AWS-DWARE | Data Warehousing onAWS
 
Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift. This course demonstrates how to ingest, store, and transform data in the data warehouse. Topics covered include: the purpose of Amazon Redshift, how Amazon Redshift addresses business and technical challenges, features and capabilities of Amazon Redshift, designing a Data Warehousing Solution on AWS by applying best practices based on the Well-Architected Framework, integration with AWS and non-AWS products and services, performance tuning, orchestration, and securing and monitoring Amazon Redshift.
 
Course objectives 
In this course, you will learn to:
Describe Amazon Redshift architecture and its roles in a modern data architecture.
Design and implement a data warehouse in the cloud using Amazon Redshift.
Identify and load data into an Amazon Redshift data warehouse from a variety of sources.
Analyze data using SQL QEV2 notebooks.
Design and implement a disaster recovery strategy for an Amazon Redshift data warehouse.
Perform maintenance and performance tuning on an Amazon Redshift data warehouse.
Secure and manage access to an Amazon Redshift data warehouse.
Share data between multiple Redshift clusters in an organization.
Orchestrate workflows in the data warehouse using AWS Step Functions state machines.
Create an ML model and configure predictors using Amazon Redshift ML.
 
Audience profile 
This course is intended for: 
Data engineers
Data architects
Database architects
Database administrators
Database developers
 
Prerequisites 
We recommend that attendees of this course have completed the following courses:
Fundamentals of Analytics on AWS – Part 1 (Digital course)
Fundamentals of Analytics on AWS – Part 2 (Digital course)
Building Data Lakes on AWS (Instructor led Training)
Building Data Analytics Solutions Using Amazon Redshift (Instructor led Training)
 
Course Outline
Day 1
Module 1: Data Warehouse Concepts
Modern data architecture
Introduction to the course story
Data warehousing with Amazon Redshift
Amazon Redshift Serverless architecture
Hands-On Lab: Launch and Configure an Amazon Redshift Serverless Data Warehouse
Module 2: Setting up Amazon Redshift
Data models for Amazon Redshift
Data management in Amazon Redshift
Managing permissions in Amazon Redshift
Hands-On Lab: Setting up a Data Warehouse using Amazon Redshift Serverless
Module 3: Loading Data
Overview of data sources
Loading data from Amazon Simple Storage Service (Amazon S3)
Extract, transform, and load (ETL) and extract, load, and transform (ELT)
Loading streaming data
Loading data from relational databases
Hands-On Lab: Populating the data warehouse
Day 2
Module 4: Deep Dive into SQL Query Editor v2 and Notebooks
Features of Amazon Redshift Query Editor v2
Demonstration: Using Amazon Redshift Query Editor v2
Advanced queries
Hands-On Lab: Data Wrangling on AWS
Module 5: Backup and Recovery
Disaster recovery
Backing up and restoring Amazon Redshift provisioned
Backing up and restoring Amazon Redshift Serverless
Module 6: Amazon Redshift Performance Tuning
Factors that impact query performance
Table maintenance and materialized views
Query analysis
Workload management
Tuning guidance
Amazon Redshift monitoring
Hands-On Lab: Performance Tuning the Data Warehouse
Module 7: Securing Amazon Redshift
Introduction to Amazon Redshift security and compliance
Authentication with Amazon Redshift
Access control with Amazon Redshift
Data encryption with Amazon Redshift
Auditing and compliance with Amazon Redshift
Hands-On Lab: Securing Amazon Redshift
Day 3
Module 8: Orchestration
Overview of data orchestration
Orchestration with AWS Step Functions
Orchestration with Amazon Managed Workflows for Apache Airflow (MWAA)
Hands-On Lab: Orchestrating the Data Warehouse Pipeline
Module 9: Amazon Redshift ML
Machine Learning Overview
Getting started with Amazon Redshift ML
Amazon Redshift ML workflow scenarios
Amazon Redshift ML Usage
Hands-On Lab: Predicting customer churn with Amazon Redshift ML
Module 10: Amazon Redshift Data Sharing
Overview of data sharing in Amazon Redshift
Amazon DataZone for Data as a service
Module 11: Wrap-Up
Hands-On Lab: End of course challenge lab
 
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Debido a las constantes actualizaciones de los contenidos de los cursos por parte del fabricante, el contenido de este temario puede variar con respecto al publicado en el sitio oficial, sin embargo, Netec siempre entregará la versión actualizada de éste