id: dwh-and-analytics
namespace: tutorial
labels:
  name: Data Warehouse and Analytics

tasks:
  - id: dbt
    type: io.kestra.plugin.core.flow.WorkingDirectory
    description: Run the dbt demo project in a dedicated working directory.
    tasks:
      - id: clone_repository
        type: io.kestra.plugin.git.Clone
        description: Clone the dbt demo repository.
        url: https://github.com/kestra-io/dbt-demo
        branch: main

      - id: dbt_build
        type: io.kestra.plugin.dbt.cli.DbtCLI
        description: Install dependencies and build the dbt project with DuckDB.
        taskRunner:
          type: io.kestra.plugin.scripts.runner.docker.Docker
        containerImage: ghcr.io/kestra-io/dbt-duckdb:latest
        commands:
          - dbt deps
          - dbt build
        profiles: |
          jaffle_shop:
            outputs:
              dev:
                type: duckdb
                path: dbt.duckdb
                extensions: 
                  - parquet
                fixed_retries: 1
                threads: 16
                timeout_seconds: 300
            target: dev      

      - id: python
        type: io.kestra.plugin.scripts.python.Script
        description: Export the dbt tables to CSV files for analysis.
        outputFiles:
          - "*.csv"
        taskRunner:
          type: io.kestra.plugin.scripts.runner.docker.Docker
        containerImage: ghcr.io/kestra-io/duckdb:latest
        script: |
          import duckdb
          import pandas as pd

          conn = duckdb.connect(database='dbt.duckdb', read_only=False)

          tables_query = "SELECT table_name FROM information_schema.tables WHERE table_schema = 'main';"

          tables = conn.execute(tables_query).fetchall()

          # Export each table to CSV, excluding tables that start with 'raw' or 'stg'

          for table_name in tables:
              table_name = table_name[0]
              # Skip tables with names starting with 'raw' or 'stg'
              if not table_name.startswith('raw') and not table_name.startswith('stg'):
                  query = f"SELECT * FROM {table_name}"
                  df = conn.execute(query).fetchdf()
                  df.to_csv(f"{table_name}.csv", index=False)

          conn.close()

description: |
  # Flow Description
  **Use case:** Data warehouse and analytics demo that builds a dbt project and exports modeled tables.
  **Highlights:**
  - Clone the dbt demo repository into an isolated working directory.
  - Run `dbt deps` and `dbt build` against DuckDB using the provided container image and profile.
  - Export modeled tables (excluding raw/stg) to CSV files via Python for downstream analysis.