Byte-Sized Wisdom·6d agoHow to Prepare for Data Engineer Interviews at OpenAI/Anthropic: A Practical Technical GuideIn the previous blog, I discussed what to expect when preparing for Data Engineering roles at companies like Anthropic and OpenAI.
InWomen in TechnologybyGangarapu Meghana·Oct 7Abort, skip, or quarantine: handling bad rows in bulk loadsBreaking Changes, Part 1 of 4 — schema evolution in a real warehouse migration.
Baheldeepti·Oct 3Why Most Data Engineering Portfolio Projects Fail Before the First Line of CodeA practical lesson in scope, data modeling, and engineering judgment from building KidneyLensA response icon1A response icon1
Gangarapu Meghana·Sep 2510 Things I’d Do Differently Before Building Another Data PipelinePart 2: what happens when it runs — and the checklistA response icon1A response icon1
suparnababu·Sep 11Auto-Magic Data Ingestion In Microsoft Fabric Lakehouse Using Data Activator & PipelinesIn this blog, I’ll walk you through a step-by-step implementation of a solution that monitors file uploads to a Lakehouse and transforms…
Devdas Pawar·Jul 27A Practical Framework for Designing Scalable Data Pipelines (8 Steps)The 8-step blueprint I follow when building data pipelines:
Gopi krishnan·Jun 24Building a Zero-Data-Copy HR EcosystemA High-Performance Technical Blueprint for Real-Time Workforce Intelligence Without Cross-Cloud Data Duplication
Priyanka Shelar·May 9CDC at Enterprise Scale: Lessons from Managing 55TB of Healthcare Supply Chain DataBy Priyanka Shelar, Data Engineering | NYU MS Computer Science
Lumina·Apr 10Designing for Failure: Idempotency in Data PipelinesPipelines fail. It’s not a matter of if — it’s a matter of when. A network blip, an out-of-memory crash, a timeout from a downstream API…A response icon1A response icon1
Leo Grochoski·Mar 31Things that break data pipelines in production (and almost nobody talks about).Key real-world problems in pipelines:
