Audience
- This course is intended for:
- Database Architects
- Database Administrators
- Database Developers
- Data Analysts
- Data Scientists
Prerequisites
We recommend that attendees of this course have:
- Taken AWS Technical Essentials (or equivalent experience with AWS)
- Familiarity with relational databases and database design concepts
Duration
3 Days. Hands-On.
Course Objectives
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.
In this course, you will:
- 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 solutio
- 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
Course Content
Day 1
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 2
- 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