motherduck-build-data-pipeline

artifacts/pipeline_stage_example.py

import json
import sys
import tempfile
from pathlib import Path

import duckdb

sys.path.append(str(Path(__file__).resolve().parents[3]))

from scripts._lib.motherduck_artifact_utils import artifact_session


def fetch_rows(conn: duckdb.DuckDBPyConnection, sql: str) -> list[dict]:
    cursor = conn.execute(sql)
    columns = [col[0] for col in cursor.description]
    return [dict(zip(columns, row)) for row in cursor.fetchall()]


def sql_string(value: str) -> str:
    return "'" + value.replace("'", "''") + "'"


def main() -> None:
    with artifact_session(
        slug="motherduck-build-data-pipeline",
        database_keys=["raw", "staging", "analytics"],
    ) as session:
        conn = session.conn
        raw_table = session.table("raw", "main", "orders_landing")
        staging_table = session.table("staging", "main", "orders_deduped")
        analytics_table = session.table("analytics", "main", "daily_revenue")

        with tempfile.TemporaryDirectory(prefix="md_pipeline_stage_") as tmpdir:
            parquet_path = Path(tmpdir) / "orders_landing.parquet"
            conn.execute(
                """
                CREATE TEMP TABLE stage_orders_extract AS
                SELECT *
                FROM (
                  VALUES
                    (1, 101, DATE '2026-03-01', 120.0, TIMESTAMP '2026-03-01 10:00:00'),
                    (1, 101, DATE '2026-03-01', 120.0, TIMESTAMP '2026-03-01 12:00:00'),
                    (2, 102, DATE '2026-03-02', 75.0, TIMESTAMP '2026-03-02 09:00:00'),
                    (3, 103, DATE '2026-03-03', 210.0, TIMESTAMP '2026-03-03 11:00:00')
                ) AS source_rows(order_id, customer_id, order_date, total_amount, updated_at)
                """
            )
            conn.execute(
                f"""
                COPY stage_orders_extract
                TO {sql_string(str(parquet_path))}
                (FORMAT PARQUET)
                """
            )
            conn.execute(
                f"""
                CREATE TABLE {raw_table} AS
                SELECT *
                FROM read_parquet({sql_string(str(parquet_path))})
                """
            )

        conn.execute(
            f"""
            CREATE OR REPLACE TABLE {staging_table} AS
            WITH ranked AS (
              SELECT *,
                     ROW_NUMBER() OVER (
                       PARTITION BY order_id
                       ORDER BY updated_at DESC
                     ) AS row_num
              FROM {raw_table}
            )
            SELECT order_id, customer_id, order_date, total_amount
            FROM ranked
            WHERE row_num = 1
            """
        )

        conn.execute(
            f"""
            CREATE OR REPLACE TABLE {analytics_table} AS
            SELECT
              order_date,
              COUNT(*) AS order_count,
              SUM(total_amount) AS total_revenue,
              AVG(total_amount) AS avg_order_value
            FROM {staging_table}
            GROUP BY 1
            ORDER BY 1
            """
        )

        result = {
            "backend": session.describe(),
            "ingestion_mode": "bulk_parquet_stage",
            "stages": {
                "raw": fetch_rows(conn, f"SELECT COUNT(*) AS row_count FROM {raw_table}"),
                "staging": fetch_rows(conn, f"SELECT COUNT(*) AS row_count FROM {staging_table}"),
                "analytics": fetch_rows(conn, f"SELECT * FROM {analytics_table}"),
            },
        }
        print(json.dumps(result, indent=2, default=str))


if __name__ == "__main__":
    main()

artifacts/pipeline_stage_example.ts

export {};
declare const process: { env: Record<string, string | undefined> };

type RawOrder = {
  order_id: number;
  customer_id: number;
  order_date: string;
  total_amount: number;
  updated_at: string;
};

function normalizeMetadataValue(value: string | undefined, fallback: string): string {
  const raw = (value ?? "").trim();
  if (!raw) return fallback;
  const normalized = raw.replace(/[^A-Za-z0-9._-]+/g, "-").replace(/^[-._]+|[-._]+$/g, "");
  return normalized || fallback;
}

function buildUseCaseUserAgent(): string {
  const harness = normalizeMetadataValue(process.env.MOTHERDUCK_AGENT_HARNESS, "unknown");
  const llm = normalizeMetadataValue(process.env.MOTHERDUCK_AGENT_LLM, "unknown");
  return `agent-skills/3.0.0(harness-${harness};llm-${llm})`;
}

const rawRows: RawOrder[] = [
  { order_id: 1, customer_id: 101, order_date: "2026-03-01", total_amount: 120.0, updated_at: "2026-03-01T10:00:00" },
  { order_id: 1, customer_id: 101, order_date: "2026-03-01", total_amount: 120.0, updated_at: "2026-03-01T12:00:00" },
  { order_id: 2, customer_id: 102, order_date: "2026-03-02", total_amount: 75.0, updated_at: "2026-03-02T09:00:00" },
  { order_id: 3, customer_id: 103, order_date: "2026-03-03", total_amount: 210.0, updated_at: "2026-03-03T11:00:00" },
];

const latestByOrder = new Map<number, RawOrder>();
for (const row of rawRows) {
  const existing = latestByOrder.get(row.order_id);
  if (!existing || existing.updated_at < row.updated_at) {
    latestByOrder.set(row.order_id, row);
  }
}
const stagingRows = Array.from(latestByOrder.values()).sort((a, b) => a.order_id - b.order_id);

const analyticsMap = new Map<string, { order_count: number; total_revenue: number }>();
for (const row of stagingRows) {
  const current = analyticsMap.get(row.order_date) ?? { order_count: 0, total_revenue: 0 };
  current.order_count += 1;
  current.total_revenue += row.total_amount;
  analyticsMap.set(row.order_date, current);
}
const analyticsRows = Array.from(analyticsMap.entries())
  .map(([order_date, value]) => ({
    order_date,
    order_count: value.order_count,
    total_revenue: value.total_revenue,
    avg_order_value: value.total_revenue / value.order_count,
  }))
  .sort((a, b) => a.order_date.localeCompare(b.order_date));

const result = {
  backend: {
    mode: "typescript-companion",
    databases: { raw: "raw", staging: "staging", analytics: "analytics" },
    user_agent: buildUseCaseUserAgent(),
  },
  ingestion_mode: "bulk_parquet_stage",
  stages: {
    raw: [{ row_count: rawRows.length }],
    staging: [{ row_count: stagingRows.length }],
    analytics: analyticsRows,
  },
};

console.log(JSON.stringify(result, null, 2));

references/dlt-dbt-motherduck-project/.env.example

MOTHERDUCK_TOKEN=your_motherduck_token
MOTHERDUCK_PIPELINE_DB=md_skills_pipeline_demo

references/dlt-dbt-motherduck-project/.gitignore

.venv/
.dlt/
logs/
target/
dbt_packages/
__pycache__/

references/dlt-dbt-motherduck-project/.user.yml

id: 8b4fc291-de2c-4770-a0d4-ce6a83c0ac44

references/dlt-dbt-motherduck-project/data/customers.jsonl

{"customer_id":"c1","customer_name":"Acme Rockets","segment":"enterprise","region":"north_america"}
{"customer_id":"c2","customer_name":"Birch Analytics","segment":"mid_market","region":"emea"}
{"customer_id":"c3","customer_name":"Cedar Logistics","segment":"enterprise","region":"apac"}

