This hub covers the shifting economics of managed vs. custom data pipelines. Four notes move from Fivetran’s pricing changes through AI-assisted development with dlt to the hybrid decision framework.
Reading Order
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Fivetran MAR Pricing Shift — The catalyst. Fivetran’s March 2025 shift to per-connector MAR pricing eliminated bulk discounts, producing 4-8x cost increases for many teams. Marketing data, with its constant retroactive updates, gets hit hardest.
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Build vs. Buy Data Pipeline Economics — The convergence. Three independent shifts — pricing unpredictability, measured AI development velocity (55.8% faster with Copilot), and dlt’s production maturity (3M monthly downloads) — compound to flip the traditional calculus.
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dlt for AI-Assisted Pipeline Development — The how. dlt’s Python-native, declarative design maps well to AI-assisted development. The REST API builder, BigQuery-specific features, and LLM-friendly documentation make the “build” option practical. Includes the workflow pattern and production results (Artsy: 98% improvement, 96% cost savings).
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Hybrid ELT Strategy — The decision framework. When buying still wins (compliance, non-technical teams, connector breadth, urgency), when building wins (high-MAR, custom, control), and the step-by-step migration path starting with your most expensive connector.
Related
- Advertising Data in the Warehouse — If your build-vs-buy question is specifically about ad platform data, this hub covers the full journey from extraction through transformation.
- Ad Data Extraction Tools — A reference comparison of managed, open-source, and native integration options for ad data specifically.
- AI Production Gap in Data Engineering — The limitations of AI-assisted development that affect the “build” option: security, compliance, and the gap between demo and production.
- BigQuery Cost Model — Understanding BigQuery’s cost model helps optimize the pipelines you build, particularly around streaming vs. batch loading and partition pruning.