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Machine Learning Engineer
This course demonstrates how to ingest, store, and transform data in the data warehouse. Topics covered include: the purpose of Amazon Redshift, how Amazon Redshift addresses business and technical challenges, features and capabilities of Amazon Redshift, designing a data warehousing solution on AWS by applying best practices based on the Well-Architected Framework, integration with AWS and non-AWS products and services, performance tuning, orchestration, and securing and monitoring Amazon Redshift.
Day 1
Module 1: Data Warehouse Concepts
- Modern data architecture
- Introduction to the course story
- Data warehousing with Amazon Redshift
- Amazon Redshift Serverless architecture
- Hands-On Lab: Launch and Configure an Amazon Redshift Serverless Data Warehouse
Module 2: Setting Up Amazon Redshift
- Data models for Amazon Redshift
- Data Management in Amazon Redshift
- Managing permissions in Amazon Redshift
- Hands-On Lab: Setting Up a Data Warehouse Using Amazon Redshift Serverless
Module 3: Loading Data
- Overview of data sources
- Loading data from Amazon Simple Storage Service (Amazon S3)
- Extract, transform, and load (ETL) and extract, load, and transform (ELT)
- Loading streaming data
- Loading data from relational databases
- Hands-On Lab: Populating the Data Warehouse
Day 2
, Module 4: In-Depth Look at SQL Query Editor v2 and Notebooks
- Features of Amazon Redshift Query Editor v2
- Demonstration: Using Amazon Redshift Query Editor v2
- Advanced queries
- Hands-On Lab: Data Wrangling on AWS
Module 5: Backup and Recovery
- Disaster recovery
- Backing up and restoring Amazon Redshift instances
- Backing Up and Restoring Amazon Redshift Serverless
Module 6: Amazon Redshift Performance Tuning
- Factors that affect query performance
- Table maintenance and materialized views
- Query analysis
- Workload management
- Tuning guidance
- Amazon Redshift monitoring
- Hands-On Lab: Performance Tuning the Data Warehouse
Module 7: Securing Amazon Redshift
- Introduction to Amazon Redshift Security and Compliance
- Authentication with Amazon Redshift
- Access control with Amazon Redshift
- Data encryption with Amazon Redshift
- Auditing and Compliance with Amazon Redshift
- Hands-On Lab: Securing Amazon Redshift
Day 3
, Module 8: Orchestration
- Overview of Data Orchestration
- Orchestration with AWS Step Functions
- Orchestration with Amazon Managed Workflows for Apache Airflow (MWAA)
- Hands-On Lab: Orchestrating the Data Warehouse Pipeline
- Module 9: Amazon Redshift ML
- Machine Learning Overview
- Getting Started with Amazon Redshift ML
- Amazon Redshift ML workflow scenarios
- Amazon Redshift ML Usage
- Hands-On Lab: Predicting Customer Churn with Amazon Redshift ML
Module 10: Amazon Redshift Data Sharing
- Overview of Data Sharing in Amazon Redshift
- Amazon DataZone for Data as a Service
Module 11: Wrap-Up
- Hands-On Lab: End-of-Course Challenge Lab
- Describing Amazon Redshift architecture and its role in a modern data architecture
- Designing and implementing a data warehouse in the cloud using Amazon Redshift
- Identifying and loading data into an Amazon Redshift data warehouse from a variety of sources
- Analyzing data using SQL QEV2 notebooks
- Designing and implementing a disaster recovery strategy for an Amazon Redshift data warehouse
- Performing maintenance and performance tuning on an Amazon Redshift data warehouse
- Securing and managing access to an Amazon Redshift data warehouse
- Sharing data across multiple Redshift clusters within an organization
- Orchestrating workflows in the data warehouse using AWS Step Functions state machines
- Creating an ML model and configuring predictors using Amazon Redshift ML
This course includes presentations, hands-on labs, and demonstrations.
This course is intended for the following job roles:
- Data analytics
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