AWS Monthly (Sep ’25): Vega OS & eBPF Observability
In a surprise move, AWS released Vega OS in September. Vega is a specialized, Linux-based OS optimized for the edge and high-performance UI rendering (with a React Native core). It’s ultra-lightweight and designed to boot in milliseconds, perfect for the next generation of smart devices. On the observability side, we got CloudWatch Application Map 2.0,…
AWS Monthly (Aug ’25): Big Data, Zero Effort
August was all about “Data Gravity.” Amazon Aurora storage limits jumped to 256 TiB, which effectively ends the “sharding” conversation for 99.9% of companies. However, the real star was the expansion of Zero-ETL Integrations. AWS enabled seamless, near-real-time replication from Aurora to OpenSearch and RDS to Redshift. By leveraging the database’s internal transaction logs, AWS…
AWS Monthly (July ’25): Kubernetes at the Edge of Sanity
July was a landmark month for the EKS (Elastic Kubernetes Service) crowd. AWS announced that EKS now supports 100,000-node clusters. While 100k nodes might be overkill for most of us, the technical optimizations required to make the Kubernetes control plane stable at that scale benefit everyone. The scheduler is faster, and ETCD performance is more…
AWS Monthly (June ’25): S3 Becomes Your Vector DB
June saw a tectonic shift in the AI data stack with the preview of Amazon S3 Vector Search. For the last two years, we have been told we need a specialized vector database (Pinecone, Milvus, etc.) for Retrieval-Augmented Generation (RAG). AWS simplified the equation: “Just store your vectors in S3.” Technically, this adds a native…
AWS Monthly (May ’25): The Death of the War Room
May brought CloudWatch Investigations, which have fundamentally changed someone’s on-call rotation. Instead of manual log correlation, this service uses AI to perform Automated Root Cause Analysis (RCA). When an alarm triggers, Investigations automatically traces the error. It correlates metric spikes with concurrent events—like a specific Git commit, a Terraform apply, or an RDS parameter change.…
Automating AWS CloudWatch Log Group Tagging with Python and Boto3
Managing tags for AWS CloudWatch log groups is crucial for operational visibility, cost management, and effective resource organization. Tagging log groups manually can be cumbersome, especially when dealing with a large number of log groups. This article outlines a straightforward method to automate this task using Python and the AWS SDK for Python (Boto3). Importance…
Automating AWS ECR Tagging with Python and Boto3
Proper tagging of AWS resources is critical for efficient resource management, cost allocation, and auditing. If you have numerous AWS Elastic Container Registry (ECR) repositories, manual tagging can be tedious and error-prone. This article provides a simple and effective solution: automating the tagging of ECR repositories using Python and the AWS SDK for Python (Boto3).…
Automating ECR Image Cleanup with Bash
Managing container images in Amazon ECR (Elastic Container Registry) is crucial for keeping your registry clean and cost-effective. Over time, unused or deprecated images can accumulate, potentially leading to increased storage costs and operational overhead. One common scenario is removing images that follow a specific tagging pattern—in this case, any image tagged with versions following…
Update ECR Repositories with Bash Script
Below is an example Bash script that uses the AWS CLI to retrieve all your Amazon ECR repositories and then sets the image tag mutability of each repository to MUTABLE. Before running the script, ensure you have the AWS CLI installed and configured with appropriate permissions. Explanation This script automates the process of ensuring all…