Amazon QuickSight

2026-10-21

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS publishes walkthrough to connect Amazon Quick to on-premises data via PrivateLink

AWS released a walkthrough showing how to keep traffic between Amazon Quick (an AI-powered workspace) and on-premises data off the public internet by using VPC endpoints (AWS PrivateLink) for user/API access and Quick-configured VPC connections for data-source access. The guide assumes an existing AWS Direct Connect or Site-to-Site VPN and details which Quick features use each path, plus current data-source limitations (e.g., Redshift supported for Quick Index; many cloud file sources are not).

5.0

2026-10-02

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS publishes 'Adjudicated Query' pattern for provable lease compliance using Amazon Quick

AWS demonstrates an "Adjudicated Query" design pattern and reference architecture that combines a generative-AI chat surface (Amazon Quick) with a deterministic, versioned rules engine to run auditable, provably complete compliance sweeps (example: tens of thousands of leases). The architecture separates conversational translation/narration (model) from pass/fail determinations (rules engine), emits a completeness receipt, and shows an end-to-end deployable sample using MCP, Amazon Bedrock, Cognito, API Gateway, Lambda, and Aurora Serverless v2.

6.0

2026-10-01

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS documents Amazon Bedrock AgentCore multi-agent workflows and integrations for cloud migrations and agent workloads

An AWS technical post explains how AWS Professional Services uses a multi-agent framework built on Amazon Bedrock AgentCore to automate end-to-end enterprise cloud migrations and reduce infrastructure-as-code development time from weeks to minutes. The post also covers related how-tos and integrations — Live Data in Amazon QuickSight, using Amazon S3 Vectors as persistent memory with NVIDIA NeMo Agent Toolkit on EKS, ambient/ambient-agent patterns, Claude Platform configuration on AWS, personalization with SageMaker AI, fine-tuning Nova 2 Lite for uniopen, a Bedrock Knowledge Bases conversational claims assistant, and AgentCore Runtime Instances for colocated GPU agent workflows.

7.0

2026-09-25

COMPANIES · 1 SOURCE · AWS Machine Learning

Datacor embeds Amazon QuickSight generative BI into TrackAbout for self‑service rental analytics

Datacor integrated Amazon QuickSight (including its generative BI assistant, Amazon Q) into its TrackAbout asset-tracking product to provide interactive dashboards, natural-language querying, and a self-service analytics experience for rental billing. The implementation includes a cross-cloud ingestion pipeline, tenant isolation and validation controls, and is aimed at helping TrackAbout customers (who manage roughly 27.5 million assets and 725,000 bills/month) analyze rental revenue and run ad-hoc "what if" scenarios without IT support.

6.0

2026-09-21

COMPANIES · 1 SOURCE · AWS Machine Learning

BMW’s CLEA uses Prophet to detect cost anomalies across 14,000 cloud accounts

BMW Group’s in-house FinOps system, Cloud Efficiency Analytics (CLEA), ingests billing exports (including AWS CUR) and builds per-account-service daily forecasts with Prophet to run daily anomaly detection across roughly 14,000 cloud accounts. The serverless pipeline (Step Functions, Lambda, S3, Distributed Map) processes hundreds of thousands of account-service time series in about 20 minutes and alerts owners when actual spend departs from the forecast; the team reports the compute run costs around $50/month.

4.0

2026-09-17

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS publishes serverless Git-metrics dashboard solution using Amazon QuickSight

AWS released a serverless, event-driven pipeline that automatically collects Git metrics from GitHub and GitLab, stores results in Amazon S3, and visualizes analytics with Amazon QuickSight. The solution uses EventBridge Scheduler, Step Functions, Lambda, and CloudFormation parameters, and includes change detection, adaptive chunking, and full/incremental loads to provide near-real-time engineering analytics and support observability in the AI-Driven Development Lifecycle framework.

3.0