Mastering AWS Glue, QuickSight, Athena & Redshift Spectrum

Mastering AWS Glue, QuickSight, Athena & Redshift Spectrum

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 !

Master serverless analytics with AWS Glue, QuickSight, Athena, & Redshift Spectrum (includes preview features with labs)

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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

Rating: 4.28125

Level: All Levels

Duration: 18.5 hours

Instructor: Siddharth Mehta