references/dlt-dbt-motherduck-project/data/orders.jsonl

{"order_id":"o1001","customer_id":"c1","order_date":"2026-01-05","status":"processing","amount":"120.00","updated_at":"2026-01-05T09:00:00Z"}
{"order_id":"o1001","customer_id":"c1","order_date":"2026-01-05","status":"paid","amount":"125.00","updated_at":"2026-01-05T11:00:00Z"}
{"order_id":"o1002","customer_id":"c1","order_date":"2026-01-12","status":"paid","amount":"30.00","updated_at":"2026-01-12T15:30:00Z"}
{"order_id":"o1003","customer_id":"c2","order_date":"2026-01-14","status":"paid","amount":"75.00","updated_at":"2026-01-14T08:45:00Z"}
{"order_id":"o1004","customer_id":"c3","order_date":"2026-01-20","status":"paid","amount":"200.00","updated_at":"2026-01-20T18:00:00Z"}
{"order_id":"o1005","customer_id":"c2","order_date":"2026-01-22","status":"cancelled","amount":"50.00","updated_at":"2026-01-22T10:00:00Z"}

references/dlt-dbt-motherduck-project/dbt_project.yml

name: "md_pipeline_demo"
version: "1.0.0"
config-version: 2
profile: "md_pipeline_demo"

model-paths: ["models"]
macro-paths: ["macros"]
clean-targets: ["target", "dbt_packages"]

models:
  md_pipeline_demo:
    staging:
      +materialized: view
      +schema: staging
    marts:
      +materialized: table
      +schema: analytics

references/dlt-dbt-motherduck-project/macros/generate_schema_name.sql

{% macro generate_schema_name(custom_schema_name, node) -%}
  {%- if custom_schema_name is none -%}
    {{ target.schema }}
  {%- else -%}
    {{ custom_schema_name | trim }}
  {%- endif -%}
{%- endmacro %}

references/dlt-dbt-motherduck-project/models/marts/fct_customer_revenue.sql

select
  customers.customer_id,
  customers.customer_name,
  customers.segment,
  customers.region,
  count(orders.order_id) as order_count,
  sum(orders.amount) as total_amount,
  max(orders.order_date) as last_order_date
from {{ ref("stg_customers") }} as customers
join {{ ref("stg_orders") }} as orders
  on customers.customer_id = orders.customer_id
group by 1, 2, 3, 4

references/dlt-dbt-motherduck-project/models/marts/marts.yml

version: 2

models:
  - name: fct_customer_revenue
    columns:
      - name: customer_id
        data_tests:
          - unique
          - not_null
      - name: order_count
        data_tests:
          - not_null
      - name: total_amount
        data_tests:
          - not_null

references/dlt-dbt-motherduck-project/models/staging/sources.yml

version: 2

sources:
  - name: raw
    schema: raw
    tables:
      - name: customers_raw
      - name: orders_raw

references/dlt-dbt-motherduck-project/models/staging/staging.yml

version: 2

models:
  - name: stg_customers
    columns:
      - name: customer_id
        data_tests:
          - unique
          - not_null
  - name: stg_orders
    columns:
      - name: order_id
        data_tests:
          - unique
          - not_null
      - name: customer_id
        data_tests:
          - not_null
          - relationships:
              arguments:
                to: ref('stg_customers')
                field: customer_id

references/dlt-dbt-motherduck-project/models/staging/stg_customers.sql

select
  cast(customer_id as varchar) as customer_id,
  cast(customer_name as varchar) as customer_name,
  cast(segment as varchar) as segment,
  cast(region as varchar) as region
from {{ source("raw", "customers_raw") }}

references/dlt-dbt-motherduck-project/models/staging/stg_orders.sql

with ranked_orders as (
    select
        cast(order_id as varchar) as order_id,
        cast(customer_id as varchar) as customer_id,
        cast(order_date as date) as order_date,
        upper(trim(cast(status as varchar))) as status,
        cast(amount as decimal(18,2)) as amount,
        cast(updated_at as timestamp) as updated_at,
        row_number() over (
            partition by cast(order_id as varchar)
            order by cast(updated_at as timestamp) desc
        ) as row_num
    from {{ source("raw", "orders_raw") }}
)

select
    order_id,
    customer_id,
    order_date,
    status,
    amount,
    updated_at
from ranked_orders
where row_num = 1
  and status = 'PAID'

references/dlt-dbt-motherduck-project/pipeline/__init__.py

references/dlt-dbt-motherduck-project/pipeline/bootstrap.py

from __future__ import annotations

import duckdb

if __package__ in (None, ""):
    import sys
    from pathlib import Path

    sys.path.append(str(Path(__file__).resolve().parents[1]))

from pipeline.settings import USER_AGENT, load_settings

SCHEMAS = ("raw", "staging", "analytics")


def bootstrap_database() -> None:
    settings = load_settings()

    workspace = duckdb.connect(
        "md:",
        config={
            "motherduck_token": settings.token,
            "custom_user_agent": USER_AGENT,
        },
    )
    try:
        workspace.execute(f'CREATE DATABASE IF NOT EXISTS "{settings.database}"')
    finally:
        workspace.close()

    target = duckdb.connect(
        f"md:{settings.database}",
        config={
            "motherduck_token": settings.token,
            "custom_user_agent": USER_AGENT,
        },
    )
    try:
        for schema in SCHEMAS:
            target.execute(f'CREATE SCHEMA IF NOT EXISTS "{schema}"')
    finally:
        target.close()

    print(
        f"Bootstrapped MotherDuck database '{settings.database}' with schemas: "
        + ", ".join(SCHEMAS)
    )


if __name__ == "__main__":
    bootstrap_database()

references/dlt-dbt-motherduck-project/pipeline/cleanup.py

from __future__ import annotations

import duckdb

if __package__ in (None, ""):
    import sys
    from pathlib import Path

    sys.path.append(str(Path(__file__).resolve().parents[1]))

from pipeline.settings import USER_AGENT, load_settings


def cleanup_database() -> None:
    settings = load_settings()

    workspace = duckdb.connect(
        "md:",
        config={
            "motherduck_token": settings.token,
            "custom_user_agent": USER_AGENT,
        },
    )
    try:
        workspace.execute(f'DROP DATABASE IF EXISTS "{settings.database}"')
    finally:
        workspace.close()

    print(f"Dropped MotherDuck database '{settings.database}'.")


if __name__ == "__main__":
    cleanup_database()

references/dlt-dbt-motherduck-project/pipeline/load_raw.py

from __future__ import annotations

import json
from pathlib import Path
from typing import Any, Iterator

import dlt
from dlt.destinations import motherduck

if __package__ in (None, ""):
    import sys

    sys.path.append(str(Path(__file__).resolve().parents[1]))

from pipeline.settings import USER_AGENT, data_dir, load_settings


def read_jsonl(path: Path) -> Iterator[dict[str, Any]]:
    with path.open("r", encoding="utf-8") as handle:
        for line in handle:
            stripped = line.strip()
            if stripped:
                yield json.loads(stripped)


@dlt.resource(name="customers_raw")
def customers_raw() -> Iterator[dict[str, Any]]:
    yield from read_jsonl(data_dir() / "customers.jsonl")


