Data Warehousing on AWS

Intended audience

This course is intended for:

• Database Architects, Database Administrators, Database Developers, Data Analysts and Data Scientists

Prerequisites

We recommend that attendees of this course have:

• Familiarity with relational databases and database design concepts

Day One

Module 1: Introduction to Data Warehousing

• Relational databases
• Data warehousing concepts
• The intersection of data warehousing and big data
• Overview of data management in AWS
• Hands-on lab 1: Introduction to Amazon Redshift

Module 2: Introduction to Amazon Redshift

• Conceptual overview
• Real-world use cases
• Hands-on lab 2: Launching an Amazon Redshift cluster

Module 3: Launching clusters

• Building the cluster
• Connecting to the cluster
• Controlling access
• Database security
• Load data
• Hands-on lab 3: Optimizing database schemas

Day Two

Module 4: Designing the database schema

• Schemas and data types
• Columnar compression
• Data distribution styles
• Data sorting methods

Module 5: Identifying data sources

• Data sources overview
• Amazon S3
• Amazon DynamoDB
• Amazon EMR
• Amazon Kinesis Data Firehose
• AWS Lambda Database Loader for Amazon Redshift
• Hands-on lab 4: Loading real-time data into an Amazon Redshift database

Module 6: Loading data

• Preparing Data
• Loading data using COPY
• Maintaining tables
• Concurrent write operations
• Troubleshooting load issues
• Hands-on lab 5: Loading data with the COPY command

Day Three

Module 7: Writing queries and tuning for performance

• Amazon Redshift SQL
• User-Defined Functions (UDFs)
• Factors that affect query performance
• The EXPLAIN command and query plans
• Workload Management (WLM)
• Hands-on lab 6: Configuring workload management

Module 8: Amazon Redshift Spectrum

• Amazon Redshift Spectrum
• Configuring data for Amazon Redshift Spectrum
• Amazon Redshift Spectrum Queries
• Hands-on lab 7: Using Amazon Redshift Spectrum

Module 9: Maintaining clusters

• Audit logging
• Performance monitoring
• Events and notifications
• Lab 8: Auditing and monitoring clusters
• Resizing clusters
• Backing up and restoring clusters
• Resource tagging and limits and constraints
• Hands-on lab 9: Backing up, restoring and resizing clusters

Module 10: Analyzing and visualizing data

• Power of visualizations
• Building dashboards
• Amazon QuickSight editions and features