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Design, implement and manage an AWS data warehouse
Your analytics depend on data that is available and properly organised. With AWS, connect collection, transformation and delivery to structure your data flows. Strengthen your ability to design processing workflows that meet application and business needs.
- Duration
- 3 days 21 hours
- Code
- DATA003FR Code
Presentation
Data warehousing on AWS centres primarily on Amazon Redshift, a fast, scalable cloud data warehouse service. It stores and analyses large volumes of structured and semi-structured data to generate business insights. Redshift integrates with other AWS services, such as S3 for storage and QuickSight for visualisation, providing a comprehensive, high-performance analytics solution.
This intensive course equips you with the practical skills to master the development of data warehousing solutions with Amazon. You will explore essential AWS tools and services, including Amazon Redshift, in depth, together with methods for ingesting, storing and transforming data in a data warehouse. Through hands-on workshops, you will learn to optimise data loading and transformation, and to secure and monitor Amazon Redshift.
By the end of this 3-day programme, you will have developed the expertise to design high-performance, secure data analytics solutions on AWS. You will master Amazon Redshift optimisation and best practices for unlocking the full potential of your cloud data.
As an Amazon Web Services authorised premium training partner (ATP), Oo2 offers skills development and certification courses that meet the organisation's rigorous quality standards.
Objectives
By the end of this Data Warehousing on AWS course, you will be able to:
- describe Amazon Redshift architecture and its roles within a modern data architecture;
- design and implement a cloud data warehouse using Amazon Redshift;
- identify and load data into a data warehouse from various sources;
- analyse data using QEV2 SQL notebooks;
- design and implement a disaster recovery strategy for a data warehouse;
- maintain and optimise data warehouse performance;
- secure and manage access to a data warehouse;
- share data across multiple Redshift clusters within an organisation;
- orchestrate data warehouse workflows using AWS Step Functions state machines;
- create an ML model and configure predictors using Amazon Redshift ML.
Program
Module 1: explore data warehouse concepts
- Modern data architecture fundamentals.
- Introduction to the course scenario.
- Data warehousing with Amazon Redshift.
- Amazon Redshift Serverless architecture.
Lab:
- Launch and configure an Amazon Redshift Serverless data warehouse.
Module 2: configure Amazon Redshift
- Data model management.
- Data management.
- Permission management.
Lab:
- Configure a data warehouse using Amazon Redshift Serverless.
Module 3: load data
- Overview of data sources.
- Importing data from Amazon S3.
- Extract, transform and load (ETL).
- Extract, load and transform (ELT).
- Loading streaming data.
- Loading data from relational databases.
Lab:
- Populate the data warehouse.
Module 4: explore SQL Query Editor v2 and notebooks
- Amazon Redshift Query Editor v2 features.
- Advanced queries.
Lab:
- Perform data wrangling on AWS.
Module 5: back up and restore
- Disaster recovery.
- Backing up and restoring provisioned and Serverless Amazon Redshift.
Module 6: optimise Amazon Redshift performance
- Factors affecting query performance.
- Table maintenance and materialised views.
- Query analysis.
- Workload management.
- Optimisation tips.
- Monitoring Amazon Redshift.
Lab:
- Optimise data warehouse performance.
Module 7: secure Amazon Redshift
- Security and compliance fundamentals.
- Authentication.
- Access control.
- Data encryption.
- Auditing and compliance.
Lab:
- Secure Amazon Redshift.
Module 8: orchestrate data
- Data orchestration fundamentals.
- Orchestration with AWS Step Functions.
- Orchestration with Amazon Managed Workflows for Apache Airflow (MWAA).
Lab:
- Orchestrate the data warehouse pipeline.
Module 9: use Amazon Redshift ML
- Machine learning fundamentals with Amazon Redshift ML.
- Getting started.
- Workflow scenarios.
- Using Amazon Redshift ML.
Lab:
- Predict customer churn with Amazon Redshift ML.
Module 10: share data with Amazon Redshift
- Data sharing fundamentals in Amazon Redshift.
- Using Amazon DataZone for data as a service.
Module 11: course wrap-up
- Review of the course's key points.
- Lab challenge.
Audience
This course is intended for:
- data engineers responsible for designing, building and maintaining data warehouses on AWS;
- data architects responsible for designing and implementing data warehousing solutions on the AWS platform;
- database administrators responsible for managing, optimising and securing Amazon Redshift databases;
- developers who want to integrate AWS data warehousing services into their applications to analyse data efficiently.
Prerequisites
The following prerequisites are recommended for this AWS course:
- completion of the "AWS Analytics Fundamentals" course;
- completion of the "Build a high-performance data lake with AWS Lake Formation" course;
- completion of the "Build data analytics solutions with Amazon Redshift" course.
Teaching and assessment methods
- Initial skills assessment
- Training materials provided to participants
- Continuous assessment throughout the course
- End-of-course feedback questionnaire
- Combination of theory and practical application
- Attendance records
- Post-course follow-up evaluation
- Practical exercises
Course highlights
- Expert AWS-certified instructors: benefit from recognised AWS-certified instructors with in-depth knowledge of data warehousing concepts and AWS tools and services.
- Interactive hands-on learning: master data warehousing tools and techniques on AWS through practical demonstrations and workshops. Prepare to tackle real-world challenges in designing data warehousing solutions, loading and transforming data, and optimising Amazon Redshift performance.
- Develop key skills: the course content is carefully designed to build essential skills in data warehouse design and implementation, data management, Amazon Redshift security, and advanced features such as Amazon Redshift ML.
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.
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