@dlt.resource(name="orders_raw")
def orders_raw() -> Iterator[dict[str, Any]]:
    yield from read_jsonl(data_dir() / "orders.jsonl")


def load_raw_data() -> None:
    settings = load_settings()

    pipeline = dlt.pipeline(
        pipeline_name="md_skills_dlt_dbt_reference",
        destination=motherduck(
            {
                "database": settings.database,
                "password": settings.token,
                "custom_user_agent": USER_AGENT,
            }
        ),
        dataset_name="raw",
    )

    info = pipeline.run(
        [customers_raw(), orders_raw()],
        write_disposition="replace",
    )
    print(info)


if __name__ == "__main__":
    load_raw_data()

references/dlt-dbt-motherduck-project/pipeline/run_all.py

from __future__ import annotations

import os
import shutil
import subprocess
import sys
from pathlib import Path

if __package__ in (None, ""):
    sys.path.append(str(Path(__file__).resolve().parents[1]))

from pipeline.bootstrap import bootstrap_database
from pipeline.load_raw import load_raw_data
from pipeline.settings import project_root


def resolve_dbt_binary() -> str:
    dbt_binary = shutil.which("dbt")
    if dbt_binary:
        return dbt_binary

    candidate = Path(sys.executable).resolve().with_name("dbt")
    if candidate.exists():
        return str(candidate)

    raise RuntimeError("dbt executable not found. Run `uv sync --python 3.12` first.")


def run_dbt_build() -> None:
    env = os.environ.copy()
    env["DBT_PROFILES_DIR"] = str(project_root())
    subprocess.run(
        [resolve_dbt_binary(), "build"],
        check=True,
        cwd=project_root(),
        env=env,
    )


def run_validation() -> None:
    subprocess.run(
        [sys.executable, "pipeline/validate.py"],
        check=True,
        cwd=project_root(),
        env=os.environ.copy(),
    )


def main() -> None:
    bootstrap_database()
    load_raw_data()
    run_dbt_build()
    run_validation()


if __name__ == "__main__":
    main()

references/dlt-dbt-motherduck-project/pipeline/settings.py

from __future__ import annotations

import os
import re
import sys
from dataclasses import dataclass
from pathlib import Path

REPO_ROOT = Path(__file__).resolve().parents[5]
if str(REPO_ROOT) not in sys.path:
    sys.path.append(str(REPO_ROOT))

from scripts._lib.motherduck_user_agent import build_use_case_user_agent

DEFAULT_DATABASE = "md_skills_pipeline_demo"
DATABASE_RE = re.compile(r"^[A-Za-z_][A-Za-z0-9_]*$")
USER_AGENT = build_use_case_user_agent()


@dataclass(frozen=True)
class Settings:
    token: str
    database: str


def project_root() -> Path:
    return Path(__file__).resolve().parents[1]


def data_dir() -> Path:
    return project_root() / "data"


def load_settings() -> Settings:
    token = os.environ.get("MOTHERDUCK_TOKEN")
    if not token:
        raise RuntimeError("Missing env var: MOTHERDUCK_TOKEN")

    database = os.environ.get("MOTHERDUCK_PIPELINE_DB", DEFAULT_DATABASE)
    if not DATABASE_RE.match(database):
        raise RuntimeError(
            "MOTHERDUCK_PIPELINE_DB must match ^[A-Za-z_][A-Za-z0-9_]*$"
        )

    return Settings(token=token, database=database)

references/dlt-dbt-motherduck-project/pipeline/validate.py

from __future__ import annotations

from datetime import date
from decimal import Decimal

import duckdb

if __package__ in (None, ""):
    import sys
    from pathlib import Path

    sys.path.append(str(Path(__file__).resolve().parents[1]))

from pipeline.settings import USER_AGENT, load_settings

EXPECTED_SUMMARY = [
    ("c1", "Acme Rockets", "enterprise", "north_america", 2, Decimal("155.00"), date(2026, 1, 12)),
    ("c2", "Birch Analytics", "mid_market", "emea", 1, Decimal("75.00"), date(2026, 1, 14)),
    ("c3", "Cedar Logistics", "enterprise", "apac", 1, Decimal("200.00"), date(2026, 1, 20)),
]


def validate_pipeline() -> None:
    settings = load_settings()
    database = f'"{settings.database}"'

    conn = duckdb.connect(
        f"md:{settings.database}",
        config={
            "motherduck_token": settings.token,
            "custom_user_agent": USER_AGENT,
        },
    )
    try:
        counts = conn.sql(
            f"""
            SELECT
                (SELECT count(*) FROM {database}."raw"."customers_raw") AS raw_customers,
                (SELECT count(*) FROM {database}."raw"."orders_raw") AS raw_orders,
                (SELECT count(*) FROM {database}."staging"."stg_orders") AS staged_orders,
                (SELECT count(*) FROM {database}."analytics"."fct_customer_revenue") AS mart_rows
            """
        ).fetchone()
        assert counts == (3, 6, 4, 3), counts

        summary = conn.sql(
            f"""
            SELECT
                customer_id,
                customer_name,
                segment,
                region,
                order_count,
                total_amount,
                last_order_date
            FROM {database}."analytics"."fct_customer_revenue"
            ORDER BY customer_id
            """
        ).fetchall()
        assert summary == EXPECTED_SUMMARY, summary
    finally:
        conn.close()

    print("Validation passed.")
    for row in EXPECTED_SUMMARY:
        print(row)


if __name__ == "__main__":
    validate_pipeline()

references/dlt-dbt-motherduck-project/profiles.yml

md_pipeline_demo:
  outputs:
    dev:
      type: duckdb
      path: "md:{{ env_var('MOTHERDUCK_PIPELINE_DB', 'md_skills_pipeline_demo') }}?motherduck_token={{ env_var('MOTHERDUCK_TOKEN') }}&custom_user_agent=agent-skills/3.0.0(harness-{{ env_var('MOTHERDUCK_AGENT_HARNESS', 'unknown') | replace(' ', '-') }};llm-{{ env_var('MOTHERDUCK_AGENT_LLM', 'unknown') | replace(' ', '-') }})"
      schema: main
      threads: 1
  target: dev

references/dlt-dbt-motherduck-project/pyproject.toml

[project]
name = "dlt-dbt-motherduck-project"
version = "0.1.0"
description = "Minimal end-to-end MotherDuck pipeline reference using dlt and dbt."
readme = "README.md"
requires-python = ">=3.11,<3.14"
dependencies = [
    "dbt-duckdb==1.10.1",
    "dlt[motherduck]==1.24.0",
    "duckdb==1.5.1",
]

[tool.uv]
package = false

references/dlt-dbt-motherduck-project/README.md

# dlt + dbt + MotherDuck Reference Project

This is a minimal end-to-end pipeline reference for the `motherduck-build-data-pipeline` skill.

## Contents

| Section | Covers |
| --- | --- |
| Why One Database | Database layout for the example |
| Verified Constraints | What the project was verified against |
| Files | Project layout |
| Run It | Commands to run the pipeline |
| Expected Output | What a successful run prints |
| Real MotherDuck Test Posture | Temporary databases and cleanup |

It captures the pipeline shape that this repo repeatedly verified against real MotherDuck runs:

- `dlt` for the raw loading step
- `dbt-duckdb` for staging and analytics modeling
- Python validation and cleanup around the workflow

The example is deliberately small and fully runnable:

- `dlt` lands raw JSONL data in MotherDuck
- `dbt` models staging and analytics relations in the same MotherDuck database
- Python validation checks the final outputs

## Why One Database

The main skill recommends separate lifecycle stages. For this reference project, the simplest runnable shape is one MotherDuck database with explicit schemas:

- `raw`
- `staging`
- `analytics`

That keeps the dbt project small and avoids extra attach configuration. When the pipeline grows and stage boundaries matter operationally, split the stages into separate MotherDuck databases and use dbt `attach`.

