How cloud-native microservices transform insurance analytics by enabling scalability, real-time processing, and seamless modernization of legacy platforms.
A clear-eyed breakdown of serverless costs — why they’re hidden, when they make sense, and how to choose between functions and containers before surprises hit your bill.
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.
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.
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