Summary of AWS blogs for the week of Monday Jun 24
In the week of Mon Jun 24 2024 AWS published 80 blog posts – here is an overview of what happened.
Topics Covered
- AWS DevOps Blog
- AWS for SAP
- Official Machine Learning Blog of AWS
- Announcements, Updates, and Launches
- Containers
- Official Database Blog of AWS
- AWS for Games Blog
- AWS Training and Certification Blog
- Official Big Data Blog of AWS
- Networking & Content Delivery
- AWS for M&E Blog
- AWS Storage Blog
- AWS Architecture Blog
- AWS Partner Network (APN) Blog
- AWS HPC Blog
- AWS Cloud Operations & Migrations Blog
- AWS for Industries
- AWS Messaging & Targeting Blog
- AWS Marketplace
- The latest AWS security, identity, and compliance launches, announcements, and how-to posts.
- Innovating in the Public Sector
AWS DevOps Blog
GitHub Actions is a continuous integration and continuous deployment (CI/CD) platform that automates the build, test, and deployment processes for various workloads. One of its features, Self-Hosted Runners, allows organizations to execute these pipelines on their own infrastructure. This offers flexibility and customization, providing greater control over the build environments.
Introduction to GitHub Self-Hosted Runners
GitHub Self-Hosted Runners provide an alternative to GitHub’s cloud-hosted runners. By using self-hosted runners, teams can utilize their own hardware resources, enabling them to run jobs on specialized environments that are not available in GitHub-hosted environments. This is particularly beneficial for workloads that require unique software dependencies or hardware configurations.
Scaling Self-Hosted Runners on AWS
Running self-hosted runners at scale requires careful planning and architecture. AWS offers a range of services to facilitate this, including Amazon EC2 for scalable compute resources, AWS Auto Scaling for dynamic scaling, and AWS Systems Manager for managing the runner instances. By leveraging these services, organizations can efficiently manage the lifecycle of self-hosted runners, ensuring they are cost-effective and performant.
Best Practices
When deploying self-hosted runners on AWS, several best practices should be followed:
- Automation: Use AWS CloudFormation or AWS CDK to automate the provisioning and management of the runners. This ensures consistency and reduces manual effort.
- Security: Implement strict IAM policies to control access to runner instances. Use AWS Secrets Manager to manage sensitive information like GitHub tokens.
- Monitoring: Set up comprehensive monitoring using Amazon CloudWatch to track performance and detect issues early.
- Cost Management: Use AWS Auto Scaling to scale runner instances based on demand, and consider using Spot Instances to reduce costs.
How Cloud2 Can Help
Cloud2, the leading AWS consultancy in Denmark, can assist organizations in setting up and managing self-hosted GitHub Action runners on AWS. Our expertise in AWS automation, security best practices, and cost optimization ensures that your CI/CD pipelines are scalable, secure, and efficient. Whether you need help with initial setup or ongoing management, Cloud2’s team of AWS experts is here to support your DevOps journey.
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AWS for SAP
SAP Convergent Mediation (SAP CM) by DigitalRoute is an integral part of the SAP Billing and Revenue Innovation Management (SAP BRIM) solution. It enables customers to track and orchestrate their billing processes efficiently. Deploying SAP CM on AWS can significantly enhance its availability and scalability through the use of AWS Auto Scaling.
Benefits of SAP CM on AWS
By leveraging AWS Auto Scaling, businesses can ensure their SAP CM deployment adjusts seamlessly to fluctuating workloads. This adaptability not only optimizes resource usage but also minimizes downtime, ensuring continuous monitoring and billing operations. Furthermore, AWS’s global infrastructure enhances the reliability and performance of SAP CM deployments.
How AWS Auto Scaling Works
AWS Auto Scaling automatically adjusts the number of EC2 instances in a deployment based on predefined policies and real-time metrics. This dynamic scaling ensures that the system handles peak loads efficiently and scales down during low demand periods, reducing costs. Integration with CloudWatch allows for real-time monitoring and automatic scaling actions triggered by custom metrics.
Implementation Steps
To implement AWS Auto Scaling for SAP CM, start by defining the scaling policies based on historical workload patterns and performance indicators. Next, configure CloudWatch to monitor key metrics such as CPU usage, memory consumption, and network throughput. Finally, set up Auto Scaling groups that will launch or terminate instances as needed to maintain optimal performance.
Business Value
Implementing AWS Auto Scaling for SAP CM can lead to significant cost savings by tailoring resource usage to actual demand. Enhanced availability ensures that billing processes are uninterrupted, leading to more efficient revenue management. This scalability also supports business growth, allowing SAP CM deployments to handle increasing workloads without manual intervention.
How Cloud2 Can Help
Cloud2 offers expert guidance and support in deploying and optimizing SAP CM on AWS. Our team can assist in designing auto-scaling policies, setting up CloudWatch monitoring, and configuring Auto Scaling groups to ensure your deployment is resilient and cost-effective. With extensive experience in both AWS and SAP solutions, Cloud2 can help you achieve maximum performance and reliability for your billing operations.
Read the full blog posts from AWS
Official Machine Learning Blog of Amazon Web Services
NinjaTech AI aims to enhance productivity by handling complex tasks with AI agents. They launched MyNinja.ai, a multi-agent AI assistant capable of scheduling meetings, conducting web research, generating code, and aiding in writing. These AI agents work autonomously and asynchronously, learning from past experiences to improve future performance, enabling users to focus on higher-priority tasks.
Building Generative AI Applications on Amazon Bedrock
Amazon Bedrock is a managed service offering access to large language models (LLMs) and foundational models from leading AI companies. It simplifies the integration of generative AI into applications, providing a secure and compliant foundation for creating text, images, audio, and code. This service is ideal for developers and businesses looking to harness the power of generative AI.
Creating Conversational Chatbots with Multiple LLMs
Generative AI allows foundation models to generate diverse content types. Choosing the right model involves selecting from various providers like Amazon, Anthropic, AI21 Labs, Cohere, and Meta. These models can work with different data formats, making them versatile for various applications, such as answering questions and summarizing text.
Automating Derivative Confirms Processing in Capital Markets
Using AWS AI services, one can automate the processing of derivative confirms at scale. This solution leverages Amazon Textract to extract text and data from scanned documents and AWS Serverless technologies for seamless integration and management of applications without the need for server management. It streamlines workflows and enhances operational efficiency in