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:
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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.
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Course explains all the labs. If you want to practice labs, it would require AWS Account and may cost $$.
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Basic working knowledge of Redshift is recommended, but not a must.
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This course has been designed for intermediate and expert AWS Developers / Architects / Administrators.
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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 you'll 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
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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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