AWS Data Warehousing Training Overview
This Data Warehousing on AWS training course teaches attendees the concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. Students learn how to collect, store, and prepare data for the data warehouse using AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3. In addition, this course explores how to use Amazon QuickSight to perform data analysis.
Accelebrate is an AWS Training Partner (ATP) and this hands-on official AWS Classroom Training course is taught by an accredited Amazon Authorized Instructor (AAI).
Location and Pricing
Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
- 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
Prerequisites
- Taken AWS Technical Essentials classroom training (or have the equivalent experience with AWS)
- Familiarity with relational databases and database design concepts
Outline
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Introduction to Data Warehousing
- Relational databases
- Data warehousing concepts
- The intersection of data warehousing and big data
- Overview of data management in AWS
- Introduction to Amazon Redshift
Introduction to Amazon Redshift
- Conceptual overview
- Real-world use cases
- Launching an Amazon Redshift cluster
Launching Clusters
- Building the cluster
- Connecting to the cluster
- Controlling access
- Database security
- Load data
- Optimizing database schemas
Designing the database schema
- Schemas and data types
- Columnar compression
- Data distribution styles
- Data sorting methods
Identifying data sources
- Data sources overview
- Amazon S3
- Amazon DynamoDB
- Amazon EMR
- Amazon Kinesis Data Firehose
- AWS Lambda Database Loader for Amazon Redshift
- Loading real-time data into an Amazon Redshift database
Loading Data
- Preparing Data
- Data Warehousing on AWS
- AWS Classroom Training
- Loading data using COPY
- Maintaining tables
- Concurrent write operations
- Troubleshooting load issues
- Loading data with the COPY command
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)
- Configuring workload management
Amazon Redshift Spectrum
- Amazon Redshift Spectrum
- Configuring data for Amazon Redshift Spectrum
- Amazon Redshift Spectrum Queries
- Using Amazon Redshift Spectrum
Maintaining Clusters
- Audit logging
- Performance monitoring
- Events and notifications
- Auditing and monitoring clusters
- Resizing clusters
- Backing up and restoring clusters
- Resource tagging and limits and constraints
- Backing up, restoring, and resizing clusters
Analyzing and Visualizing Data
- Power of visualizations
- Building dashboards
- Amazon QuickSight editions and features
Training Materials
All AWS students will receive comprehensive courseware.
Software Requirements
A modern web browser and an Internet connection free of restrictive firewalls, so that the student can connect by SSH or Remote Desktop (RDP) into AWS virtual machines.
