Notes to self.

AWS CDK lets you define cloud infrastructure in familiar programming languages - Python, TypeScript, Java - and synthesise it into CloudFormation. Compared to writing raw CloudFormation or Terraform HCL, CDK gives you loops, conditionals, type safety, and reusable constructs. This series covers practical CDK patterns using Python, with a consistent use case across all parts so the trade-offs are easy to compare. Why CDK over CloudFormation for AWS-native workloads? CloudFormation is YAML or JSON - no loops, no conditionals, no abstraction. Any shared pattern gets copy-pasted. CDK uses a real programming language - loops, functions, classes, and type checks all apply to infrastructure the same way they apply to application code. L2 constructs handle boilerplate. bucket.grant_read(fn) generates the IAM policy, role attachment, and resource reference in one call. The CloudFormation equivalent is four resources wired together manually. The compiler catches mistakes before CloudFormation ever sees the template. CDK still synthesises to CloudFormation under the hood - rollbacks, drift detection, and stack history are unchanged. Why CDK over Terraform for AWS-native workloads? Terraform’s strength is multi-cloud. For AWS-only workloads, that comes with overhead that doesn’t pay off - a state backend, a provider version to pin, and HCL alongside the application code. CDK uses CloudFormation as the deployment engine - AWS manages state natively. No S3 bucket for state, no DynamoDB table for locking, no terraform init in the pipeline. New AWS services appear in CDK constructs faster. IAM is easier. Terraform requires writing policy JSON by hand and threading ARNs between resources. CDK’s grant_* methods generate least-privilege policies from the resource graph. Terraform is the right call when infrastructure spans multiple cloud providers, or when the team already has a mature Terraform codebase. Use case Deploy an AWS Lambda function that processes files uploaded to an S3 bucket. Configuration - bucket name, log level, Lambda timeout - varies per environment (dev, staging, prod). Simple infrastructure by design - the CDK patterns are the point, not the resources. ...

Problem GitHub branch protection requires all commits to have verified GPG signatures. Two commits at the base of the branch (made before GPG signing was configured) were unsigned, blocking the merge. Root cause Commits made before commit.gpgsign=true was configured in git have no GPG signature. GitHub requires all commits in a PR to be verified - one unsigned commit blocks the merge regardless of approvals. Steps to fix 1. Identify unsigned commits git log --format="%G? %ad %s" --date=short origin/main..HEAD Look for lines starting with N (no signature). Note the hash of the oldest unsigned commit’s parent on main. ...

This is Chapter 5 of the Infrastructure as Code with AWS CDK series. The same cdk deploy command that runs locally works in any pipeline - the differences are auth and orchestration. Common flags for CI These apply regardless of which CI system you use: cdk synth # generate CloudFormation template cdk deploy --require-approval never -c env=prod # deploy without interactive prompts cdk deploy --outputs-file outputs.json # write stack outputs to file --require-approval never skips the IAM change confirmation that CDK shows interactively. Required in CI. ...

This is Chapter 4 of the Infrastructure as Code with AWS CDK series. Chapter 3 built FileProcessorConstruct - the tests here cover that construct and the stack it lives in. How CDK testing works CDK unit tests don’t deploy anything. They synthesize a stack into a CloudFormation template and run assertions against that template. No AWS credentials needed, and the suite runs in CI without any special setup. The aws_cdk.assertions module is already part of aws-cdk-lib - no extra install required. ...

This is Chapter 3 of the Infrastructure as Code with AWS CDK series. Chapters 1 and 2 put the S3 bucket and Lambda directly in MyStack. Both get extracted into a reusable construct here. L1, L2, L3 CDK organises constructs into three levels: L1 - raw CloudFormation resource wrappers, prefixed with Cfn. CfnBucket, CfnFunction. No defaults added - every property must be set explicitly. L2 - CDK’s built-in higher-level constructs. s3.Bucket, _lambda.Function. These set sensible defaults, expose helper methods, and handle IAM via grant_* methods. L3 - composite constructs you write that bundle multiple resources into a single unit. The previous chapters used L2 constructs directly inside a stack. This chapter builds an L3. The problem The current MyStack creates the bucket and Lambda directly: ...

This is Chapter 2 of the Infrastructure as Code with AWS CDK series. Four approaches to environment-specific configuration in CDK stacks - same use case throughout so the trade-offs sit side by side. Use case: An S3 bucket and Lambda function where bucket name, log level, and Lambda timeout vary per environment (dev, staging, prod). Quick comparison Approach Version controlled Supports secrets Change without redeploy Shared across stacks Static config (cdk.json) Yes No No No Dynamic config No Partial Yes No Secrets Manager No Yes Yes Yes CI/CD context injection No No Yes No Local dev patterns Suitable for personal projects or local development. Neither is a good fit for CI/CD pipelines or shared team environments. ...