Category: Analytics

Learn how Datacor built a self-service rental analytics experience for gas and welding distributors by embedding Amazon Quick Sight dashboards and natural language querying into its TrackAbout platform, powered by an automated cross-cloud data pipeline and multi-tenant row-level security.

Learn how to build a fully serverless pipeline that automatically collects Git metrics from GitHub and GitLab and visualizes them in interactive Amazon Quick Sight dashboards, giving engineering teams near-real-time delivery analytics at low cost.

Learn how to embed individual Amazon Quick Sight visuals into a React application with per-user access control. This walkthrough uses Amazon Cognito authentication and a serverless AWS Lambda backend to generate scoped embed URLs, deployed with a single AWS CloudFormation stack.

In this post, you will learn how GoDaddy migrated from their legacy business intelligence (BI) tool to Amazon Quick. This was a two-year transformation that delivered results across every dimension of the business: 15,000 hours saved annually, 50% reduction in dashboard count, rendering times cut to under 5 seconds, and AI-powered self-service analytics now accessible to every employee.

Amazon OpenSearch Service now supports MCP Apps, which return interactive visualizations alongside your AI agent’s text responses. Learn how a single, locally run MCP server lets your agent move from alert to trace to logs to root cause in one conversation, and how you can verify every step inline without leaving your IDE.

In Part 3 of this no-code ML series, you bring fraud detection predictions to life. Import your Amazon SageMaker Canvas predictions into Amazon Quick Sight, build interactive dashboards, use generative BI to answer questions in natural language, and publish AI-generated executive summaries for stakeholders.

AWS offers a broad portfolio of vector search built directly into the databases and storage services you already use, with no standalone vector database or data migration required. This post covers six purpose-built services, a decision framework for choosing the right engine, and customer proof points for each.

Learn how to visualize and analyze Amazon Bedrock cost attribution using Amazon Athena and CUDOS dashboards. This post shows how to set up CUR 2.0 with IAM principal data, query Bedrock spend by principal, project, and team, and build dashboards to track AI costs across your organization.

Formula 1® partnered with AWS to build the Data Accelerator, using agentic AI on Amazon Bedrock AgentCore to transform its MarTech data platform. Learn how F1 cut data source onboarding from up to 8 weeks to about 40 minutes, automated schema evolution, and gained end-to-end observability across its fan-engagement data estate.

This post walks through deploying Kimi K3 on AWS using two approaches: Amazon SageMaker HyperPod, and  Amazon Elastic Kubernetes Service (Amazon EKS) cluster.