## Verified Constraints

These are based on a real local run against MotherDuck:

- Bootstrap the MotherDuck database before `dlt` runs. The `motherduck` destination does not create the target database for you.
- Use Python 3.11 or 3.12 for this stack. `dbt-duckdb` did not run correctly on Python 3.14 in this environment.
- Keep `dbt` concurrency at `threads: 1` for a small MotherDuck project like this.
- Override `generate_schema_name` so dbt uses exact schema names instead of `main_<schema>`.
- Run post-build validation in a fresh process. A long-lived local DuckDB process may not immediately see schemas written by a separate `dbt` subprocess.

## Files

- `pipeline/bootstrap.py`: creates the MotherDuck database and schemas
- `pipeline/load_raw.py`: loads raw data into MotherDuck with `dlt`
- `pipeline/run_all.py`: runs bootstrap, load, dbt build, and validation
- `pipeline/validate.py`: asserts row counts and final mart output
- `dbt_project.yml`, `profiles.yml`, `models/`, `macros/`: dbt project
- `data/*.jsonl`: tiny input dataset

## Run It

Set credentials:

```bash
export MOTHERDUCK_TOKEN=...
export MOTHERDUCK_PIPELINE_DB=md_skills_pipeline_demo
```

Install dependencies with a supported Python:

```bash
uv sync --python 3.12
```

Run the whole pipeline:

```bash
uv run python pipeline/run_all.py
```

Drop the temporary MotherDuck database when you are done:

```bash
uv run python pipeline/cleanup.py
```

Run the steps individually if you want to inspect them:

```bash
uv run python pipeline/bootstrap.py
uv run python pipeline/load_raw.py
DBT_PROFILES_DIR=. uv run dbt build
uv run python pipeline/validate.py
```

## Expected Output

After a successful run, the final mart contains three rows:

| customer_id | customer_name     | order_count | total_amount |
|-------------|-------------------|-------------|--------------|
| c1          | Acme Rockets      | 2           | 155.00       |
| c2          | Birch Analytics   | 1           | 75.00        |
| c3          | Cedar Logistics   | 1           | 200.00       |

The staging model also proves two common pipeline patterns:

- deduplicate by latest `updated_at`
- filter analytics output to `PAID` orders only

## Real MotherDuck Test Posture

This project is intended to run against a real MotherDuck database, not just local DuckDB.

- use a temporary `MOTHERDUCK_PIPELINE_DB` for validation runs
- bootstrap the database first
- run validation after `dbt build`
- drop the temporary database with `pipeline/cleanup.py` after the run

references/dlt-dbt-motherduck-project/uv.lock

version = 1
revision = 3
requires-python = ">=3.11, <3.14"

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references/EXECUTION_REFERENCE.md

# Execution Reference

Read this for example execution or an explicit structured-output request. These fixtures illustrate the pattern; they are not the user’s dataset or a prerequisite for ordinary work.

## Structured Output

If the caller explicitly asks for structured JSON, return raw JSON only with no Markdown fences or prose before/after it.
This is mainly for automated tests, regression checks, or downstream tooling that needs a stable machine-readable shape. Normal human-facing use of the skill can stay in prose unless JSON is explicitly requested.

Use this exact top-level shape when JSON is requested:

```json
{
  "summary": {},
  "assumptions": [],
  "implementation_plan": [],
  "validation_plan": [],
  "risks": []
}
```

## Runnable Artifact

- `artifacts/pipeline_stage_example.py` -- MotherDuck-backed Python example that stages a Parquet extract, lands it into raw, deduplicates it, and publishes analytics output across raw/staging/analytics databases
- `artifacts/pipeline_stage_example.ts` -- TypeScript companion artifact with the same stage layout and output contract
- `references/dlt-dbt-motherduck-project/` -- end-to-end MotherDuck example that bootstraps the target database, lands raw data with `dlt`, builds staging and analytics models with `dbt`, and validates the final mart

From the repository root, run it with (for an installed skill, substitute its absolute artifact path):

```bash
uv run --with duckdb python skills/motherduck-build-data-pipeline/artifacts/pipeline_stage_example.py
```

Run the same stage pattern against temporary MotherDuck databases:

```bash
MOTHERDUCK_ARTIFACT_USE_MOTHERDUCK=1 \
uv run --with duckdb python skills/motherduck-build-data-pipeline/artifacts/pipeline_stage_example.py
```

From a checkout of this repository, validate the TypeScript companion artifacts:

```bash
uv run scripts/test_typescript_artifacts.py
```

For the full MotherDuck project:

```bash
cd skills/motherduck-build-data-pipeline/references/dlt-dbt-motherduck-project
export MOTHERDUCK_TOKEN=...
export MOTHERDUCK_PIPELINE_DB=md_skills_pipeline_demo
uv sync --python 3.12
uv run python pipeline/run_all.py
uv run python pipeline/cleanup.py
```

## Verified Notes

- Bootstrap the target MotherDuck database before running `dlt`. The `motherduck` destination does not create the database for you.
- Use Python 3.11 or 3.12 to reproduce this reference project; its tested `dbt-duckdb` path did not run reliably on Python 3.14.
- If you want exact schema names like `raw`, `staging`, and `analytics` in dbt, override `generate_schema_name`.
- When a long-lived Python process loads data and a separate `dbt` subprocess builds models, run post-build validation in a fresh process or refresh database state before reading new relations.

references/PIPELINE_IMPLEMENTATION_GUIDE.md

<!-- Preserved detailed implementation guidance moved from SKILL.md so the main skill can stay concise. -->


# Build a Data Pipeline with MotherDuck

Use this skill when designing an end-to-end workflow that moves data from raw sources through transformation stages into analytics-ready output. This is a use-case skill -- it ties together lower-level skills into a complete pipeline.

## Contents

- [Source Of Truth](#source-of-truth)
- [Language Focus: TypeScript/Javascript and Python](#language-focus-typescriptjavascript-and-python)
- [TypeScript/Javascript Orchestration Starter](#typescriptjavascript-orchestration-starter)
- [Prerequisites](#prerequisites)
- [Runnable Reference Project](#runnable-reference-project)
- [Verified Delivery Defaults](#verified-delivery-defaults)
- [Validation Signals](#validation-signals)
- [Pipeline Architecture](#pipeline-architecture)
- [Step 1: Design the Target Schema](#step-1-design-the-target-schema)
- [Step 2: Ingest Raw Data into Raw](#step-2-ingest-raw-data-into-raw)
- [Step 3: Promote Into Staging and Write Transformation Queries](#step-3-promote-into-staging-and-write-transformation-queries)
- [Step 4: Materialize Analytics Tables](#step-4-materialize-analytics-tables)
- [Step 5: Validate Data Quality](#step-5-validate-data-quality)
- [Step 6: Serve Results](#step-6-serve-results)
- [Incremental Load Patterns](#incremental-load-patterns)
- [Complete Pipeline Example](#complete-pipeline-example)
- [Scheduling Considerations](#scheduling-considerations)
- [Key Rules](#key-rules)
- [Common Mistakes](#common-mistakes)
- [Related Skills](#related-skills)

## Source Of Truth

- Prefer current MotherDuck loading, connection, tagging, and storage docs first.
- If the MotherDuck MCP `ask_docs_question` feature is available, use it before falling back to public docs.
- Keep the pipeline guidance aligned with the documented posture:
  - batch over streaming
  - raw landing before curation
  - Parquet and bulk paths over row-by-row inserts
  - native MotherDuck storage first unless DuckLake is explicitly required

## Language Focus: TypeScript/Javascript and Python

- Prefer **Python** as the default language for pipeline implementation:
  - ingestion jobs
  - transformation runners
  - notebook validation
  - orchestration glue
- Prefer **TypeScript/Javascript** when the pipeline connects directly to:
  - backend services
  - event ingestion APIs
  - product-side control planes
- If the user asks for implementation code, bias toward Python unless their existing stack is clearly Node.js.

