•Course explains all the labs. If you want to practice labs, it would require AWS Account and may cost $$.
•Basic working knowledge of Redshift is recommended, but not a must.
•This course has been designed for intermediate and expert AWS Developers / Architects / Administrators.
•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 !
•Who this course is for:
•Anyone who wants to learn AWS Serverless technologies for data and analytics should take this course
•Instructor and Course Introduction
•Pre-requisites – What you’ll need for this course
•Course Objectives
•AWS Serverless Analytics and Data Lake Basics
•Section Agenda
•What is Serverless Computing ?
•Basics of AWS Serverless Data Lake Architecture
•Amazon S3 – Test-Data Setup
•Lab: Sample Data Setup on Amazon S3
•Lab: Amazon S3 – Analytics Configuration
•Amazon Redshift – Cluster and Sample Data Setup
•Amazon Redshift – Introduction and Pre-requisites
•Amazon Redshift – Developing a Redshift Cluster
•Amazon Redshift – Installing Client Tools
•Amazon Redshift – Installing Sample Data
•AWS Glue – Architecture and Setup
•AWS Glue – Architecture
•AWS Glue – Terminology
•AWS Glue – Applications
•AWS Glue – Internals
•AWS Glue – Cost
•Lab: AWS Glue – Security and Privileges Setup
•AWS Glue – Advance Network Configuration
•Lab: AWS Glue – Advance Network Configuration
•AWS Glue – Database Objects
•AWS Glue – Data Catalog
•Lab: AWS Glue – Databases
•AWS Glue – Tables
•AWS Glue – Designing Tables
•AWS Glue – Crawlers
•AWS Glue – Introduction to Crawlers
•Lab – Introduction to AWS Glue Classifiers
•Lab 1 – AWS Glue – Developing Data Catalog with Crawlers
•Lab 2 – AWS Glue – Developing Data Catalog with Crawlers
•Lab 3 – AWS Glue – Developing Data Catalog with Crawlers
•Lab 4 – AWS Glue – Developing Data Catalog with Crawlers
•Lab 5 – AWS Glue – Developing Data Catalog with Crawlers
•Lab 6 – AWS Glue – Developing Data Catalog with Crawlers
•Lab 7 – AWS Glue – Developing Data Catalog with Crawlers
•AWS Glue – ETL Jobs
•Lab 1 – Developing AWS Glue Jobs
•AWS Glue Job Properties
•Lab 2 – Developing AWS Glue Jobs
•Lab 3 – Assignment : Importing Data from Redshift
•Lab 4 – Developing AWS Glue Jobs
•AWS Glue Job Scripts and Properties
•Lab 5 – Developing AWS Glue Jobs
•AWS Glue – Built-in ETL Transformations and Job Bookmarks
•AWS Glue – Triggers
•Lab 1 – Developing AWS Glue Triggers
•Lab 2 – Developing AWS Glue Triggers
•AWS Glue – Dev Ops Setup
•Lab: Creating a AWS Glue Development Endpoint
•Lab: Installing and configuring Apache Zeppelin
•Lab: Port Forwarding Configuration
•Lab: Integrating AWS Glue Development Endpoint with Apache Zeppelin
•AWS Glue Monitoring
•AWS Athena – Architecture and Setup
•AWS Athena – Architecture
•AWS Athena – Features
•AWS Athena – Object Model
•AWS Athena – Development and Administration
•Lab 1 – Developing Data Catalog with AWS Athena
•Lab 2 – Developing Data Catalog with AWS Athena
•AWS Athena – Data Types and DDL Statements
•AWS Athena – SerDe
•Lab 3 – AWS Glue – Developing Data Catalog with Athena
•AWS Athena – Querying AWS Logs
•AWS Athena – Limitations
•Amazon Redshift Spectrum – Architecture and Setup
•Amazon Redshift Spectrum – Architecture
•Amazon Redshift Spectrum – Features
•Amazon Redshift Spectrum – Development
•Lab: Amazon Redshift Spectrum – Security and Privileges Setup
•Lab: Amazon Redshift Spectrum – Developing Schema
•Lab: Amazon Redshift Spectrum – Querying Data
•Amazon QuickSight – Architecture and Setup
•Overview – What is Amazon Quicksight ?
•Amazon QuickSight – Architecture
•Lab: Amazon Quicksight – Setup
•Amazon QuickSight – Report Authoring Workflow
•Amazon QuickSight – Developing Your First Analysis in QuickSight
•Lab 1 – Amazon QuickSight – Types of Visuals
•Lab 2 – Amazon QuickSight – Data Source and Report
•Lab 3 – Amazon QuickSight – Axis and Aggregations
•Lab 4 – Amazon QuickSight – Filters
•Lab 5 – Amazon QuickSight – Storyboard and Scenes
•Lab 6 – Amazon QuickSight – Account Settings
•Amazon Quicksight – Data Ingestion
•Lab 1 – S3 Analytics as data source for Amazon QuickSight
•Lab 2 – S3 Buckets as data source for Amazon QuickSight
•Lab 3 – Redshift (auto) as data source for Amazon QuickSight
•Lab 4 – Redshift (manual) as data source for Amazon QuickSight
•Lab 5 – Athena as data source for Amazon QuickSight
•Lab 6 – Geospatial Reporting from file based data sources in Amazon QuickSight
•Lab 7 – Redshift Spectrum as data source for Amazon QuickSight
•Amazon QuickSight – Data Preparation
•Lab 1 – Amazon QuickSight – Data Refresh
•Lab 2 – Amazon QuickSight – Permissions for Data Security
•Lab 3 – Amazon QuickSight – Applying Row Security on Reports
•Lab 4 – Amazon QuickSight – Joining Tables
•Lab 5 – Amazon QuickSight – Creating Custom Filters
•Lab 6 – Amazon QuickSight – Calculated Fields, Functions, and Operators
•Lab 7 – Amazon QuickSight – Dimensions and Measures
•Amazon QuickSight – Developing Advanced A