InfoQ Homepage Architecture Content on InfoQ
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Decoupled APIs through Microservices
David Simons introduces microservices as a developer's API tool, discussing why and when makes sense to use them, and the tools that make it easy to deal with a microservices architecture.
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Journey from Data Integration to Data Science
Michael Wise discusses the journey from having data integrated across an organization, to employing data science to make good use of it.
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Smashing the Monolith
Leonard Garvey and Louis Simoneau discuss how to decompose a monolith, architectural and integration patterns to avoid creating a monolith, and useful patterns and tools along the way.
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The Architecture that Helps Stripe Move Faster
Evan Broder talks about how Stripe has designed the systems to speed up the development process and how the software infrastructure in their API enables the next tech companies to build faster.
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Scaling Container Architectures with OSS & Mesos
David Greenberg discusses how Two Sigma was able to scale up their research to harness tens of thousands of CPUs and the challenges faced.
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Scaling Uber to 1,000 Services
Matt Ranney talks about Uber’s growth and how they’ve embraced microservices. This has led to an explosion of new services, crossing over 1,000 production services in early March 2016.
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Apache Beam: The Case for Unifying Streaming APIs
Andrew Psaltis talks about Apache Beam, which aims to provide a unified stream processing model for defining and executing complex data processing, data ingestion and integration workflows.
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DigitalOcean: Microservices in Your Datacenter
Phil Calçado talks about the patterns and techniques DigitalOcean has used over the years to migrate from a monolithic architecture to SOA and microservices.
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Business Process Orchestration & APIs
Saul Caganoff discusses the different use cases for API consumption and the technical affordances API designers can provide to support those use cases.
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Containers Change Everything
Anne Currie talks about the architectural impact of containers, and what modern container schedulers mean for resilience, redundancy and server density.
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Future of Container-Enabled Infrastructure
Brandon Philips describes how bringing containers, schedulers, and distributed systems together will create more reliable and greatly more trusted server infrastructures.
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Monitoring and Troubleshooting Real-Time Data Pipelines
Alan Ngai and Premal Shah discuss best practices on monitoring distributed real-time data processing frameworks and how DevOps can gain control and visibility over these data pipelines.