Integration & Automation
Automating server creation with EC2 Image Builder and AWS Systems Manager: A collaboration between AWS and Ziff Davis
In this post, AWS and Ziff Davis collaborated to tackle server management challenges by implementing an automated solution using EC2 Image Builder and AWS Systems Manager. The solution addressed issues of inconsistent configurations, undocumented processes, and manual patching, resulting in improved efficiency, consistency, and disaster recovery capabilities for Ziff Davis’s virtual phone number services.
Optimize message delivery to third-party services using AWS Lambda and AWS Step Functions
In this post, we demonstrate how to optimize message delivery to third-party services using AWS Lambda and AWS Step Functions by implementing adaptive scaling strategies based on throttling patterns. We describe how YOOX, a leading online fashion retailer, improved their product update delivery system by using AWS Serverless services to achieve a 95% success rate within 10 minutes of job start while maintaining efficient message processing.
Monitor AWS Backup Vault Lock compliance across your organization
In this post, we show how to implement automated reporting for AWS Backup Vault Lock status across accounts in your organization. The solution uses AWS Organizations, the AWS Command Line Interface (AWS CLI), and cross-account access to create vault lock compliance reports.
Automate security compliance and remediation across organizations
In this post, we highlight how Fortra partnered with AWS to develop an innovative approach for automating security compliance checks and remediation across complex, multi-account, multi-Region organizations.
Enhance customer experience with an integrated AI assistant
In this post, we demonstrate how to build an enterprise AI assistant solution that uses LLMs in Amazon Bedrock with the precision of enterprise knowledge bases using the RAG approach. By integrating AWS services such as Lambda and Amazon Bedrock, our solution enables organizations to securely access and retrieve proprietary data, providing contextually relevant and accurate responses. The RAG approach not only enhances the assistant’s ability to provide tailored responses within specific enterprise data domains, but also mitigates the risk of hallucinations. By injecting the latest enterprise proprietary knowledge into the response generation context, our solution makes sure that the assistant remains up-to-date and adaptable to evolving specific business needs. The sample code repository and CloudFormation template can enable organizations to streamline the development and deployment of their RAG-based AI assistant solutions.
Updating your AWS Elastic Disaster Recovery settings at scale with the DRS Settings Tool
Deploying AWS Elastic Disaster Recovery at scale provides robust protection for your infrastructure. The DRS Settings Tool is an invaluable resource for updating settings across your infrastructure efficiently. In this post, we show you how to setup and use the DRS Settings Tool to update all your Elastic Disaster Recovery source server settings in bulk.
A practical guide to getting started with policy as code
In this post, we detail the concepts, processes, and steps to get started with policy as code (PaC) and adopt this into your software development lifecycle. PaC can improve your overall security posture, improve consistency of service usage across your organization, and reduce rework or workloads deployed to your AWS accounts.
Automate security scans on Amazon EKS with Kubescape, AWS CodeBuild, and AWS CodePipeline
As organizations increasingly adopt Amazon Elastic Kubernetes Service (Amazon EKS) to manage their containerized applications, implementing robust security measures and maintaining compliance become critical. The scalable and flexible nature of Amazon EKS has made it a popular choice for businesses seeking to streamline their application deployment and management processes. However, with this adoption comes the […]
Build an automated deployment of generative AI with agent lifecycle changes using Terraform
This blog post guides you through implementing a generative AI intelligent chatbot using Amazon Bedrock and AWS services, including developer lifecycle changes. It covers creating a scalable chatbot infrastructure with Terraform, using services like Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, OpenSearch Service, Lambda, Amazon S3, and IAM. The focus on IaC and automation brings key DevOps benefits: version control, consistent deployments, reduced errors, faster provisioning, and improved collaboration.
Build workflows-as-code automations for AWS services using Flowpipe
Learn about Turbot’s cloud scripting engine Flowpipe along with practical examples for automating cloud operations on AWS.