DWAWSAWAmazon Web ServicesAW-DWAWS2.0<p>In this course, you will learn to: </p> <ul> <li>Describe Amazon Redshift architecture and its roles in a modern data architecture</li><li>Design and implement a data warehouse in the cloud using Amazon Redshift</li><li>Identify and load data into an Amazon Redshift data warehouse from a variety of sources</li><li>Analyze data using SQL QEV2 notebooks</li><li>Design and implement a disaster recovery strategy for an Amazon Redshift data warehouse</li><li>Perform maintenance and performance tuning on an Amazon Redshift data warehouse</li><li>Secure and manage access to an Amazon Redshift data warehouse</li><li>Share data between multiple Redshift clusters in an organization</li><li>Orchestrate workflows in the data warehouse using AWS Step Functions state machines</li><li>Create an ML model and configure predictors using Amazon Redshift ML</li></ul><p>We recommend that attendees of this course have completed the following courses: </p> <ul> <li>Fundamentals of Analytics on AWS &ndash; Part 1 (Digital course)</li><li>Fundamentals of Analytics on AWS &ndash; Part 2 (Digital course)</li><li>Building Data Lakes on AWS <span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/en/course/amazon-bdla"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Building Data Lakes on AWS <span class="fl-prod-pcode">(BDLA)</span></a></span> (Instructor led Training)</li><li>Building Data Analytics Solutions Using Amazon Redshift <span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/en/course/amazon-bdasar"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Building Data Analytics Solutions Using Amazon Redshift <span class="fl-prod-pcode">(BDASAR)</span></a></span> (Instructor led Training)</li></ul><p>This course is intended for: </p> <ul> <li>Data engineers</li><li>Data architects</li><li>Database architects</li><li>Database administrators</li><li>Database developers</li></ul><p><strong>Day 1</strong></p> <h4>Module 1: Data Warehouse Concepts</h4><ul> <li>Modern data architecture</li><li>Introduction to the course story</li><li>Data warehousing with Amazon Redshift</li><li>Amazon Redshift Serverless architecture</li><li>Hands-On Lab: Launch and Configure an Amazon Redshift Serverless Data Warehouse</li></ul><h4>Module 2: Setting up Amazon Redshift</h4><ul> <li>Data models for Amazon Redshift</li><li>Data management in Amazon Redshift</li><li>Managing permissions in Amazon Redshift</li><li>Hands-On Lab: Setting up a Data Warehouse using Amazon Redshift Serverless</li></ul> <h4>Module 3: Loading Data</h4><ul> <li>Overview of data sources</li><li>Loading data from Amazon Simple Storage Service (Amazon S3)</li><li>Extract, transform, and load (ETL) and extract, load, and transform (ELT)</li><li>Loading streaming data</li><li>Loading data from relational databases</li><li>Hands-On Lab: Populating the data warehouse</li></ul><p><strong>Day 2</strong></p> <h4>Module 4: Deep Dive into SQL Query Editor v2 and Notebooks</h4><ul> <li>Features of Amazon Redshift Query Editor v2</li><li>Demonstration: Using Amazon Redshift Query Editor v2</li><li>Advanced queries</li><li>Hands-On Lab: Data Wrangling on AWS</li></ul><h4>Module 5: Backup and Recovery</h4><ul> <li>Disaster recovery</li><li>Backing up and restoring Amazon Redshift provisioned</li><li>Backing up and restoring Amazon Redshift Serverless</li></ul><h4>Module 6: Amazon Redshift Performance Tuning</h4><ul> <li>Factors that impact query performance</li><li>Table maintenance and materialized views</li><li>Query analysis</li><li>Workload management</li><li>Tuning guidance</li><li>Amazon Redshift monitoring</li><li>Hands-On Lab: Performance Tuning the Data Warehouse</li></ul><h4>Module 7: Securing Amazon Redshift</h4><ul> <li>Introduction to Amazon Redshift security and compliance</li><li>Authentication with Amazon Redshift</li><li>Access control with Amazon Redshift</li><li>Data encryption with Amazon Redshift</li><li>Auditing and compliance with Amazon Redshift</li><li>Hands-On Lab: Securing Amazon Redshift</li></ul><p><strong>Day 3</strong></p> <h4>Module 8: Orchestration</h4><ul> <li>Overview of data orchestration</li><li>Orchestration with AWS Step Functions</li><li>Orchestration with Amazon Managed Workflows for Apache Airflow (MWAA)</li><li>Hands-On Lab: Orchestrating the Data Warehouse Pipeline</li></ul><h4>Module 9: Amazon Redshift ML</h4><ul> <li>Machine Learning Overview</li><li>Getting started with Amazon Redshift ML</li><li>Amazon Redshift ML workflow scenarios</li><li>Amazon Redshift ML Usage</li><li>Hands-On Lab: Predicting customer churn with Amazon Redshift ML</li></ul><h4>Module 10: Amazon Redshift Data Sharing</h4><ul> <li>Overview of data sharing in Amazon Redshift</li><li>Amazon DataZone for Data as a service</li></ul> <h4>Module 11: Wrap-Up</h4><ul> <li>Hands-On Lab: End of course challenge lab</li></ul>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 MLWe 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 Building Data Lakes on AWS (BDLA) (Instructor led Training) - Building Data Analytics Solutions Using Amazon Redshift Building Data Analytics Solutions Using Amazon Redshift (BDASAR) (Instructor led Training)This course is intended for: - Data engineers - Data architects - Database architects - Database administrators - Database developersDay 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 lab3 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