# 1507 Etl Patterns C0d97b2d

> ETL Patterns Reference

- Skill: `tools-only/1507-etl-patterns-c0d97b2d` (Agent Skill, multi-file: 3 files)
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- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/tools-only/1507-etl-patterns-c0d97b2d

---

# ETL Patterns Reference

## Pattern Summary

| Pattern                | When to Use                                          |
| ---------------------- | ---------------------------------------------------- |
| File import assets     | Loading CSVs, Parquet, JSON from local/cloud storage |
| API resource + assets  | Fetching data from REST APIs                         |
| dlt integration        | Embedded ETL with schema inference and pagination    |
| Sling integration      | Database-to-database replication                     |
| Configurable ingestion | Dynamic file/endpoint selection at runtime           |

---

## File Import Pattern

### Basic File Import with Config

```python
import dagster as dg
from pathlib import Path

class IngestionFileConfig(dg.Config):
    path: str

@dg.asset
def import_file(config: IngestionFileConfig) -> str:
    """Resolve file path from config."""
    file_path = Path(__file__).parent / f"../../../data/source/{config.path}"
    return str(file_path.resolve())
```

### Load File into Database

```python
from dagster_duckdb import DuckDBResource

@dg.asset(kinds={"duckdb"})
def raw_data_table(
    context: dg.AssetExecutionContext,
    database: DuckDBResource,
    import_file: str,
) -> None:
    """Load CSV file into DuckDB table."""
    table_name = "raw_data"

    with database.get_connection() as conn:
        # Create table if not exists
        conn.execute(f"""
            CREATE TABLE IF NOT EXISTS {table_name} (
                date DATE,
                value FLOAT,
                category VARCHAR
            )
        """)

        # Load data using COPY
        conn.execute(f"COPY {table_name} FROM '{import_file}'")
```

### Running with Config

**CLI:**

```bash
dg launch --assets import_file,raw_data_table \
  --config-json '{"ops": {"import_file": {"config": {"path": "2024-01-01.csv"}}}}'
```

**UI YAML:**

```yaml
ops:
  import_file:
    config:
      path: 2024-01-01.csv
```

---

## API Integration Pattern

### API Resource

```python
import dagster as dg
import requests

class NASAResource(dg.ConfigurableResource):
    api_key: str
    base_url: str = "https://api.nasa.gov"

    def get_near_earth_asteroids(self, start_date: str, end_date: str) -> list:
        url = f"{self.base_url}/neo/rest/v1/feed"
        params = {
            "start_date": start_date,
            "end_date": end_date,
            "api_key": self.api_key,
        }

        response = requests.get(url, params=params)
        response.raise_for_status()
        return response.json()["near_earth_objects"][start_date]
```

### API Asset

```python
@dg.asset
def asteroid_data(
    context: dg.AssetExecutionContext,
    nasa: NASAResource,
) -> list[dict]:
    """Fetch asteroid data from NASA API."""
    data = nasa.get_near_earth_asteroids("2024-01-01", "2024-01-07")
    context.log.info(f"Retrieved {len(data)} asteroids")
    return data
```

### Register API Resource

```python
@dg.definitions
def resources():
    return dg.Definitions(
        resources={
            "nasa": NASAResource(
                api_key=dg.EnvVar("NASA_API_KEY"),
            ),
        },
    )
```

---

## dlt Integration

dlt (data load tool) handles schema inference, pagination, and loading automatically.

### Basic dlt Pipeline

```python
import dlt

@dlt.source
def simple_source():
    @dlt.resource
    def load_dict():
        data = [
            {"id": 1, "name": "Alice"},
            {"id": 2, "name": "Bob"},
        ]
        yield data

    return load_dict

# Standalone execution
pipeline = dlt.pipeline(
    pipeline_name="simple_pipeline",
    destination="duckdb",
    dataset_name="mydata",
)
load_info = pipeline.run(simple_source())
```

### dlt with Dagster

```python
import dagster as dg
import dlt
from dagster_dlt import DagsterDltResource, dlt_assets

@dlt.source
def simple_source():
    @dlt.resource
    def load_dict():
        data = [
            {"id": 1, "name": "Alice"},
            {"id": 2, "name": "Bob"},
        ]
        yield data

    return load_dict

@dlt_assets(
    dlt_source=simple_source(),
    dlt_pipeline=dlt.pipeline(
        pipeline_name="simple_pipeline",
        dataset_name="simple",
        destination="duckdb",
        progress="log",
    ),
)
def dlt_assets(context: dg.AssetExecutionContext, dlt: DagsterDltResource):
    yield from dlt.run(context=context)
```

### dlt Resource Registration

```python
from dagster_dlt import DagsterDltResource

@dg.definitions
def resources():
    return dg.Definitions(
        resources={
            "dlt": DagsterDltResource(),
        },
    )
```

### dlt Benefits

- **Schema inference**: Automatically detects and creates table schemas
- **Type inference**: Maps Python types to database types
- **Pagination handling**: Built-in support for paginated APIs
- **Incremental loading**: Track state for incremental updates
- **Multiple destinations**: DuckDB, Snowflake, BigQuery, Postgres, etc.

---

## Sling Integration

Sling is declarative database-to-database replication.

