Data Pipeline Foundation. @data-pipeline-foundation

Most companies don't need a data strategy deck — they need their data in one governed place with dashboards people trust. The Data Pipeline Foundation builds that baseline: ingestion from your core systems, a modeled warehouse on Snowflake, Databricks, or your cloud-native stack, and a first set of dashboards answering the questions your team actually asks.

The pipelines are built as code — dbt for modeling, Airflow or Prefect for orchestration — with data quality checks and governance from the first table, because everything downstream (BI, ML, AI) inherits whatever quality the foundation has.

Source Ingestion

Reliable ELT from your core systems — product database, CRM, billing, events — into one warehouse, scheduled and monitored.

Warehouse & Modeling

A dbt-modeled warehouse with documented, tested models — not a pile of raw tables — on the platform that fits your stack and budget.

First Dashboards

Working dashboards in Tableau, Power BI, or Looker over the modeled data, built against real stakeholder questions.

Quality & Governance Baseline

Freshness and quality tests, access controls, and documentation — the guardrails that keep the platform trustworthy as it grows.

1. Scope — Data landscape assessment: source inventory, stakeholder interviews, and a fixed scope of sources, models, and dashboards.
2. Build — Provision the platform, build ingestion and models, and stand up the dashboards — with quality tests written alongside.
3. Handover — A documented, monitored platform handed to your team: runbooks, model docs, and a roadmap for what to build next.

The Data Pipeline Foundation is a menu item on the Scrums.com delivery catalog, available at every plan tier. Add it to your plan backlog and your delivery team schedules it like any other item — scoped, tracked, and reported through the SEOP. See Delivery Plan Tiers.

Which platform will we end up on?

The one that fits your stack: Snowflake, Databricks, or cloud-native (BigQuery, Redshift, Azure Synapse). The assessment phase makes the choice explicit, with cost and capability trade-offs documented.

Do we own the result?

Yes. Everything runs in your cloud accounts, as code, with documentation — no proprietary layer between you and your data, and no vendor lock-in.

What if we want to keep building after the foundation?

That's the designed path: add the Data & Analytics Pod to your plan backlog and the same platform grows against a metrics backlog — more sources, more models, ML-ready infrastructure.