Official technical community of Amazon Cloud. Shares practical AI/ML, big data, database, modern app development, IoT content, offers comprehensive learning resources, hosts regular developer events, and continuously empowers developers.
This article walks through using Kiro AI IDE to develop an Amazon EMR Flink monitoring system, detailing spec‑driven development, MCP integration, steering rules, the full backend and frontend stack, and shows how the workflow cuts implementation time from 60–80 hours to about 10 hours, delivering a 6–8× efficiency gain.
A performance comparison of Amazon EMR Serverless Storage on a 3 TB TPC‑DS benchmark shows up to 55 % cost reduction and 25 % faster runtimes for shuffle‑intensive Spark jobs, while outlining usage limits and providing Python tools to analyze shuffle data from Spark event logs.
The article explains how traditional Flink monitoring suffers from scattered metrics, manual root‑cause analysis, and lack of actionable advice, and introduces a cloud‑native system built on Strands Agents and Amazon Bedrock that automatically collects metrics, performs LLM‑powered analysis, generates optimization recommendations, and interacts with users via natural‑language dialogue and real‑time streaming output.
This article presents a rigorous, multi‑dimensional evaluation of the RAG‑MCP framework versus a full‑tool MCP approach on Amazon Bedrock, showing up to 74% token reduction, higher tool‑selection accuracy, lower latency, and better scalability for large tool sets.
Using Amazon Global Accelerator, NLB, and Direct Connect can cause asymmetric routing that blocks UDP traffic, but adding a NAT gateway and adjusting route tables creates symmetric paths, restores bidirectional UDP communication, and scales with multi‑IP support, albeit with added cost.
Generative Business Intelligence combines large language models with traditional BI to let users query, analyze, and visualize data through natural language, and Amazon offers three distinct solutions—QuickSight, Redshift, and a Bedrock‑based RetailBI—each tailored to different technical levels and business scenarios.
This article walks through using Amazon OpenSearch Service’s vector search and ML connector together with the DeepSeek large language model to create a low‑cost, high‑efficiency enterprise knowledge base, covering architecture, step‑by‑step deployment, RAG pipeline configuration, and conversational search extensions.
The article examines why single‑agent ChatBI solutions fall short in enterprise settings and presents a three‑layer, multi‑agent architecture—interaction, orchestration, and execution—built with Amazon Quick Suite, Amazon Bedrock AgentCore, and Snowflake Cortex AI, detailing routing, synchronous/asynchronous processing, semantic modeling, and deployment recommendations.
The article presents an end‑to‑end automated solution for AWS resource tagging that scans all accounts, discovers services, identifies resources missing specified tags, and batch‑applies them, while offering configurable workflows, performance optimizations, permission requirements, and optional AI‑driven MCP integration for smarter governance.
The article explains how Amazon SageMaker's Sticky Session routing creates session affinity, allowing KV cache reuse across requests, which eliminates redundant computation, reduces latency, and improves memory efficiency for multi‑turn LLM applications.
