Data Warehousing on AWS

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This course is designed to teach you how to:

• Discuss the core concepts of data warehousing, and the intersection between data warehousing and big
  data solutions.
• Launch an Amazon Redshift cluster and use the components, features, and functionality to implement a
  data warehouse in the cloud.
• Use other AWS data and analytic services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis,
  and Amazon S3, to contribute to the data warehousing solution.
• Architect the data warehouse.
• Identify performance issues, optimize queries, and tune the database for better performance.
• Use Amazon Redshift Spectrum to analyze data directly from an Amazon S3 bucket.
• Use Amazon QuickSight to perform data analysis and visualization tasks against the data warehouse.

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.

• 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.