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In this course, you will learn new concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS.
Data Warehousing on AWS demonstrates how to collect, store, and prepare data for the data warehouse by using AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3. Additionally, this course demonstrates how to use Amazon QuickSight to perform analysis on your data.
Relational databases
Data warehousing concepts
The intersection of data warehousing and big data
Overview of data management in AWS
Conceptual overview
Real-world use cases
Building the cluster
Connecting to the cluster
Controlling access
Database security
Load data
Schemas and data types
Columnar compression
Data distribution styles
Data sorting methods
Data sources overview
Amazon S3
Amazon DynamoDB
Amazon EMR
Amazon Kinesis Data Firehose
AWS Lambda Database Loader for Amazon Redshift
Preparing Data
Loading data using COPY
Maintaining tables
Concurrent write operations
Troubleshooting load issues
Amazon Redshift SQL
User-Defined Functions (UDFs)
Factors that affect query performance
The EXPLAIN command and query plans
Workload Management (WLM)
Amazon Redshift Spectrum
Configuring data for Amazon Redshift Spectrum
Amazon Redshift Spectrum Queries
Audit logging
Performance monitoring
Events and notifications
Resizing clusters
Backing up and restoring clusters
Resource tagging and limits and constraints
Power of visualizations
Building dashboards
Amazon QuickSight editions and features
We recommend that individuals interested in this training have experience with relational databases and database design concepts.