Jakarta EE 12 aligns repositories, restrictions, queries, ORM, and NoSQL into a unified data model, making domain-centric data access a first-class platform feature.
A new volume type has recently joined the Kubernetes ecosystem: the image volume. This feature promises to change how we manage static data and configurations.
We rebuilt a failing activation stack as a governed platform using Segment, Databricks, and Iterable, reducing incidents and enabling safer self-service.
Use a query router for LLM analytics — Redshift (KPIs), OpenSearch (definition), Neptune (lineage), and Cache (repeats) — to improve accuracy, latency, and costs.
GPU-as-a-Service makes it easier to share accelerators, but it also raises concerns about isolation and security. This introduces a secure Kubernetes architecture.
Migrating legacy monolithic systems to the cloud is risky. Here is a proven pattern for automating regression testing at scale by replaying production traffic.
Cloud cost is a distributed systems failure mode. This article explains how to make it observable, prevent waste, and manage spend as an operational metric.
This article explains how to build a self-healing observability system with AWS Bedrock AgentCore using AI agents to analyze and remediate infrastructure issues.
NetOps teams often face a skills gap when troubleshooting complex infrastructure. This article presents an automation pattern for an AI co-pilot for incident response.
MCP is production-ready for LLM-to-tool integration; A2A enables emerging multi-agent collaboration. They complement, not compete, and neither replaces Spark or Airflow.
A guide to eight AI agent types with implementations, real-world use cases, and selection framework. Learn about LCM, HRM, LAM, SLM, VLM, LRM, MOE, and GPT architectures.
Single sign-on plays an important role in enhancing the security of your application. Let's deep dive into implementing SSO in an Angular-based web application.
Active Directory is the heartbeat of the enterprise, and a favorite target of attackers. Here is an architectural pattern for AI-driven anomaly detection and remediation.
Hashing detects tampering, but it doesn't prevent it. Here is an architectural pattern for securing business-critical files using Amazon QLDB and the Symbol Blockchain.