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.
Manual ticket routing is a hidden tax on IT efficiency. Here is an architectural pattern for using Logistic Regression and Skype status APIs to automate this.
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.
Jakarta Data in Jakarta EE 12 M2 extends the EE 11 repository model with stateful operations, unified querying, and SQL/NoSQL alignment for domain-centric data access.
Jakarta Query unifies queries across Jakarta Persistence, Data, and NoSQL, with common and relational levels to simplify polyglot persistence in EE 12 M2.
Migrating legacy monolithic systems to the cloud is risky. Here is a proven pattern for automating regression testing at scale by replaying production traffic.
Learn context engineering to build better AI apps. This guide covers key techniques, practical examples, and resources to master this essential AI skill.
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.
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.