Category: Artificial Intelligence

Agentic Data Ingestion is an open-source solution that uses AI agents on Amazon Bedrock to automate data pipeline creation, from file upload to queryable tables. Point it at a file or database table, and foundation models propose the field mapping, generate and test the AWS Glue transform, and run the job behind human approval gates.

Governed, multi-tenant data access is a key requirement for SaaS and B2C products that embed AI-powered analytics. This post shows how to combine Amazon Cognito and AWS Lake Formation on Amazon Bedrock AgentCore to enforce row- and column-level data access for external users, with a minimal-privilege Lambda acting as a credential broker.

Amazon Aurora PostgreSQL zero-ETL integration with Amazon SageMaker replicates your operational data to a lakehouse in near real time, without building custom ETL pipelines. Learn the architecture and change data capture mechanics, then set up the integration and query your data in Amazon SageMaker.

AI agents can reach enterprise data where it lives instead of routing every question through data engineers. This post presents three reference patterns for federated data access using Model Context Protocol (MCP) servers and Amazon Bedrock AgentCore: catalog-first, direct source, and hybrid access.

With Apache Iceberg materialized views in Amazon SageMaker, you can build a Bronze, Silver, and Gold medallion architecture as three SQL statements. This declarative approach folds transformation, orchestration, and incremental processing into per-layer definitions, with no ETL jobs, orchestrators, or change-data-capture code to maintain.

In this post, you build a contract compliance search system that combines semantic search with semantic highlighting in Amazon OpenSearch Service. You deploy the solution using two AWS CloudFormation stacks, test it with synthetic contract documents, and see how a single query surfaces both the right contracts and the right clauses within them.

This post focuses on explaining the architecture approach to build the open lakehouse architecture on AWS, unifying the metadata catalog across providers for the AI agents to access. In addition, it highlights the architecture trade-offs and best practices.

In this post, we explore how MDAA transforms data architecture development from months of manual coding to production-ready deployment through configuration-driven infrastructure and embedded governance, examine a real customer transformation, and provide a clear implementation pathway for your own data modernization journey.

In this post, you learn how to build an AI-powered solution that collects the telemetry, pre-computes performance signals, correlates them with CloudWatch, and uses Amazon Bedrock to generate prioritized recommendations.

In this post, you learn how tombola followed a strict engineering principle: no changes to production without evidence. That meant a head-to-head comparison of RA3 versus RG on their actual workload. You also see benchmark results on Amazon S3 Tables and the migration from RA3 to RG instances.