This course is intended for:
- Database Architects
- Database Administrators
- Database Developers
- Data Analysts
- Data Scientists
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 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
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
- Szkolenie: polski
- Materiały: angielski
This course includes presentations, group exercises, and hands-on labs.
Course outline
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
- Loading data using COPY
- Maintaining tables
- Concurrent write operations
- Troubleshooting load issues
- Hands-on lab 5: Loading data with the COPY command
Day 3
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