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Orchestration: DAGs, Retries and Backfills

An orchestrator runs tasks in dependency order, retries what fails, and lets you re-run history without corrupting it. The scheduling is the easy half. The half that decides whether a deployment survives is whether tasks are idempotent, because an orchestrator's whole value is that it will run things more than once.

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RELATED CONCEPTS

Idempotent Data Pipelines ETL vs ELT Data Quality and Validation Retries, Exponential Backoff and Jitter Observability for AI Systems

LESSONS THAT TEACH THIS

Foundations of Forward Deployed EngineeringMake replays safe without overwriting newer facts30 min→The FDE EngagementMake replay, quarantine and reconciliation part of the pipeline contract30 min→

PRACTICE THIS IN REAL QUESTIONS

MLOps & ML EngineeringDesign an automated retraining pipeline. What should trigger retraining?→Coding & DSABuild a client for a flaky API: retries with exponential backoff and jitter, timeouts, and idempotency→SQL & Data EngineeringDesign a daily pipeline that survives reruns, late-arriving data, and a 3-year backfill, without double-counting.→ML Infrastructure & GPUsDesign a GPU scheduling system that allocates a shared cluster across competing training and inference workloads.→RAG & Agent System DesignSingle tool-loop, planner-executor, or multi-agent: how do you choose an agent architecture?→RAG & Agent System DesignLangChain, LlamaIndex, LangGraph, what are the real differences, and when would you use each?→