hamilton-integrations

name	hamilton-integrations
description	Hamilton integration patterns for Airflow, Dagster, FastAPI, Streamlit, Jupyter notebooks, and other frameworks. Use when integrating Hamilton with other tools.
allowed-tools	Read, Grep, Glob, Bash(python:*), Bash(jupyter:*)
user-invocable	true
disable-model-invocation	false

Hamilton Integrations

This skill covers integrating Hamilton with orchestrators, web frameworks, notebooks, and other tools.

Why Integrate Hamilton?

Hamilton focuses on dataflow definition, letting you integrate with:

• Orchestrators (Airflow, Dagster, Prefect) - Schedule and monitor
• Web frameworks (FastAPI, Flask) - Serve predictions
• Dashboards (Streamlit, Plotly Dash) - Interactive visualization
• Notebooks (Jupyter) - Interactive development
• Experiment tracking (MLflow, Weights & Biases) - Track experiments

Airflow Integration

Use Case: Schedule Hamilton DAGs as Airflow tasks

"""Hamilton in Airflow DAG."""
from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta
from hamilton import driver
import my_hamilton_module

def run_hamilton_pipeline(**context):
    """Execute Hamilton DAG within Airflow task."""
    dr = driver.Driver({}, my_hamilton_module)

    results = dr.execute(
        ['final_output'],
        inputs={
            'data_date': context['ds'],  # Airflow execution date
            'run_id': context['run_id']
        }
    )

    # Push results to XCom for downstream tasks
    context['task_instance'].xcom_push(key='results', value=results)
    return results

default_args = {
    'owner': 'data-team',
    'depends_on_past': False,
    'start_date': datetime(2024, 1, 1),
    'retries': 2,
    'retry_delay': timedelta(minutes=5)
}

with DAG(
    'hamilton_etl_pipeline',
    default_args=default_args,
    schedule_interval='@daily',
    catchup=False
) as dag:

    hamilton_task = PythonOperator(
        task_id='run_hamilton_etl',
        python_callable=run_hamilton_pipeline,
        provide_context=True
    )

Multiple Hamilton DAGs in Airflow:

"""Orchestrate multiple Hamilton pipelines."""
def run_data_ingestion(**context):
    """Hamilton pipeline 1: Ingest data."""
    import ingestion_module
    dr = driver.Driver({}, ingestion_module)
    return dr.execute(['ingested_data'], inputs={'date': context['ds']})

def run_feature_engineering(**context):
    """Hamilton pipeline 2: Feature engineering."""
    import feature_module
    # Get data from previous task
    ingested_data = context['task_instance'].xcom_pull(task_ids='ingest')
    dr = driver.Driver({}, feature_module)
    return dr.execute(['features'], inputs={'raw_data': ingested_data})

def run_model_training(**context):
    """Hamilton pipeline 3: Train model."""
    import training_module
    features = context['task_instance'].xcom_pull(task_ids='features')
    dr = driver.Driver({}, training_module)
    return dr.execute(['trained_model'], inputs={'features': features})

with DAG('ml_pipeline', schedule_interval='@weekly') as dag:
    ingest = PythonOperator(task_id='ingest', python_callable=run_data_ingestion)
    features = PythonOperator(task_id='features', python_callable=run_feature_engineering)
    train = PythonOperator(task_id='train', python_callable=run_model_training)

    ingest >> features >> train

Dagster Integration

Use Case: Define Hamilton as Dagster assets

"""Hamilton in Dagster."""
from dagster import asset, AssetExecutionContext
from hamilton import driver
import my_hamilton_module

@asset
def customer_features(context: AssetExecutionContext) -> dict:
    """Execute Hamilton DAG as Dagster asset."""
    dr = driver.Driver({}, my_hamilton_module)

    context.log.info("Starting Hamilton pipeline")

    results = dr.execute(
        ['customer_segments', 'feature_importance'],
        inputs={'data_path': '/data/customers.csv'}
    )

    context.log.info(f"Generated {len(results['customer_segments'])} segments")

    return results

@asset(deps=[customer_features])
def segment_report(context: AssetExecutionContext, customer_features: dict) -> str:
    """Use Hamilton output in downstream Dagster asset."""
    segments = customer_features['customer_segments']
    # Generate report
    return f"Processed {len(segments)} segments"

FastAPI Integration

Use Case: Serve Hamilton DAGs as REST API endpoints

"""Hamilton as FastAPI microservice."""
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from hamilton import driver
import prediction_module

app = FastAPI(title="ML Prediction Service")