## TypeScript/Javascript Orchestration Starter

For Node.js pipelines, prefer the native DuckDB path when you need any of these:

- local-file ingestion
- extension-backed reads
- hybrid local and remote execution
- tighter control over DuckDB behavior

Use the PG endpoint only when the pipeline already lives in a PostgreSQL-driver environment and the work is limited to server-side SQL against MotherDuck-managed data or remote object reads.

Native DuckDB path for Node.js:

```ts
import { DuckDBInstance } from "@duckdb/node-api";
import { readFile } from "node:fs/promises";

const instance = await DuckDBInstance.create(
  "md:?custom_user_agent=agent-skills/3.0.0(harness-<harness>;llm-<llm>)"
);
const conn = await instance.connect();
for (const file of ["01_ingest.sql", "02_transform.sql", "03_publish.sql"]) {
  await conn.run(await readFile(`sql/pipeline/${file}`, "utf8"));
}
conn.close();
```

PG endpoint path for existing PostgreSQL-driver stacks:

```ts
import pg from "pg";
import { readFile } from "node:fs/promises";

const client = new pg.Client({
  host: "pg.us-east-1-aws.motherduck.com",
  port: 5432,
  database: "staging",
  user: "postgres",
  password: process.env.MOTHERDUCK_TOKEN,
  ssl: { rejectUnauthorized: true },
});

await client.connect();
for (const file of ["01_ingest.sql", "02_transform.sql", "03_publish.sql"]) {
  await client.query(await readFile(`sql/pipeline/${file}`, "utf8"));
}
await client.end();
```

Do not use the PG endpoint for local-file `COPY`, extension installation, or other client-only DuckDB behaviors.

## Prerequisites

- MotherDuck connection established (see `motherduck-connect` skill)
- Familiarity with data ingestion patterns (see `motherduck-load-data` skill)
- Understanding of schema design (see `motherduck-model-data` skill)
- Ability to write transformation queries (see `motherduck-query` skill)

## Runnable Reference Project

For a fully runnable example in this repo, start with:

- `references/dlt-dbt-motherduck-project/`

That reference project is intentionally small and verified against a real MotherDuck run. It combines:

- `dlt` for raw loading
- `dbt-duckdb` for staging and analytics models
- Python validation for output checks

Operational notes from that verified example:

- bootstrap the target MotherDuck database before running `dlt`; the `motherduck` destination does not create the database for you
- use Python 3.11 or 3.12 to reproduce this reference stack; its tested `dbt-duckdb` path did not run reliably on Python 3.14
- if you want exact schema names like `raw`, `staging`, and `analytics` in dbt, override `generate_schema_name`; otherwise dbt defaults may append the target schema name

## Verified Delivery Defaults

The repeated repo runs point to a stable pipeline posture:

- prefer Parquet or other bulk landing paths over row inserts
- keep explicit `raw`, `staging`, and `analytics` boundaries even in small examples
- ship one small MotherDuck-backed artifact plus one deeper runnable reference project
- measure and validate the pipeline with real MotherDuck runs rather than relying on local-only examples
- bootstrap the target MotherDuck database before loaders that assume it already exists

## Validation Signals

Use these signals for testing, review, and regression checks. They are not an instruction to include a separate "Validation Signals" section in normal user-facing replies.

- run `artifacts/pipeline_stage_example.py` against temporary MotherDuck databases
- verify the output reports `ingestion_mode` as `bulk_parquet_stage`
- verify the stage counts show raw > staging only when deduplication is expected
- run `references/dlt-dbt-motherduck-project/` end to end when the change affects the reference pipeline shape

---

## Pipeline Architecture

Every pipeline follows four stages. Do not skip stages.

For code examples and execution:

- default to **Python** for the pipeline runner
- show **TypeScript/Javascript** only when the pipeline is embedded in an existing Node.js service or control plane

```
Source --> Raw --> Staging --> Analytics/Serve
```

- **Source:** External data -- files, cloud storage (S3, GCS, Azure), APIs, databases.
- **Raw:** Append-only landing data preserved as close to the source as practical.
- **Staging:** Cleaned, typed, deduplicated intermediate tables.
- **Analytics/Serve:** Analytics-ready tables, views, Dives, or shares for downstream consumption.

Separating stages ensures you never lose raw data, can debug transformations independently, and can rebuild downstream assets from raw or staging at any time.

When stages live in separate databases, MotherDuck supports cross-database queries seamlessly. Reference tables in other databases with fully qualified names:

```sql
-- Query staging data from the analytics database context
SELECT * FROM "raw"."main"."orders_landing" WHERE order_date >= '2024-01-01';
-- Join across databases
SELECT s.*, c.customer_name
FROM "staging"."main"."orders_clean" s
LEFT JOIN "raw"."main"."customers_landing" c ON s.customer_id = c.customer_id;
```

This means pipeline SQL does not need to switch database context between stages -- every query can reference any stage by name.

---

## Step 1: Design the Target Schema

Start from the end -- what does the analytics team need? Design output tables first, then work backward.

For production pipelines, prefer a multi-database structure to enforce stage separation:

```sql
CREATE DATABASE IF NOT EXISTS raw;       -- Append-only ingested data
CREATE DATABASE IF NOT EXISTS staging;   -- Cleaned and deduplicated data
CREATE DATABASE IF NOT EXISTS analytics; -- Denormalized, business-ready tables
```

Design wide, denormalized analytics tables (see `motherduck-model-data` skill). Pre-join dimensions so analysts do not need to write joins.

For a minimal dbt project, one MotherDuck database with explicit `raw`, `staging`, and `analytics` schemas is also acceptable. That keeps the project small while preserving stage boundaries in the relation names.

---

## Step 2: Ingest Raw Data into Raw

Use `motherduck-load-data` skill patterns. Land data in `raw` as-is -- no transformations at this stage.

```sql
CREATE OR REPLACE TABLE "raw"."main"."orders_landing" AS
SELECT * FROM read_parquet('s3://bucket/orders/*.parquet');

CREATE OR REPLACE TABLE "raw"."main"."customers_landing" AS
SELECT * FROM read_csv('s3://bucket/customers/customers.csv');
```

Use `CREATE OR REPLACE TABLE` for idempotent full refreshes. Validate after loading:

```sql
SELECT 'orders_landing' AS table_name, count(*) AS row_count FROM "raw"."main"."orders_landing"
UNION ALL
SELECT 'customers_landing', count(*) FROM "raw"."main"."customers_landing";
```

Operational defaults:

- buffer API or event traffic before writing analytical tables
- prefer staged Parquet, Arrow/dataframes, or `COPY`
- tag long-lived workloads with `custom_user_agent`; for repo use-case builds, use `agent-skills/3.0.0(harness-<harness>;llm-<llm>)`
- keep write transactions comfortably bounded instead of unbounded monoliths

---

## Step 3: Promote Into Staging and Write Transformation Queries

Apply transformations in order: deduplicate, cast types, join, aggregate. Use CTEs for readability.

