{
  "id": "stack-profile:data-warehouse-bi",
  "_kind": "StackProfile",
  "_file": "domain/stack-profiles/deep-stacks-4.yaml",
  "_cluster": "domain",
  "attributes": {
    "displayName": "Data Warehouse / BI Stack (dbt, BigQuery, Metabase/Looker, Python, Airflow)",
    "description": "A modern data warehouse and business intelligence stack where Airflow\norchestrates ELT pipelines that land raw data into BigQuery, dbt\ntransforms it into dimensional models with tested, documented SQL,\nand Metabase or Looker provides self-serve dashboards and ad-hoc\nexploration for business stakeholders. Python scripts handle custom\ndata source connectors and quality checks. SQL is the primary\nmodeling language across the stack. Designed for data teams in\ngrowth-stage companies building their first production analytics\nlayer. The tradeoff is warehouse cost management — BigQuery's\non-demand pricing can spike unpredictably with poorly optimized\nqueries, requiring careful slot reservation planning and query\ngovernance as usage scales.\n",
    "composes": [
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      "language:python",
      "tool:airflow",
      "library:pandas",
      "tool:docker"
    ]
  },
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    {
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      "to": "workflow:dbt-model-review",
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    {
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    {
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    {
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      "kind": "requires_skill_area"
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    {
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  "incomingEdges": []
}