AWS Lambda

AWS Lambda

AWS Lambda is Amazon’s event-driven, serverless compute service that runs code in response to triggers without provisioning or managing servers.

Remembering (Knowledge / Recall)

🧠 At this level, an expert can **define** AWS Lambda and **name** its core components, terminology, and usage contexts.

• Core terminology & definitions
  • AWS Lambda – Amazon Web Services’ serverless compute platform that executes functions in response to events.
  • Serverless computing – A cloud execution model where infrastructure, scaling, and capacity are fully managed by the provider.
  • Function-as-a-Service (FaaS) – A cloud model where applications run as stateless functions invoked on demand.
  • Event-driven programming – A programming paradigm in which execution is triggered by external events.
  • Amazon Web Services (AWS) – The cloud platform that provides Lambda and related services.

• Key components & services involved
  • Amazon S3 – Can trigger Lambda functions on object events.
  • Amazon DynamoDB – Can invoke Lambda on table streams.
  • Amazon API Gateway – Routes API requests to Lambda.
  • Amazon CloudWatch – Stores logs and metrics for Lambda.

• Canonical tools & frameworks
  • AWS CloudFormation – Infrastructure-as-code deployment.
  • AWS CLI / SDKs – Programmatic Lambda management.
  • IDEs – Used to write, package, and test Lambda code.
  • CI/CD pipelines – Commonly automate Lambda deployments.

• Where AWS Lambda is commonly used
  • Serverless APIs, microservices, backend automation
  • Data processing pipelines and batch jobs
  • Real-time file, stream, or database event handling
  • Cloud automation and DevOps workflows

• Typical recall-level facts
  • Introduced by AWS in **2014**.
  • Supports multiple languages (e.g., Python, Node.js, Java, Go).
  • Billed based on requests and execution duration.
  • Functions run in short-lived, stateless execution environments.

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Understanding (Comprehension)

📖 At this level, an expert can **describe**, **summarize**, and **compare** AWS Lambda concepts and behaviors.

• Conceptual relationships & contrasts
  • AWS Lambda vs. cloud VMs – No servers to manage or provision.
  • Serverless vs. microservices – Lambda can implement microservice patterns but is not required.
  • AWS Lambda vs. containerization – Lambda abstracts runtime environments and scaling.

• Core principles & paradigms
  • Stateless execution — functions do not retain memory between invocations.
  • Automatic scaling — concurrency increases as events increase.
  • Pay-per-use — costs align with actual execution time.
  • Event-driven architecture — workloads react to triggers.

• How AWS Lambda works
  • Code + runtime + configuration = deployed function.
  • Execution environments are created on demand.
  • Events trigger invocation; outputs return to the calling service.

• Producer vs. consumer perspectives
  • Developer – Writes and packages function logic.
  • Platform engineer – Secures, monitors, and manages scaling.
  • Application user – Interacts indirectly through APIs, apps, or event triggers.

• Typical comprehension-level abilities
  • Can explain why Lambda is useful.
  • Can identify services that can trigger a Lambda function.
  • Can describe cold starts and their impact.

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Applying (Use / Application)

🛠️ At this level, an expert can **use** AWS Lambda to build functioning workloads.

• "Hello, World" examples
  • Creating a simple function triggered by API Gateway.
  • Uploading a Lambda function package via AWS Console or CLI.

• Guides for core task loops
  • Develop → package → deploy → test → monitor.
  • Connecting Lambda to S3, DynamoDB, or SNS event sources.
  • Wiring Lambda behind an HTTP endpoint.

• Common commands / operations
  • Deploy function with AWS CLI (`aws lambda create-function`).
  • Update code or configuration (`aws lambda update-function-code`).
  • View logs in CloudWatch (`aws logs tail`).

• Real-world use cases
  • Image or document processing on S3 upload.
  • Scheduled maintenance via CloudWatch Events.
  • ETL workloads on DynamoDB Streams or Kinesis.

• Typical application-level abilities
  • Can deploy and invoke Lambda functions.
  • Can attach appropriate event sources.
  • Can troubleshoot basic permission or runtime issues.

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Analyzing (Break Down / Analysis)

🔬 At this level, an expert can **examine**, **compare**, and **diagnose** AWS Lambda behavior and architecture.

• Comparative analysis
  • Lambda vs. EC2 — operational burden vs. flexibility.
  • Lambda vs. ECS/EKS — ephemeral execution vs. container orchestration.
  • Lambda vs. Cloud Functions / Azure Functions — cross-provider trade-offs.

• Failure modes & root causes
  • Timeout due to slow dependencies or networking.
  • Throttling caused by insufficient concurrency limits.
  • Permissions failures from incorrect IAM roles.

• Troubleshooting & observability techniques
  • Use CloudWatch Logs for debugging errors.
  • Inspect distributed traces with AWS X-Ray.
  • Monitor concurrency, durations, and error rates.

• Architectural insights
  • Event fan-out through SNS, SQS, or EventBridge.
  • Managing shared dependencies using Lambda layers.

• Typical analysis-level abilities
  • Can identify performance bottlenecks.
  • Can map system behavior across multiple event sources.
  • Can choose between compute options based on workload.

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Creating (Synthesis / Create)

🏗️ At this level, an expert can **design**, **compose**, and **optimize** systems using AWS Lambda.

• Design patterns & best practices
  • Event-driven microservices using SNS or EventBridge.
  • Reusable logic via Lambda layers.
  • Idempotent execution to handle retries safely.

• Security & authentication patterns
  • Principle of least privilege in IAM roles.
  • Secret storage using AWS Systems Manager Parameter Store or Secrets Manager.
  • VPC-integrated Lambda for private networking.

• Lifecycle management strategies
  • Automated deployments with CI/CD pipelines.
  • Versioning and aliasing for staged rollouts.
  • Traffic shifting (blue-green or canary deployments).

• Scalability & optimization patterns
  • Asynchronous processing with SQS.
  • Provisioned concurrency for latency-sensitive workloads.
  • Efficient packaging and dependency management.

• Typical creation-level abilities
  • Can architect multi-service solutions using Lambda.
  • Can design cost-efficient, scalable serverless systems.
  • Can enforce security, resiliency, and maintainability.

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Evaluating (Judgment / Evaluation)

⚖️ At this level, an expert can **assess**, **compare**, and **justify** AWS Lambda adoption and system quality.

• Evaluation frameworks & tools
  • Load-testing APIs backed by Lambda.
  • Reviewing CloudWatch metrics for performance and cost.
  • Security audits for IAM roles, triggers, and data handling.

• Maturity & adoption considerations
  • Team familiarity with serverless development.
  • Operational readiness for monitoring and incident response.

• Key performance indicators
  • Execution duration, memory usage, concurrency.
  • Error rate, cold-start latency, throughput.
  • Cost per workload or per invocation.

• Strategic decision criteria
  • Choose Lambda when workloads are event-driven, bursty, or unpredictable.
  • Prefer EC2/ECS/EKS when workloads require long-running compute or custom OS access.

• Holistic impact analysis
  • Cost efficiency vs. operational control.
  • Vendor lock-in and portability considerations.
  • Developer productivity and release velocity.

• Typical evaluation-level abilities
  • Can defend or reject Lambda adoption based on workload needs.
  • Can recommend service boundaries and compute alternatives.
  • Can prioritize improvements using objective data.