AWS Architecture Blog

Category: Compute

Architecting offline-first generative AI applications for edge deployments using AWS services

According to Siemens’ 2024 report The True Cost of Downtime, Fortune 500 companies lose an estimated $1.4 trillion annually because of unplanned downtime. This downtime is often worsened by a lack of skills to detect and resolve issues quickly. Generative AI offers a promising path to address this, but deploying these capabilities in industrial environments […]

Eclipse Dataspace Components on AWS: Cost optimization strategies

When you deploy Eclipse Dataspace Components (EDC) connectors on AWS, one of the first challenges you face is predicting and controlling the cost of the required infrastructure. Without clear benchmarks, it is difficult to make informed decisions about workload sizing, environment configuration, and long-term investment. Part 1 of this 3-part blog series covered the fundamentals […]

Eclipse Dataspace Components on AWS: Architecture patterns in production

Running Eclipse Dataspace Components (EDC) connectors in production on AWS requires deliberate architecture decisions around isolation, managed services, and security layering. In Part 1 of this series, we covered the fundamentals of data space architectures and EDC per the International Data Space Association’s (IDSA) standards. If you are new to EDC, we recommend starting there. […]

Eclipse Dataspace Components on AWS: Data sharing fundamentals

This three-part blog series guides you through implementing Eclipse Dataspace Components (EDC) on AWS, from foundational concept to production deployment. Part 1 establishes the theoretical foundation with IDSA standards, the Dataspace Protocol (DSP), and core EDC architecture. Part 2 provides production-ready AWS deployment patterns using services like Amazon Elastic Container Service (Amazon ECS), Amazon Aurora, […]

How Mapfre Insurance modernized fraud claims with Amazon EMR Serverless

Insurance fraud remains a significant challenge for the insurance industry because fraudulent claims can increase loss costs, reduce trust, and consume investigation capacity that could otherwise be focused on serving customers. Traditional fraud detection approaches typically rely on rules-based controls, manual investigation triggers, historical claim patterns, and structured-data-only analysis. These approaches are useful for known […]

Specification-driven composition for flexible data workflows

Specification-driven composition addresses a common scalability bottleneck in data pipelines. Data pipelines often start as simple scripts, but as they grow, you duplicate transformation logic and small changes cascade across multiple workflows. Copying and modifying data transformation logic across scripts leads to workflows that become difficult to manage at scale. Tracking what each pipeline does […]

Modernizing financial analytics with Amazon SageMaker Unified Studio

Avanse Financial Services, India’s leading education loan providers, migrated to a cloud-native lakehouse architecture using Amazon SageMaker Unified Studio, which unified their data engineering, analytics, and artificial intelligence (AI) workflows in a single governed environment on AWS. In this post, we walk through their migration journey so you can adapt their approach to your own environment.