Category: AWS Lambda

The ability to invoke Lambda functions in response to Amazon RDS for SQL Server database events enables powerful use cases such as triggering automated workflows, sending real-time notifications, calling external APIs, and orchestrating complex business processes. In this post, we demonstrate how to enable this integration by using Amazon CloudWatch subscription filters, Amazon SQS, and Amazon SNS to invoke Lambda functions from RDS for SQL Server stored procedures, helping you build responsive, data-driven applications.

In this post, I show you how to build a serverless workflow orchestration engine that uses Amazon DynamoDB and AWS Lambda. The complete implementation is available in a GitHub repository, which includes two fully functional examples that you can deploy and run immediately to see the orchestration engine in action.

In this three-part series, we explore approaches to implement enhanced event-driven patterns for DynamoDB-backed applications. In this post (Part 2), we explore another method which uses global secondary indexes (GSIs) to handle fine-grained Time to Live (TTL) requirements.

In this three-part series, we explore approaches to implement enhanced event-driven patterns for DynamoDB-backed applications. In this post (Part 1), we focus on improving DynamoDB’s native TTL functionality by implementing near real-time data eviction using EventBridge Scheduler, reducing the typical time to delete expired items from within a few days to less than one minute.

In this post, we walk through a solution that automates the notification of Amazon RDS and Aurora recommendations through email using AWS Lambda, Amazon EventBridge and Amazon Simple Email Service (Amazon SES).

In this post, we show how to capture and retain failed stream events for later analysis or replay using Amazon S3 as a durable destination. We compare this approach with the traditional Amazon SQS dead-letter queue (DLQ) pattern, and explain when and why Amazon S3 is a preferred option.

In this post, we explore a streamlined solution that uses AWS Lambda and Python to read and ingest CSV data into an existing Amazon DynamoDB table. This approach adheres to organizational security restrictions, supports infrastructure as code (IaC) for table management, and provides an event-driven process for ingesting CSV datasets into DynamoDB.

In this post, we discuss integer sequence overflow, its causes, and—most importantly—how to efficiently set up alerts using Amazon SNS and use AWS Lambda to resolve such issues in Amazon Aurora PostgreSQL-Compatible Edition and Amazon RDS for PostgreSQL.

In this post, we demonstrate how to set up an event-driven workflow to run stored procedures for Amazon RDS for PostgreSQL with AWS Lambda to bridge this gap by securely connecting to an Aurora PostgreSQL database using AWS Secrets Manager, making sure that stored procedures can be managed in the cloud. We explore the step-by-step process, discuss the advantages of this approach, and address the limitations of invoking stored procedures from Lambda functions.

By combining DynamoDB streams with Lambda, you can build responsive, scalable, and cost-effective systems that automatically react to data changes in real time. In this post, we explore best practices for architecting event-driven systems using DynamoDB and Lambda. DynamoDB provides two options for capturing data changes (CDC): DynamoDB streams and Amazon Kinesis Data Streams (KDS). In this post, we focus exclusively on DynamoDB streams.