL Job bookmarks

Lecture 24: 22-Nov-2019 :AWS Glue support FindMatches ML Transform on Spark 2.4.3 & Glue 1.0

Lecture 25: 25-Nov-2019 : AWS Glue supports bringing your own JDBC driver for Spark ETL Jobs

Lecture 26: 16-Jan-2020 : Glue adds new transforms – Purge, Transition and Merge

Lecture 27: 03-Apr-2020 : Glue supports reading & writing to DocumentDB & MongoDB Collection

Lecture 28: 03-Apr-2020 : AWS Glue supports new tables, update schema & partitions from Jobs

Lecture 29: 27-Apr-2020 : AWS Glue supports serverless streaming ETL

Chapter 12: AWS Athena – Architecture and Setup

Instructors

Siddharth Mehta
Enterprise Cloud Architect, Published Author, Cloud Geek

Rating Distribution

1 stars: 142 votes

2 stars: 139 votes

3 stars: 444 votes

4 stars: 1297 votes

5 stars: 1637 votes

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