Overview
Most data teams reach a point where their pipelines are a tangle of shell scripts, Kettle transformations, and bespoke schedulers — each with its own retry behavior and observability story. Pipeline modernization replaces that surface area with a small set of well-understood primitives: Airflow for orchestration, dbt for transformation, and your warehouse of choice as the system of record.
Reference Architecture
flowchart TB subgraph Before["Before"] direction LR Cron[Cron Jobs] --> Kettle[Kettle PDI] Kettle --> Shell[Shell Scripts] Shell --> Warehouse1[(Warehouse)] end subgraph After["After"] direction LR Airflow[Apache Airflow] --> Dbt[dbt Project] Dbt --> Warehouse2[(Warehouse)] Airflow -.-> Observability[Logs / Metrics / Alerts] end Before -.->|Phased Migration| After
Engagement Model
Modernizations are inherently risky — we run new and legacy pipelines in parallel and reconcile their output until the team has full confidence in the new system, then retire the old surface area in a single decisive cut-over.