Programming Concepts and Infrastructure as Code
Reliable deployment of data pipelines is crucial, emphasizing the integration of CI/CD, version control, and Infrastructure as Code (IaC). CI/CD automates the build, test, and deployment processes, significantly reducing manual errors. Version control with Git ensures a comprehensive audit trail of changes. IaC, using AWS CloudFormation and CDK, allows for consistent and repeatable infrastructure deployments. Additionally, AWS SAM simplifies serverless application deployment, enhancing local testing and development efficiency. Together, these practices ensure that data pipelines are delivered reliably and consistently across environments.
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Deploying data pipelines reliably to production is just as critical as building them. On the AWS Certified Data Engineer – Associate exam, you are expected to understand how CI/CD, version control, Infrastructure as Code, and serverless deployment models work together to deliver Glue jobs, Lambda functions, Redshift configurations, and Step Functions workflows in a repeatable, auditable manner.
This lesson covers the four deployment pillars that transform manually configured data infrastructure into automated, version-controlled, production-grade systems. With Lambda-based serverless patterns and Redshift transformations established in prior lessons, we now shift focus to the practices that get those pipelines into production without manual errors.
CI/CD flow
Continuous integration (CI) automatically builds and tests pipeline code on every commit, catching bugs before they propagate. Continuous delivery (CD) takes validated changes and deploys them to staging or production environments without manual intervention. Together, they eliminate the fragile, error-prone process of manually uploading Glue scripts or clicking through the console to update infrastructure.
AWS provides three tightly integrated developer tools that form the backbone of a data pipeline CI/CD workflow.
AWS CodeCommit serves as a fully managed, Git-compatible repository where pipeline code, CloudFormation templates, and Glue ETL scripts are stored and versioned.
AWS CodePipeline orchestrates multi-stage release workflows, connecting source, build, test, and deploy stages into a single automated flow.
AWS CodeBuild executes build and test commands inside managed containers, running unit tests, linting checks, and packaging steps without provisioning any servers.
A typical CI/CD flow for a data pipeline works as follows. A developer pushes an updated Glue ETL script or CloudFormation template to CodeCommit. CodePipeline detects the change and triggers the pipeline. CodeBuild runs unit tests and linting against the new code. Upon success, the deploy stage uses CloudFormation to provision or update the target Glue jobs, S3 buckets, and IAM roles in the destination environment. If tests fail, the pipeline halts and can trigger a rollback to the last known good state.
The following diagram illustrates this end-to-end CI/CD flow for a data engineering ... ...