Planning and Designing Databases on AWS

Live Online (VILT) & Classroom Corporate Training Course

Aws Certified

Learn how to identify and design the most suitable AWS database solutions so you can modernize your data infrastructure with fully managed, purpose-built databases to save time and cost, improve performance and scale, and accelerate innovation.

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Planning and Designing Databases on AWS

Overview

This course explores how to the use of the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the process pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Learners with little to no machine learning

Activites

This course includes presentations, group exercises, demonstrations, and hands-on labs.

Objectives

In this Machine Learning Pipeline on AWS course, participants will be able to:

  • Select and justify the appropriate ML approach for a given business problem

  • Use the ML pipeline to solve a specific business problem

  • Train, evaluate, deploy, and tune an ML model using Amazon SageMaker

  • Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS

  • Apply machine learning to a real-life business problem after the course is complete

Prerequisites

We recommend that attendees of The Machine Learning Pipeline on AWS course have:

  • Basic knowledge of Python programming language
  • Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
  • Basic experience working in a Jupyter notebook environment

Course Outline

Module 02022-12-21T09:43:09+05:30

Course Introduction

  • Course overview
Module 12022-12-21T09:47:51+05:30

AWS Purpose-Built Databases

  • Discussing well-architected databases
  • Analyzing workload requirements
  • Choosing the data model
  • Choosing the right purpose-built database
  • Knowledge check
Module 22022-12-21T09:48:16+05:30

Amazon Relational Database Service (Amazon RDS)

  • Discussing a relational database
  • What is Amazon RDS?
  • Why Amazon RDS?
  • Amazon RDS design considerations
  • Knowledge check
Module 32022-12-21T09:49:12+05:30

Amazon Aurora

  • What is Amazon Aurora?
  • Why Amazon Aurora?
  • Aurora design considerations
  • Knowledge check
Challenge Lab 12022-12-21T09:52:00+05:30

Working with Amazon Aurora databases

Class Activity 12022-12-21T09:50:38+05:30

Choose the Right Relational Database

Module 42022-12-21T09:53:12+05:30

Amazon DynamoDB

  • Discussing a key value database
  • What is DynamoDB?
  • Why DynamoDB?
  • DynamoDB design considerations
  • Knowledge check
Module 52022-12-21T09:54:19+05:30

Amazon Keyspaces (for Apache Cassandra)

  • Discussing a wide-column database
  • What is Apache Cassandra?
  • What is Amazon Keyspaces?
  • Why Amazon Keyspaces?
  • Amazon Keyspaces design considerations
  • Knowledge check
Module 62022-12-21T09:56:58+05:30

Amazon DocumentDB (with MongoDB compatibility)

  • Discussing a document database
  • What is Amazon DocumentDB?
  • Why Amazon DocumentDB?
  • Amazon DocumentDB design considerations
  • Knowledge check
Module 72022-12-21T09:56:21+05:30

Amazon Quantum Ledger Database (Amazon QLDB)

  • Discussing a ledger database
  • What is Amazon QLDB?
  • Why Amazon QLDB?
  • Amazon QLDB design considerations
  • Knowledge check
Class Activity 22022-12-21T09:57:40+05:30

Choose the Right Nonrelational Database

Challenge Lab 22022-12-21T09:58:20+05:30

Working with Amazon DynamoDB Tables

Module 92022-12-21T10:00:10+05:30

Amazon Timestream

  • Discussing a timeseries database
  • What is Amazon Timestream?
  • Why Amazon Timestream?
  • Amazon Timestream design considerations
  • Knowledge check
Module 102022-12-21T10:01:26+05:30

Amazon ElastiCache

  • Discussing an in-memory database
  • What is ElastiCache?
  • Why ElastiCache?
  • ElastiCache design considerations
  • Knowledge check
Module 112022-12-21T10:02:17+05:30

Amazon MemoryDB for Redis

  • What is Amazon MemoryDB (for Redis)?
  • Why Amazon MemoryDB?
  • Amazon MemoryDB design considerations
  • Knowledge check
Class Activity 32022-12-21T10:03:10+05:30

Let’s Cache In

Module 122022-12-21T10:04:15+05:30

Amazon Redshift

  • Discussing a data warehouse
  • What is Amazon Redshift?
  • Why Amazon Redshift?
  • Amazon Redshift design considerations
  • Knowledge check
Module 132022-12-21T10:04:55+05:30

Tools for Working with AWS Databases

  • Data access and analysis with Amazon Athena
  • Data migration with SCT and DMS
Class Activity 42022-12-21T10:05:32+05:30

Overall Picture

Challenge Lab 32022-12-21T10:06:06+05:30

Working with Amazon Redshift clusters

2023-01-06T13:47:55+05:30

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