Enterprises face numerous challenges with a plethora of heterogenous data types and sources they generate or use. How do you manage them all in a coherent yet manageable solution? How can you leverage the right tools that lead to actionable data-driven business insight? This is where a data warehouse solution comes in.
This course introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using AWS services.
You’ll learn how to determine and configure the appropriate AWS services to deploy a data warehouse solution, implement data pipelines, monitor and troubleshoot issues, secure access, and optimize cost and performance in accordance with best practices.
The course is divided into different modules. Learning modules allow you to learn new concepts and get introduced to AWS services to build your solution. Lab modules are in-depth, hands-on activities, with step by step instructions for you to apply what you’ve learned.
Activities
- Interactive content, videos, knowledge checks, assessments, and hands-on labs
Course objectives
In this course, you will learn to:
- Recognize an analytics customer challenge and describe the appropriate AWS solution for solving it featuring a data warehouse.
- Summarize how to design a data warehouse solution.
- Describe how to ingest the data, process and catalog it, and serve that data for consumption.
- Identify performance issues in a data pipeline.
- Describe how to use notifications during monitoring to send alerts.
- Identify troubleshooting and maintaining data pipelines.
- Describe the integration of various AWS services to perform logging.
- Identify automation and optimization options for Amazon Redshift.
- Describe how to perform Amazon Redshift stored procedures.
- Explain how to use Step functions for Orchestration.
- Describe how to secure Amazon Redshift.
- Identify how to monitor and troubleshoot Amazon Redshift.
Intended audience
This course is intended for:
- Data engineer
- Data analyst
- Data architect
- Business intelligence engineer
Recommended Skills
We recommend that attendees of this course have:
- 2-3 years of experience in data engineering
- 1–2 years of hands-on experience with AWS services
- Completed AWS Cloud Practitioner Essentials or equivalent
- Completed Fundamentals of Analytics on AWS Part 1 and 2
- Completed Data Engineering on AWS – Foundations
Courses outline
Module 1: Building a Data Warehouse Solution (75 min)This course presents Amazon Redshift and how to build an efficient data pipeline from ingestion to analysis. Throughout the module, you find videos that depicts a use case where a data warehouse provides a solution to a typical enterprise modern data problem.
- Introduction
- Designing the Data Warehouse Solution
- Ingesting Data
- Processing Data
- Serving Data for Consumption
- Assessment
- Conclusion
This lab is a step-by-step hands-on activity to set up a serverless data warehouse using Amazon Redshift.
- Lab overview
- Task 1: Create a data warehouse with Amazon Redshift Serverless
- Task 2: Create a schema
- Task 3: Create a table
- Task 4: Load sample data from Amazon S3
- Task 5: Ingest and query TICKIT data (Optional)
- Conclusion
This course discusses how to optimize, orchestrate, secure, and apply data governance to Amazon Redshift. Throughout the module, you’ll find best practices you can apply in your own data warehouse solution.
- Monitoring and optimization options
- Orchestration options
- Security and governance options
- Assessment
- Conclusion
This lab is a step-by-step hands-on activity to set up a serverless data warehouse, secure it, and test data ingestion.
- Lab overview
- Task 1: Create a data warehouse with Amazon Redshift Serverless
- Task 2: Create a schema
- Task 3: Create a table
- Task 4: Load sample data from Amazon S3
- Task 5: Ingest and query TICKIT data (Optional)
- Conclusion