### Sling Connection Resources

```python
from dagster_sling import SlingConnectionResource, SlingResource

# Source database
source = SlingConnectionResource(
    name="MY_POSTGRES",
    type="postgres",
    host="localhost",
    port=5432,
    database="source_db",
    user="user",
    password="password",
)

# Destination database
destination = SlingConnectionResource(
    name="MY_DUCKDB",
    type="duckdb",
    connection_string="duckdb:///data/staging/data.duckdb",
)

# Combine into SlingResource
sling = SlingResource(
    connections=[source, destination],
)
```

### Sling Replication Config

```yaml
# sling_replication.yaml
source: MY_POSTGRES
target: MY_DUCKDB

defaults:
  mode: full-refresh
  object: "{stream_schema}_{stream_table}"

streams:
  data.customers:
  data.products:
  data.orders:
```

### Sling Replication Modes

| Mode           | Description                              |
| -------------- | ---------------------------------------- |
| `full-refresh` | Drop and recreate table each run         |
| `incremental`  | Append new records                       |
| `truncate`     | Truncate table before loading            |
| `snapshot`     | Full refresh with point-in-time snapshot |

### Sling Assets

```python
from dagster_sling import SlingResource, sling_assets

replication_config = dg.file_relative_path(__file__, "sling_replication.yaml")

@sling_assets(replication_config=replication_config)
def postgres_sling_assets(context: dg.AssetExecutionContext, sling: SlingResource):
    yield from sling.replicate(context=context).fetch_column_metadata()
```

### Register Sling Resource

```python
@dg.definitions
def resources():
    return dg.Definitions(
        resources={
            "sling": sling,
        },
    )
```

---

## Choosing ETL Approach

| Scenario                      | Recommended Approach  |
| ----------------------------- | --------------------- |
| Load local files              | File import pattern   |
| Custom REST API               | API resource + assets |
| Standard API (Stripe, GitHub) | dlt verified sources  |
| Schema-on-read ingestion      | dlt                   |
| Database replication          | Sling                 |
| Complex transformations       | Custom assets or dbt  |

---

## Partitioned ETL

### Partitioned File Import

```python
daily_partition = dg.DailyPartitionsDefinition(start_date="2024-01-01")

@dg.asset(partitions_def=daily_partition)
def daily_import(context: dg.AssetExecutionContext) -> str:
    partition_date = context.partition_key  # "2024-01-01"
    file_path = f"data/source/{partition_date}.csv"
    return file_path

@dg.asset(partitions_def=daily_partition)
def daily_load(
    context: dg.AssetExecutionContext,
    database: DuckDBResource,
    daily_import: str,
) -> None:
    partition = context.partition_key

    with database.get_connection() as conn:
        # Delete existing partition data
        conn.execute(f"DELETE FROM raw_data WHERE date = '{partition}'")
        # Load new data
        conn.execute(f"COPY raw_data FROM '{daily_import}'")
```

### Partitioned API Fetch

```python
@dg.asset(partitions_def=daily_partition)
def daily_api_data(
    context: dg.AssetExecutionContext,
    api: MyAPIResource,
) -> list[dict]:
    partition_date = context.partition_key
    return api.fetch_data_for_date(partition_date)
```

---

## Data Validation

### Validate After Load

```python
@dg.asset(deps=["raw_data_table"])
def validated_data(database: DuckDBResource) -> dg.MaterializeResult:
    with database.get_connection() as conn:
        # Check row count
        result = conn.execute("SELECT COUNT(*) FROM raw_data").fetchone()
        row_count = result[0]

        # Check for nulls
        null_check = conn.execute(
            "SELECT COUNT(*) FROM raw_data WHERE value IS NULL"
        ).fetchone()
        null_count = null_check[0]

    return dg.MaterializeResult(
        metadata={
            "row_count": row_count,
            "null_values": null_count,
            "null_percentage": round(null_count / row_count * 100, 2) if row_count > 0 else 0,
        }
    )
```

### Asset Check for Data Quality

```python
@dg.asset_check(asset=raw_data_table)
def no_duplicate_records(database: DuckDBResource) -> dg.AssetCheckResult:
    with database.get_connection() as conn:
        result = conn.execute("""
            SELECT COUNT(*) - COUNT(DISTINCT id) as duplicates
            FROM raw_data
        """).fetchone()
        duplicates = result[0]

    return dg.AssetCheckResult(
        passed=duplicates == 0,
        metadata={"duplicate_count": duplicates},
    )
```

---

## Error Handling

### Retry Pattern

```python
import time

class RobustAPIResource(dg.ConfigurableResource):
    api_key: str
    max_retries: int = 3

    def fetch_with_retry(self, endpoint: str) -> dict:
        for attempt in range(self.max_retries):
            try:
                response = requests.get(endpoint, headers={"Authorization": self.api_key})
                response.raise_for_status()
                return response.json()
            except requests.RequestException as e:
                if attempt == self.max_retries - 1:
                    raise
                time.sleep(2 ** attempt)  # Exponential backoff
```

### Graceful Failure

```python
@dg.asset
def api_data_with_fallback(
    context: dg.AssetExecutionContext,
    api: MyAPIResource,
) -> dg.MaterializeResult:
    try:
        data = api.fetch_data()
        return dg.MaterializeResult(
            metadata={"status": "success", "row_count": len(data)}
        )
    except Exception as e:
        context.log.error(f"API fetch failed: {e}")
        raise  # Let Dagster handle the failure
```

---

## Anti-Patterns to Avoid

| Anti-Pattern         | Better Approach                        |
| -------------------- | -------------------------------------- |
| Hardcoded file paths | Use `Config` for dynamic paths         |
| No schema validation | Add asset checks for data quality      |
| Ignoring pagination  | Use dlt or implement proper pagination |
| Full refresh always  | Consider incremental loading           |
| No retry logic       | Add retries with exponential backoff   |

---

## References

- [dlt Documentation](https://dlthub.com/docs)
- [Sling Documentation](https://docs.slingdata.io/)
- [Dagster dlt Integration](https://docs.dagster.io/integrations/libraries/dlt)
- [Dagster Sling Integration](https://docs.dagster.io/integrations/libraries/sling)
- [ETL Pipeline Tutorial](https://docs.dagster.io/etl-pipeline-tutorial)