### Deduplication

```sql
CREATE OR REPLACE TABLE "staging"."main"."orders_deduped" AS
SELECT * FROM "raw"."main"."orders_landing"
QUALIFY ROW_NUMBER() OVER (PARTITION BY order_id ORDER BY updated_at DESC) = 1;
```

For composite keys, partition by the full key:

```sql
CREATE OR REPLACE TABLE "staging"."main"."order_lines_deduped" AS
SELECT * FROM "raw"."main"."order_lines_landing"
QUALIFY ROW_NUMBER() OVER (
    PARTITION BY order_id, line_item_id
    ORDER BY updated_at DESC
) = 1;
```

### Type Casting and Normalization

```sql
CREATE OR REPLACE TABLE "staging"."main"."orders_clean" AS
SELECT
    order_id,
    CAST(order_date AS DATE) AS order_date,
    customer_id,
    CAST(quantity AS INTEGER) AS quantity,
    CAST(unit_price AS DECIMAL(18,2)) AS unit_price,
    CAST(quantity * unit_price AS DECIMAL(18,2)) AS total_amount,
    UPPER(TRIM(status)) AS status
FROM "staging"."main"."orders_deduped"
WHERE order_id IS NOT NULL;
```

### Joining Across Sources

```sql
CREATE OR REPLACE TABLE "analytics"."main"."orders" AS
SELECT
    o.order_id, o.order_date, o.customer_id,
    c.customer_name, c.segment AS customer_segment,
    p.product_name, p.category AS product_category,
    o.quantity, o.unit_price, o.total_amount, c.region
FROM "staging"."main"."orders_clean" o
LEFT JOIN "raw"."main"."customers_landing" c ON o.customer_id = c.customer_id
LEFT JOIN "raw"."main"."products_landing" p ON o.product_id = p.product_id;
COMMENT ON TABLE "analytics"."main"."orders" IS 'Denormalized order data with customer and product attributes';
```

### Aggregation

```sql
CREATE OR REPLACE TABLE "analytics"."main"."daily_revenue" AS
SELECT
    order_date, region, product_category,
    COUNT(*) AS order_count, SUM(total_amount) AS total_revenue,
    AVG(total_amount) AS avg_order_value, COUNT(DISTINCT customer_id) AS unique_customers
FROM "analytics"."main"."orders"
GROUP BY ALL;
COMMENT ON TABLE "analytics"."main"."daily_revenue" IS 'Daily revenue by region and product category from orders';
COMMENT ON COLUMN "analytics"."main"."daily_revenue"."total_revenue" IS 'SUM(total_amount) from analytics.main.orders';
COMMENT ON COLUMN "analytics"."main"."daily_revenue"."avg_order_value" IS 'AVG(total_amount) from analytics.main.orders';
COMMENT ON COLUMN "analytics"."main"."daily_revenue"."unique_customers" IS 'COUNT(DISTINCT customer_id) from analytics.main.orders';
```

---

## Step 4: Materialize Analytics Tables

Use CTAS for expensive aggregations queried repeatedly. Use views for lightweight, always-current logic.

```sql
-- Materialized: expensive computation
CREATE OR REPLACE TABLE "analytics"."main"."customer_lifetime_value" AS
SELECT
    customer_id, customer_name, customer_segment,
    COUNT(DISTINCT order_id) AS total_orders,
    SUM(total_amount) AS lifetime_revenue,
    MIN(order_date) AS first_order_date,
    MAX(order_date) AS last_order_date
FROM "analytics"."main"."orders"
GROUP BY ALL;
COMMENT ON TABLE "analytics"."main"."customer_lifetime_value" IS 'Customer lifetime value metrics from orders';
COMMENT ON COLUMN "analytics"."main"."customer_lifetime_value"."total_orders" IS 'COUNT(DISTINCT order_id) from analytics.main.orders';
COMMENT ON COLUMN "analytics"."main"."customer_lifetime_value"."lifetime_revenue" IS 'SUM(total_amount) from analytics.main.orders';
COMMENT ON COLUMN "analytics"."main"."customer_lifetime_value"."first_order_date" IS 'MIN(order_date) from analytics.main.orders';
COMMENT ON COLUMN "analytics"."main"."customer_lifetime_value"."last_order_date" IS 'MAX(order_date) from analytics.main.orders';

-- View: always-current, lightweight
CREATE OR REPLACE VIEW "analytics"."main"."recent_orders" AS
SELECT * FROM "analytics"."main"."orders"
WHERE order_date >= current_date - INTERVAL 30 DAY;
COMMENT ON VIEW "analytics"."main"."recent_orders" IS 'Orders from the last 30 days from analytics.main.orders';
```

---

## Step 5: Validate Data Quality

Validate the data contracts affected by the change before publishing downstream outputs. For a new pipeline, check each stage boundary; a scoped transformation edit should rerun its affected models and dependent checks.

```sql
-- Row count sanity check across stages
SELECT 'raw.orders_landing' AS table_name, count(*) AS row_count
    FROM "raw"."main"."orders_landing"
UNION ALL
SELECT 'staging.orders_deduped', count(*) FROM "staging"."main"."orders_deduped"
UNION ALL
SELECT 'analytics.orders', count(*) FROM "analytics"."main"."orders";

-- NULL check on required columns
SELECT
    count(*) FILTER (WHERE order_id IS NULL) AS null_order_ids,
    count(*) FILTER (WHERE customer_id IS NULL) AS null_customer_ids,
    count(*) FILTER (WHERE total_amount IS NULL) AS null_amounts
FROM "analytics"."main"."orders";

-- Uniqueness check
SELECT order_id, count(*) AS cnt FROM "analytics"."main"."orders"
GROUP BY order_id HAVING cnt > 1;

-- Range validation
SELECT MIN(order_date) AS earliest, MAX(order_date) AS latest,
    count(*) FILTER (WHERE total_amount < 0) AS negative_amounts
FROM "analytics"."main"."orders";
```

---

## Step 6: Serve Results

```sql
-- Views for common query patterns
CREATE OR REPLACE VIEW "analytics"."main"."top_customers" AS
SELECT customer_id, customer_name, lifetime_revenue
FROM "analytics"."main"."customer_lifetime_value"
ORDER BY lifetime_revenue DESC LIMIT 100;
COMMENT ON VIEW "analytics"."main"."top_customers" IS 'Top 100 customers by lifetime revenue from customer_lifetime_value';
```

- Use the `motherduck-create-dive` skill for interactive visualizations powered by analytics tables.
- Use the `motherduck-share-data` skill to distribute databases to teams or partners:

```sql
CREATE SHARE IF NOT EXISTS analytics_share FROM analytics (
    ACCESS RESTRICTED, VISIBILITY DISCOVERABLE, UPDATE AUTOMATIC,
    INCLUDE_PATTERN 'main.monthly_*, main.top_customers'
);
GRANT READ ON SHARE analytics_share TO ROLE analyst;
```

Before sharing, make sure the serving tables are curated and documented. Shares are zero-copy and easy to distribute, so be deliberate about what database boundary you are publishing.

