Deploying a Serverless Web App with AWS Lambda and DynamoDB

Introduction

Building a serverless web app can dramatically reduce operational overhead while improving scalability. This guide walks through designing, building, and deploying a serverless web app using AWS Lambda, API Gateway, and DynamoDB with CI/CD integration. You will get practical architecture patterns, code and configuration examples, and a GitHub Actions pipeline to automate deployments.

Why choose a serverless web app

Serverless architectures remove the need to manage servers, letting teams focus on code and product features. For web apps that have variable traffic, serverless scales automatically and often reduces costs because you pay per invocation. DynamoDB provides a fully managed NoSQL datastore capable of single-digit millisecond latency and automatic scaling, while API Gateway provides secure endpoints in front of Lambda functions.

Designing the architecture

A minimal, production-ready serverless web app typically contains:

• API Gateway to handle HTTP(S) requests and authorization.
• Lambda functions for request processing and business logic.
• DynamoDB for persistence with a primary key and optional secondary indexes.
• IAM roles scoped with least privilege for Lambda and API Gateway.
• Optional CloudFront for CDN and caching static assets.

Example flow: a browser sends a POST to /items -> API Gateway routes to CreateItem Lambda -> Lambda validates input and writes to DynamoDB -> returns 201 with item ID. Use environment variables or AWS Systems Manager Parameter Store for connection settings and secrets.

Building the app: Lambda, API Gateway, and DynamoDB

Choose a runtime (Node.js, Python, or Go are common). Keep Lambdas small (single responsibility) and make them idempotent. A simple Node.js handler for creating an item looks like:

Note: below is a conceptual example, not executable inline code.

const AWS = require(‘aws-sdk’); const db = new AWS.DynamoDB.DocumentClient(); exports.handler = async (event) => { const body = JSON.parse(event.body); const item = { id: crypto.randomUUID(), …body }; await db.put({ TableName: process.env.TABLE, Item: item }).promise(); return { statusCode: 201, body: JSON.stringify(item) }; };

For DynamoDB table design, define a clear partition key (for example, itemId) and use Global Secondary Indexes (GSIs) for alternate query patterns. Provisioned capacity can be used for predictable loads; otherwise use on-demand mode for automatic scaling. Enable autoscaling or on-demand to handle bursts and avoid throttling.

For API Gateway, use REST or HTTP APIs. HTTP APIs are cheaper and lower latency for most REST use cases. Configure request validation, throttling, and a custom domain. Use Lambda proxy integration to receive full request context in the Lambda event object.

CI/CD for serverless: GitHub Actions example

Continuous deployment ensures repeatable, auditable releases. A simple pipeline stages include build, test, package, and deploy. Use the AWS SAM CLI, CloudFormation, or the Serverless Framework to describe infrastructure as code. Below is an outline for a GitHub Actions workflow:

• Trigger: push to main or pull request to main.
• Jobs: install dependencies, run tests (unit and integration), synthesize template (SAM or Serverless), package artifacts to S3, and deploy CloudFormation stack.
• Use AWS credentials stored in GitHub Secrets (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) scoped to a deploy role.

Example steps: npm test -> sam build -> sam package –s3-bucket $S3 -> sam deploy –stack-name my-app –capabilities CAPABILITY_IAM. Add a manual approval step for production deployments using environments protection rules in GitHub or a manual gate in your pipeline.

Testing, monitoring, and cost optimization

Automated tests should include unit tests for business logic, integration tests against a local or test AWS environment (localstack or a sandbox account), and end-to-end tests for API flows. For monitoring, enable CloudWatch Logs and set meaningful log levels. Configure CloudWatch Alarms on Lambda errors, function duration, and DynamoDB throttling. Use AWS X-Ray for distributed tracing to find latency and cold start issues.

Cost tips: prefer HTTP APIs over REST where features suffice, use on-demand DynamoDB for unpredictable usage or set up autoscaling for provisioned mode, and keep Lambda memory and timeout tuned to balance cost and performance. Cold starts can be mitigated by keeping functions warm or using provisioned concurrency for latency-sensitive endpoints.

Implementation checklist and example resources

• Define the API endpoints and data model (partition key, GSIs).
• Create IAM roles with least privilege for Lambda and API Gateway.
• Write small Lambdas and separate concerns (auth, validation, persistence).
• Use infrastructure as code: SAM, CloudFormation, or Serverless Framework.
• Set up GitHub Actions with secrets and deploy steps; include test jobs.
• Enable logging, alarms, and tracing for production observability.

Example cost/scale note: DynamoDB can serve millions of requests per second when properly partitioned; be mindful of hot keys. Lambdas can scale to thousands of concurrent executions but verify concurrency limits on your AWS account and request increases for production traffic.

Conclusion

Deploying a serverless web app with AWS Lambda, API Gateway, and DynamoDB delivers fast time-to-market, automatic scaling, and lower operational overhead. Use infrastructure as code and CI/CD to make releases consistent and safe. Focus on small, testable Lambdas, secure IAM policies, and monitoring to run a reliable production serverless web app.