Course Overview
Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. This course 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.
What you’ll learn
Upon completion of the Data Warehousing on AWS course, students will be able 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
Requirements
- Taken AWS Technical Essentials (or equivalent experience with AWS)
- Familiarity with relational databases and database design concepts
Target audiences
- Database Architects, Database Administrators, Database Developers, Data Analysts and Data Scientists
Curriculum
- 3 Sections
- 10 Lessons
- 3 Days
Expand all sectionsCollapse all sections
- Day One3
- 1.1Module 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
- 1.2Module 2: Introduction to Amazon Redshift Conceptual overview Real-world use cases Hands-on lab 2: Launching an Amazon Redshift cluster
- 1.3Module 3: Launching clusters Building the cluster Connecting to the cluster Controlling access Database security Load data Hands-on lab 3: Optimizing database schemas
- Day Two3
- 2.1Module 4: Designing the database schema Schemas and data types Columnar compression Data distribution styles Data sorting methods
- 2.2Module 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
- 2.3Module 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 Three4
- 3.1Module 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
- 3.2Module 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
- 3.3Module 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
- 3.4Module 10: Analyzing and visualizing data Power of visualizations Building dashboards Amazon QuickSight editions and features
Instructor
