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Development

Featured in Development

How to Use Apache Spark to Craft a Multi-Year Data Regression Testing and Simulations Framework

Vivek Yadav, an engineering manager from Stripe, shares his experience in building a testing system based on multi-year worth of data. He shares insights into why Apache Spark was the choice for creating such a system and how it fits in the "traditional" engineering practices.

All in development

Architecture & Design

Featured in Architecture & Design

Where Architects Sit in the Era of AI

As AI evolves from tool to collaborator, architects must shift from manual design to meta-design. This article introduces the "Three Loops" framework (In, On, Out) to help navigate this transition. It explores how to balance oversight with delegation, mitigate risks like skill atrophy, and design the governance structures that keep AI-augmented systems safe and aligned with human intent.

All in architecture-design

AI Infrastructure

Featured in AI, ML & Data Engineering

Lessons Learned From Shipping AI-Powered Healthcare Products

Clara Matos discusses the journey of shipping AI-powered healthcare products at Sword Health. She explains how to implement input/output guardrails for regulated industries and shares a framework for robust evaluations using human and LLM-based ratings. From prompt engineering to RAG and user feedback loops, she shares a data-driven roadmap for building reliable AI care agents at scale.

All in ai-ml-data-eng

Culture & Methods

Featured in Culture & Methods

Empowering Teams: Decentralizing Architectural Decision-Making

Peter Hunter & Elena Stojmilova share Open GI's journey from a slow, legacy monolith to a cloud-native SaaS platform. They detail how adopting Team Topologies and a decentralized architectural approach empowered teams. Key practices discussed include utilizing Domain-Driven Design to create a Context Map, implementing the Advice Process with Architectural Principles, and more.

All in culture-methods

DevOps

Featured in DevOps

Overload Protection: the Missing Pillar of Platform Engineering

Overload protection is often overlooked in platform engineering, leaving teams to create inconsistent, fragile fixes. Centralized rate limits, quotas, adaptive controls, and clear visibility give services predictable ways to handle traffic spikes, reduce reliability debt, and prevent cascading failures across systems.

All in devops

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Topics

How to Use Apache Spark to Craft a Multi-Year Data Regression Testing and Simulations Framework

Where Architects Sit in the Era of AI

Lessons Learned From Shipping AI-Powered Healthcare Products

Empowering Teams: Decentralizing Architectural Decision-Making

Overload Protection: the Missing Pillar of Platform Engineering

Helpful links

Choose your language

Data-Oriented Programming

Magika 1.0: Smarter, Faster File Detection with Rust and AI

HL is a Fast, Rust-Based JSON Log Viewer Offering up to 2GiB/s Parsing Speed

Lessons Learned in Migrating to Micro-Frontends by Luca Mezzalira at QCon SF

AWS Expands Well-Architected Framework with Responsible AI and Updated ML and Generative AI Lenses

Where Architects Sit in the Era of AI

InfoQ Announces January Online Architect Cohort Focused on Socio-Technical Leadership

Lessons Learned from Migrating a Legacy Test Suite to Gauge with Kotlin

Empowering Teams: Decentralizing Architectural Decision-Making

Leading from Any Position: Richard Bown on Humane Engineering Organizations

QConAI NY 2025 - Designing AI Platforms for Reliability: Tools for Certainty, Agents for Discovery

Google Metrax Brings Predefined Model Evaluation Metrics to JAX

Decathlon Switches to Polars to Optimize Data Pipelines and Infrastructure Costs

Pinterest Engineering Reduces Android CI Build Times by 36% with Runtime-Aware Sharding

AWS Debuts “DevOps Agent” to Automate Incident Response and Improve System Reliability

Netflix Migrates to Amazon Aurora: 75% Performance Boost and 28% Cost Reduction

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Data-Oriented Programming