Build a Data Analyst AI Agent in 3 Mins
AWS AI Data Analyst: Query Your Data in Plain English! #shorts
Want to build an AI agent that turns plain English into SQL and charts? Here's the full AWS architecture in 3 minutes.
1️⃣Storage layer: Parquet in S3 + AWS Glue catalog + Athena for cheap SQL.
2️⃣Semantic layer: Dataset description JSONs → Bedrock embeddings → Amazon S3 Vectors so the agent finds the right table by meaning, not filename.
3️⃣Frontend: React on S3 + CloudFront + WAF, with Cognito + Amplify for auth.
4️⃣Brain: Amazon Bedrock AgentCore orchestrates the flow, Claude Opus interprets the rows and writes chart code.
Why this stack matters: no real database to maintain, pay-per-query economics, and S3 Vectors makes semantic search affordable for small teams. Perfect reference architecture for internal analytics agents.
#AWS #ArtificialIntelligence #DataEngineering #AmazonBedrock #GenerativeAI #CloudArchitecture #Serverless #AmazonAthena #AWSGlue #ClaudeOpus #DevOps #TechTips #DataAnalytics #ReactJS
Want to build an AI agent that turns plain English into SQL and charts? Here's the full AWS architecture in 3 minutes.
1️⃣Storage layer: Parquet in S3 + AWS Glue catalog + Athena for cheap SQL.
2️⃣Semantic layer: Dataset description JSONs → Bedrock embeddings → Amazon S3 Vectors so the agent finds the right table by meaning, not filename.
3️⃣Frontend: React on S3 + CloudFront + WAF, with Cognito + Amplify for auth.
4️⃣Brain: Amazon Bedrock AgentCore orchestrates the flow, Claude Opus interprets the rows and writes chart code.
Why this stack matters: no real database to maintain, pay-per-query economics, and S3 Vectors makes semantic search affordable for small teams. Perfect reference architecture for internal analytics agents.
#AWS #ArtificialIntelligence #DataEngineering #AmazonBedrock #GenerativeAI #CloudArchitecture #Serverless #AmazonAthena #AWSGlue #ClaudeOpus #DevOps #TechTips #DataAnalytics #ReactJS
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