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Topics

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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Learn how leading engineering teams run AI in production—reliably, securely, and at scale.

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InfoQ Homepage Interviews Ville Tuulos on Big Data and Map/Reduce in Erlang and Python with Disco

Ville Tuulos on Big Data and Map/Reduce in Erlang and Python with Disco

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16:28

Bio

Ville Tuulos is a researcher with Nokia Research in Palo Alto and has been working with large data sets since 1999, building solutions for statistical information retrieval. He started Disco, an Erlang/Python implementation of the Map/Reduce framework for distributed computing, now used by Nokia and others for prototyping of data-intensive software with hundreds of gigabytes of real-world data.

About the conference

The Erlang Factory is an event that focuses on Erlang - the computer language that was designed to support distributed, fault-tolerant, soft-realtime applications with requirements for high availability and high concurrency. The main part of the Factory is the conference - a two-day collection of focused subject tracks with an enormous opportunity to meet the best minds in Erlang and network with experts in all its uses and applications.

Jun 24, 2011

Interview with

Ville Tuulos

by

Sadek Drobi

BT