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Decoupling Azure Releases With GitHub Actions (Thu, 25 Dec 2025)
Cloud deployments often fail because environment configurations are hardcoded into the build process. Here is a pattern to decouple your Build Artifacts from your Deployment Logic using GitHub Actions and a flexible JSON Configuration map. In the world of Kubernetes, we are used to the separation of concerns: Docker builds the image, and Helm/Kustomize handles the environment configuration. However, when working with serverless (Azure Functions) or PaaS (App Service), developers often fall into the trap of monolithic pipelines. They build a package that only works in DEV, and then rebuild it for PROD.
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A Principled Framework for Scalable Experimentation and Reliable A/B Testing (Thu, 25 Dec 2025)
If you’ve ever shipped a feature and thought, “Did we actually make things better?”, you’re not alone. A/B testing is supposed to be our scientific answer to that question, but running good experiments takes more than sprinkling some feature flags and plotting a graph. In practice, many teams learn experimentation the hard way. They launch tests with unclear hypotheses, biased assignments, or underpowered sample sizes, only to discover weeks later that their results are inconclusive or misleading. This means going back to the drawing board, restarting experiments, and losing valuable time, a hit to both product velocity and team morale.
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Extracting Clean Excel Tables From PDFs Using Python + Docling (Thu, 25 Dec 2025)
PDFs remain the most widely used format for distributing structured reports — financial statements, regulatory filings, research documents, fund fact sheets, and more. Yet despite their structured appearance, PDFs are not machine-readable. Extracting tables reliably is famously error-prone and often requires hours of manual cleanup. This is especially true in finance and enterprise environments where analysts rely on Excel for modeling and reporting.
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AI-Driven Schema Evolution and Adaptive Pipelines (Thu, 25 Dec 2025)
The Modern Challenge of Schema Drift In dynamic enterprise ecosystems, schemas rarely stay static. APIs evolve, microservices introduce new fields, IoT devices emit data in new formats, and third-party providers alter payloads overnight. The result: schema drift — the silent killer of data reliability. A single schema mismatch can cascade through hundreds of tables, breaking joins, corrupting aggregates, and halting analytics pipelines. Traditional ETL frameworks rely on static definitions or manual version control to handle schema changes. But at today’s scale — millions of daily events, hundreds of interconnected datasets, dozens of teams — manual remediation simply doesn’t scale.
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DevOps Cafe Podcast

DevOps Cafe Ep 79 - Guests: Joseph Jacks and Ben Kehoe (Mon, 13 Aug 2018)
Triggered by Google Next 2018, John and Damon chat with Joseph Jacks (stealth startup) and Ben Kehoe (iRobot) about their public disagreements — and agreements — about Kubernetes and Serverless. 
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DevOps Cafe Ep 78 - Guest: J. Paul Reed (Mon, 23 Jul 2018)
John and Damon chat with J.Paul Reed (Release Engineering Approaches) about the field of Systems Safety and Human Factors that studies why accidents happen and how to minimize the occurrence and impact. Show notes at http://devopscafe.org
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DevOps Cafe Ep. 77 - Damon interviews John (Wed, 20 Jun 2018)
A new season of DevOps Cafe is here. The topic of this episode is "DevSecOps." Damon interviews John about what this term means, why it matters now, and the overall state of security.  Show notes at http://devopscafe.org
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