---

## Incremental Load Patterns

Full refreshes work for small-to-medium datasets. For large or frequently updated datasets, use incremental patterns.

```sql
-- Append new data only
INSERT INTO "raw"."main"."orders_landing"
SELECT * FROM read_parquet('s3://bucket/orders/date=2024-03-24/*.parquet')
WHERE order_date > (SELECT MAX(order_date) FROM "raw"."main"."orders_landing");

-- Upsert: load into temp table, delete old rows, insert new
CREATE OR REPLACE TEMP TABLE new_orders AS
SELECT * FROM read_parquet('s3://bucket/orders/latest/*.parquet');

DELETE FROM "raw"."main"."orders_landing"
WHERE order_id IN (SELECT order_id FROM new_orders);

INSERT INTO "raw"."main"."orders_landing"
SELECT * FROM new_orders;

-- Incremental aggregation: rebuild only affected date range
DELETE FROM "analytics"."main"."daily_revenue"
WHERE order_date >= (SELECT MAX(order_date) - INTERVAL 3 DAY FROM "raw"."main"."orders_landing");

INSERT INTO "analytics"."main"."daily_revenue"
SELECT order_date, region, product_category,
    COUNT(*) AS order_count, SUM(total_amount) AS total_revenue,
    AVG(total_amount) AS avg_order_value, COUNT(DISTINCT customer_id) AS unique_customers
FROM "analytics"."main"."orders"
WHERE order_date >= (SELECT MAX(order_date) - INTERVAL 3 DAY FROM "raw"."main"."orders_landing")
GROUP BY ALL;
```

---

## Complete Pipeline Example

End-to-end: CSV ingest, deduplicate, join, aggregate, validate, create views, share.

```sql
-- 1. Create databases
CREATE DATABASE IF NOT EXISTS raw;
CREATE DATABASE IF NOT EXISTS staging;
CREATE DATABASE IF NOT EXISTS analytics;

-- 2. Ingest raw data
CREATE OR REPLACE TABLE "raw"."main"."sales_landing" AS
SELECT * FROM read_csv('s3://acme-data/sales/sales_2024.csv');

CREATE OR REPLACE TABLE "raw"."main"."customers_landing" AS
SELECT * FROM read_csv('s3://acme-data/customers/customers.csv');

-- 3. Deduplicate
CREATE OR REPLACE TABLE "staging"."main"."sales_deduped" AS
SELECT * FROM "raw"."main"."sales_landing"
QUALIFY ROW_NUMBER() OVER (PARTITION BY sale_id ORDER BY updated_at DESC) = 1;

-- 4. Transform and join
CREATE OR REPLACE TABLE "analytics"."main"."sales" AS
SELECT s.sale_id, s.sale_date, s.product_name, s.quantity, s.unit_price,
    CAST(s.quantity * s.unit_price AS DECIMAL(18,2)) AS total_amount,
    c.customer_name, c.segment, c.region
FROM "staging"."main"."sales_deduped" s
LEFT JOIN "raw"."main"."customers_landing" c ON s.customer_id = c.customer_id;
COMMENT ON TABLE "analytics"."main"."sales" IS 'Denormalized sales with customer attributes';

-- 5. Aggregate
CREATE OR REPLACE TABLE "analytics"."main"."revenue_summary" AS
SELECT date_trunc('month', sale_date) AS month, region, segment,
    COUNT(*) AS sale_count, SUM(total_amount) AS total_revenue,
    COUNT(DISTINCT customer_name) AS unique_customers
FROM "analytics"."main"."sales" GROUP BY ALL;
COMMENT ON TABLE "analytics"."main"."revenue_summary" IS 'Monthly revenue summary by region and segment from sales';
COMMENT ON COLUMN "analytics"."main"."revenue_summary"."total_revenue" IS 'SUM(total_amount) from analytics.main.sales';
COMMENT ON COLUMN "analytics"."main"."revenue_summary"."unique_customers" IS 'COUNT(DISTINCT customer_name) from analytics.main.sales';

-- 6. Validate
SELECT count(*) FILTER (WHERE sale_id IS NULL) AS null_ids,
    count(*) FILTER (WHERE total_amount < 0) AS negative_amounts
FROM "analytics"."main"."sales";

SELECT sale_id, count(*) AS cnt FROM "analytics"."main"."sales"
GROUP BY sale_id HAVING cnt > 1;

-- 7. Serve
CREATE OR REPLACE VIEW "analytics"."main"."monthly_revenue" AS
SELECT month, SUM(total_revenue) AS revenue, SUM(unique_customers) AS customers
FROM "analytics"."main"."revenue_summary" GROUP BY month ORDER BY month;
COMMENT ON VIEW "analytics"."main"."monthly_revenue" IS 'Monthly total revenue and customer counts rolled up from revenue_summary';
COMMENT ON COLUMN "analytics"."main"."monthly_revenue"."revenue" IS 'SUM(total_revenue) from analytics.main.revenue_summary';
COMMENT ON COLUMN "analytics"."main"."monthly_revenue"."customers" IS 'SUM(unique_customers) from analytics.main.revenue_summary';

-- 8. Share
CREATE SHARE IF NOT EXISTS analytics_share FROM analytics (
    ACCESS RESTRICTED, VISIBILITY DISCOVERABLE, UPDATE AUTOMATIC,
    INCLUDE_PATTERN 'main.monthly_revenue'
);
GRANT READ ON SHARE analytics_share TO ROLE analyst;
```

---

## Scheduling Considerations

Use a MotherDuck Flight for a Python-native scheduled ingestion or transformation that should run on MotherDuck compute. Create it without a schedule, validate one on-demand run and its logs, then attach the UTC cron and a plan-valid `max_runtime_sec`. Keep reusable conventions in the reserved `flights` Guide topic.

Use an external scheduler such as Dagster, Airflow, Prefect, GitHub Actions, or cron when the workflow coordinates several external systems, needs a richer DAG/control plane, or must run outside MotherDuck.

Store SQL transformations in version-controlled `.sql` files. Execute them from a scheduled script.

### Native DuckDB (recommended)

Use native `duckdb.connect("md:")` for pipeline runners. This gives you full DuckDB SQL support, cross-database queries, and no driver translation layer.

```python
# pipeline.py -- run via cron, Airflow, or GitHub Actions
import duckdb
import os
from pathlib import Path

PIPELINE_USER_AGENT = "agent-skills/3.0.0(harness-<harness>;llm-<llm>)"

def run_pipeline():
    conn = duckdb.connect(f"md:?custom_user_agent={PIPELINE_USER_AGENT}")
    for step in sorted(Path("sql/pipeline").glob("*.sql")):
        print(f"Running {step.name}...")
        conn.execute(step.read_text())
    conn.close()

if __name__ == "__main__":
    run_pipeline()
```

### PG endpoint alternative

Use the PG endpoint when the pipeline runs in an environment that already has PostgreSQL drivers and you want to avoid installing `duckdb`. This is common in serverless runtimes, container images with existing `psycopg2`, or TypeScript backends.

```python
# pipeline_pg.py -- PG endpoint alternative
import psycopg2, certifi, os
from pathlib import Path

def run_pipeline():
    conn = psycopg2.connect(
        host="pg.us-east-1-aws.motherduck.com", port=5432,
        dbname="staging", user="postgres",
        password=os.environ["MOTHERDUCK_TOKEN"],
        sslmode="verify-full", sslrootcert=certifi.where(),
    )
    conn.autocommit = True
    for step in sorted(Path("sql/pipeline").glob("*.sql")):
        print(f"Running {step.name}...")
        conn.cursor().execute(step.read_text())
    conn.close()

if __name__ == "__main__":
    run_pipeline()
```

Number files to enforce execution order (`01_ingest.sql`, `02_dedupe.sql`, etc.). Each file should be idempotent -- use `CREATE OR REPLACE` so re-running is safe.

