InfoQ Homepage Tools Content on InfoQ
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Reasoning about Complex Distributed Systems
Erich Ess discusses technical tools needed to gain information on a complex system and practical approaches to convert that information into an actual understanding of the system.
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More Reliable Delivery with Monte Carlo & Mapping
Conal Scanlon talks about how to use Monte Carlo simulations to remove the guesswork from planning, story mapping to discover the story up front, and reviews an example of how to automate a forecast.
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Development Metrics You Should Use But Don't
Cat Swetel discusses new ways and tools to visualize a team’s reliability and variability of delivery using the data already collected.
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Growing A Development Team’s Process Guided by Tests
Orta Therox introduces Danger, a tool for automating a team's conventions surrounding code review.
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API Testing with Code Libraries and Cucumber
Ole Lensmar discusses various ways and tools for testing web APIs, focusing on using Cucumber.
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An Introduction to Distributed Tracing and Zipkin
Adrian Cole overviews debugging latency problems using call graphs created by Zipkin and reviews the ecosystem, including tools to trace other languages and frameworks.
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Understand, Automate, and Collaborate for Development Speed with Microservices
Russ Miles discusses how to ensure proper collaboration between microservices teams using the Atomist suite of ChatOps tools and services.
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Automating at a Higher Level with Atomist
Jessica Kerr demonstrates the standard Atomist coordination and automation tools, plus how to program instant automation for a code and team.
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Data Preparation for Data Science: A Field Guide
Casey Stella presents a utility written with Apache Spark to automate data preparation, discovering missing values, values with skewed distributions and discovering likely errors within data.
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Machine Learning at Scale
Aditya Kalro discusses using large-scale data for Machine Learning (ML) research and some of the tools Facebook uses to manage the entire process of training, testing, and deploying ML models.
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Elastic Data Analytics Platform @Datadog
Doug Daniels discusses the cloud-based platform they have built at DataDog and how it differs from a traditional datacenter-based analytics stack, pros and cons and the tooling built.
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Architecting for Failure in a Containerized World
Tom Faulhaber discusses the new container-based toolbox for building systems that are robust in the face of failures, how to recover from failure and how the tools can be used to best effect.
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