Learn to do it right the first time.
As your business moves to the cloud, your teams may face a steep learning curve with new technologies. Without proper training, you risk inefficiencies, security vulnerabilities, and missed opportunities for innovation, which could put your organisation at a competitive disadvantage.
How We Can Help
Our interactive, on-site training sessions are designed to help your team build real-world, working software. Led by two senior architects from fourTheorem, each session provides hands-on experience, deep insights, and full access to training materials and source code.
We cover everything from AWS fundamentals to advanced serverless development and event-driven architectures, equipping your team with the skills necessary to drive successful cloud adoption.
We also offer bespoke training, collaborating with you to craft a custom program that addresses your organisation’s most pressing needs.
AWS Enterprise Best Practices
Audience – Operations teams, IT administrators
- Understand the best practices that will set your AWS organisation up for security, compliance and productivity
- Establish secure and easy-to-understand methods of providing AWS access to multiple users and groups
- Get off on the right foot with a secure, scalable and performant network that integrates into existing networks and other AWS accounts
- Understand security considerations and tooling to stay on top of threats and breaches
- Know how to establish configuration management to get oversight of all AWS resources and use CloudTrail to monitor and query changes to infrastructure
- AWS Organisations
- SSO
- VPCs
- Multi-account networking
- Auditing with CloudTrail
- Security fundamentals
- Compliance rules
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AWS Development Fundamentals
Audience – Development teams, DevOps teams and solutions architects
- Know how to create entire applications using Infrastructure as Code
- Understand Terraform, and CloudFormation, where to use both and how to manage them in a consistent, modular way
- Putting observability into every application on AWS and using AWS services to monitor, troubleshoot and prevent issues including cost and billing issues!
- Establish good practices for managing credentials with SSO, IAM, cross-account access and elevated privileges
- Understand the trade-offs between AWS native CI/CD services and GitHub Actions or other third-party services
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- Working with multiple accounts and environments
- Infrastructure as Code – CloudFormation and Terraform
- Identity and Access Management (IAM), how it works and how to make it work for productive development
- Managing credentials for productivity without compromising security
- Observability with CloudWatch Logs, Alarms, Metrics and Insights
- Cost management; tagging, cost explorer and budgets
- CI/CD: GitHub Actions with AWS, CodeBuild and CodePipeline
- Development tooling
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Full-stack Serverless Development on AWS with Node.js
Audience – Software development teams, including backend and frontend engineers
We also offer a variant of this training programme for Python developers
- Prepare developers for full-stack serverless development with JavaScript/TypeScript on AWS
- Be able to understand and contribute to frontend (React), backend (Node.js) and cloud deployments (AWS with CDK)
- Understand Node.js/JavaScript ecosystems, tooling and development best practices
- Get people comfortable with the fundamentals and make them able to continue the journey on their own
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See what the NN Group have to say about this training.
- JavaScript and TypeScript
- Node.js tooling and ecosystem
- Callbacks, Promises and async/await
- Serverless development on AWS
- AWS CDK and SAM
- Lambda
- API Gateway
- DynamoDB
- SQS
- SNS
- EventBridge
- Troubleshooting with CloudWatch Logs Insights
- Testing
- Frontend development with React
- Building an example e-commerce application
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Event-Driven Application Development on AWS
Audience – Software development teams, both backend and frontend engineers
- Gain an in-depth understanding of all the key AWS services for sending events within and across applications
- Understand constraints, performance and delivery semantics for every service
- Know how to integrate services like SQS, SNS, EventBridge and Kinesis to other AWS services, including S3, Lambda, Step Functions, EC2 and ECS
- Understand how to build a cross-domain event backbone for your organisation and how to resist tight coupling while building consistency and maintainability into your message flows
- Get comfortable tracing and tracking missing or failed events in development or production workflows
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- Message and event patterns
- Event buses, pub/sub and point-to-point channels
- Performance characteristics, delivery guarantees and limits
- Error handling, monitoring, dead letter queues and redelivery
- SQS
- SNS
- EventBridge
- Kinesis
- Kafka and MSK
- Creating a cross-account event backbone
- Internal application events
- Event structure, schemas and validation
- Integrating events to other AWS services
- Logging, monitoring and troubleshooting for event-driven applications
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Migrating to a data lakehouse and data analytics in AWS
Audience – Development teams, Data teams and solutions architects
- Get a deep understanding of the key AWS services used for data storage, querying and transformation
- Understand which service is best suited to each scenario
- Understand the cost implications of a real-time v batch ETL job
- Know how to integrate services like S3, Glue, Lambda, Step Functions and Athena to create an end-to-end data platform
- Create dashboards and Machine Learning models
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- Creating a Data lakehouse on Amazon S3 (storage)
- AWS Athena for querying data in the Data Lakehouse
- Using AWS Glue for event-based ETL
- Data ingestion using batch (upload to S3 / SFTP) and real-time (API Gateway, Transfer Family, Lambda)
- Build a scheduled task to move data from an on-premise database to AWS.
- Building dashboards using Power B.I. or Quicksight
- ETL pipelines using AWS Step Functions.
- Alerting, monitoring and observability of workloads
- Using AWS managed A.I. services
- Creating and deploying machine-learning models
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