---

## Key Rules

- **Separate lifecycle stages explicitly.** Production default: `raw`, `staging`, and `analytics` as separate databases. Minimal dbt projects may use one database with `raw`, `staging`, and `analytics` schemas.
- **Land data in `raw` before curation.** Preserve source-like tables so downstream rebuilds stay simple.
- **Validate affected stage contracts.** Use row counts, nullability, uniqueness, and range checks where they protect the data contract.
- **Preserve raw data.** Never transform during ingestion. Rebuild downstream tables from staging.
- **Materialize only what needs fast repeated access.** Use views for lightweight, always-current logic.
- **Use `CREATE OR REPLACE` for idempotent rebuilds.** Every pipeline step should be safe to re-run.
- **Version control all SQL transformations.** Store `.sql` files in git, not in ad-hoc query editors.
- **Deduplicate before building analytics tables.** Raw sources often contain duplicates.
- **Use fully qualified table names** in every statement: `"database"."schema"."table"`.
- **Tag long-lived pipeline runners with `custom_user_agent`.** This makes workload attribution and cost analysis possible later. For repo use-case builds, use `agent-skills/3.0.0(harness-<harness>;llm-<llm>)`.

---

## Common Mistakes

### Loading and transforming in a single step

Combining ingestion with transformation loses the raw data. If a bug is discovered later, you must re-ingest from the external source.

```sql
-- Wrong: raw data is lost
CREATE TABLE "analytics"."main"."orders" AS
SELECT order_id, UPPER(status) AS status FROM read_parquet('s3://bucket/orders.parquet');

-- Right: ingest raw first, then transform
CREATE TABLE "raw"."main"."orders_landing" AS
SELECT * FROM read_parquet('s3://bucket/orders.parquet');
CREATE TABLE "analytics"."main"."orders" AS
SELECT order_id, UPPER(status) AS status FROM "raw"."main"."orders_landing";
```

### Not deduplicating before analytics tables

Raw sources frequently contain duplicates from retries, overlapping file loads, or CDC replication. Without deduplication, aggregations produce inflated numbers.

### Forgetting data validation between stages

Skipping validation means bad data propagates silently. A NULL customer ID in staging becomes an orphaned order in analytics and an incorrect revenue number in a dashboard.

### Using DROP TABLE then CREATE TABLE instead of CREATE OR REPLACE

`DROP` then `CREATE` is non-atomic -- queries fail during the gap. Use `CREATE OR REPLACE TABLE` for atomic replacement.

### Over-aggregating and losing detail

Pre-aggregating to monthly granularity when analysts later need hourly or daily breakdowns forces a pipeline rebuild. Ask what the finest useful grain is before choosing -- for some use cases that is hourly, for others event-level. Keep that grain as the base table and build coarser rollups (daily, weekly, monthly) on top. When in doubt, preserve more detail -- it is easy to aggregate up but impossible to disaggregate down.

---

## Related Skills

- `motherduck-connect` -- Establish a MotherDuck connection
- `motherduck-load-data` -- Ingest data from files, cloud storage, and external sources
- `motherduck-model-data` -- Design database schemas and data models
- `motherduck-query` -- Execute DuckDB SQL queries and transformations
- `motherduck-explore` -- Discover databases, tables, columns, and shares
- `motherduck-create-dive` -- Build interactive visualizations from analytics tables
- `motherduck-share-data` -- Distribute analytics databases to teams and partners

SKILL.md

---
name: motherduck-build-data-pipeline
description: Build ingestion-to-serving pipelines on MotherDuck, including stage boundaries, transformations, and publication. Use for any recurring ingestion from an API, SaaS app, or database into MotherDuck.
license: MIT
---

# Build a Data Pipeline with MotherDuck

## Start Here: Is a MotherDuck Server Active?

Use an active remote MotherDuck MCP server or local MotherDuck server to inspect the in-scope database, schema, grain, keys, and relevant metrics. Reuse known context and narrow discovery to the requested work; do not scan the whole workspace by default. Let the actual data model shape the result.

Resolve the target from the request or active context. Ask only if ambiguity materially affects the result. Without a server, use supplied schema and explicit assumptions for planning; do not imply live validation.

## Pipeline Defaults

- batch over streaming
- raw landing before curation
- explicit raw -> staging -> analytics boundaries
- bulk ingest paths over row-by-row writes: stage files and load with `read_parquet`/`read_json_auto` or one `INSERT ... SELECT`; never `executemany()` or per-row `INSERT` loops against MotherDuck
- idempotent stage rebuilds or append contracts before scheduled automation
- verify the MotherDuck-supported DuckDB client version before recommending upstream-only write, checkpoint, or lakehouse features
- native MotherDuck storage unless DuckLake is explicitly required
- MotherDuck CLI for Flight source and large file-shaped output when the agent has a shell; MCP for chat-only operation
- a `flights` Guide for reusable scheduling, naming, secret, and ingestion conventions when the organization has them
- Flights sized to their Python work (`F4` when the job mostly sends SQL) and instrumented for the Flight Explainer so each run shows step status and the failing step
- AI enrichment (classification with `prompt_jev()`, extraction with `prompt()`) as an incremental transform step that writes results to a table, never recomputed at read time

## Workflow

1. Inspect the available MotherDuck server or supplied source and target context, including the current target data model.
2. Define raw, staging, and analytics boundaries.
4. Ingest raw data.
5. Deduplicate, type, and promote into staging.
6. Materialize analytics-ready outputs.
7. Validate counts, freshness, uniqueness, and business metrics before publishing downstream assets.
8. When durable context is part of delivery, capture stable business definitions and operating caveats in referenced Guides; keep executable transformation logic in source control.

Match execution to the request: answer, review, or planning work returns the requested pipeline artifacts; build or change work creates the requested in-scope files and warehouse objects and validates them. Ask before destructive actions, unrelated external writes, or a material expansion of scope.

When this skill produces a native DuckDB (`md:`) connection, watermark it with `custom_user_agent=agent-skills/3.0.0(harness-<harness>;llm-<llm>)`. If metadata is missing, fall back to `harness-unknown` and `llm-unknown`.

## Output

For a full engagement, cover the following as relevant to the request:

- the stage layout
- the ingestion method
- the transformation sequence
- the serving tables or views
- the validation checks

For explicit structured JSON requests, read [the output contract](references/EXECUTION_REFERENCE.md#structured-output). Otherwise use the format that fits the requested deliverable.

## References

Read only the sections relevant to the task; these are guidance, not a mandatory itinerary.

- `references/dlt-dbt-motherduck-project/` -- fully runnable MotherDuck reference project using `dlt`, `dbt-duckdb`, and validation queries
- `references/PIPELINE_IMPLEMENTATION_GUIDE.md` -- stage design, transformation sequencing, and ingestion-to-serving examples

## Examples

Read [the execution reference](references/EXECUTION_REFERENCE.md) only to run the bundled examples or reproduce their validation.

- [pipeline_stage_example.py](artifacts/pipeline_stage_example.py)
- [pipeline_stage_example.ts](artifacts/pipeline_stage_example.ts)

## Related Skills

Load related skills only for missing capabilities; reuse established context.

- `motherduck-connect` -- choose the right connection path
- `motherduck-load-data` -- ingestion mechanics
- `motherduck-model-data` -- shape the analytics layer
- `motherduck-query` -- write transformations and validations
- `motherduck-share-data` -- publish curated outputs
- `motherduck-ducklake` -- only when open-table-format storage is a real requirement
- `motherduck-create-flight` -- schedule and run the pipeline on MotherDuck compute