# Initialize driver once at startup
prediction_driver = driver.Driver({}, prediction_module)

class PredictionRequest(BaseModel):
    """Request schema."""
    user_id: str
    feature_a: float
    feature_b: float
    feature_c: float

class PredictionResponse(BaseModel):
    """Response schema."""
    user_id: str
    prediction: float
    confidence: float

@app.post("/predict", response_model=PredictionResponse)
def predict(request: PredictionRequest):
    """Stateless prediction endpoint."""
    try:
        result = prediction_driver.execute(
            ['prediction', 'confidence'],
            inputs=request.dict()
        )

        return PredictionResponse(
            user_id=request.user_id,
            prediction=result['prediction'],
            confidence=result['confidence']
        )
    except Exception as e:
        raise HTTPException(status_code=, detail=(e))


 ():
    
     {: , : }

Async FastAPI with Async Hamilton:

"""Async Hamilton with FastAPI."""
from fastapi import FastAPI
from hamilton import async_driver
import async_prediction_module

app = FastAPI()

# Async driver initialization
prediction_driver = None

@app.on_event("startup")
async def startup():
    """Initialize async driver on startup."""
    global prediction_driver
    prediction_driver = await async_driver.Builder()\
        .with_modules(async_prediction_module)\
        .build()

@app.post("/predict")
async def predict(request: PredictionRequest):
    """Async prediction endpoint."""
    result = await prediction_driver.execute(
        ['prediction'],
        inputs=request.dict()
    )
    return {"prediction": result['prediction']}

Streamlit Integration

Use Case: Interactive data apps with Hamilton

"""Hamilton-powered Streamlit dashboard."""
import streamlit as st
from hamilton import driver
import analytics_module

st.title("Customer Analytics Dashboard")

# Sidebar controls
date_range = st.sidebar.date_input("Select date range", [])
metric = st.sidebar.selectbox("Metric", ["revenue", "users", "conversions"])
segment = st.sidebar.multiselect("Segments", ["new", "returning", "churned"])

# Execute Hamilton DAG with user inputs
if st.sidebar.button("Run Analysis"):
    with st.spinner("Running analysis..."):
        dr = driver.Driver({'metric': metric}, analytics_module)

        results = dr.execute(
            ['daily_metrics', 'segment_breakdown', 'trends'],
            inputs={
                'date_range': date_range,
                'segments': segment
            }
        )

        # Display results
        st.header("Daily Metrics")
        st.line_chart(results['daily_metrics'])

        st.header("Segment Breakdown")
        st.bar_chart(results['segment_breakdown'])

        st.header("Trends")
        st.dataframe(results['trends'])

        # Visualize DAG
        st.header("Pipeline Visualization")
        dr.visualize_execution(
            ['trends'],
            './pipeline.png',
            inputs={'date_range': date_range, : segment}
        )
        st.image()

Jupyter Notebook Integration

Use Case: Interactive development and experimentation

Jupyter Magic Extension

"""Use Hamilton directly in notebooks."""
# Install magic extension
%load_ext hamilton.plugins.jupyter_magic

# Define Hamilton functions in cells
%%cell_to_module my_analysis --display

import pandas as pd

def raw_data(csv_path: str) -> pd.DataFrame:
    """Load data."""
    return pd.read_csv(csv_path)

def cleaned_data(raw_data: pd.DataFrame) -> pd.DataFrame:
    """Clean data."""
    return raw_data.dropna()

def summary_stats(cleaned_data: pd.DataFrame) -> dict:
    """Calculate summary."""
    return {
        'mean': cleaned_data['value'].mean(),
        'std': cleaned_data['value'].std()
    }

# Displays DAG visualization automatically!

Standard Notebook Pattern

"""Hamilton in Jupyter without magic."""
# Cell 1: Define Hamilton functions
# my_functions.py equivalent
def load_data(data_path: str) -> pd.DataFrame:
    return pd.read_csv(data_path)

def process_data(load_data: pd.DataFrame) -> pd.DataFrame:
    return load_data.dropna()

# Cell 2: Create driver
from hamilton import driver
import sys

# Add current module to driver
dr = driver.Driver({}, sys.modules[__name__])

# Cell 3: Execute and explore
results = dr.execute(
    ['process_data'],
    inputs={'data_path': 'data.csv'}
)

results['process_data'].head()

# Cell 4: Visualize
dr.visualize_execution(
    ['process_data'],
    './notebook_dag.png',
    inputs={'data_path': 'data.csv'}
)

from IPython.display import Image
Image('./notebook_dag.png')

MLflow Integration

Use Case: Track experiments and models

"""Hamilton with MLflow tracking."""
from hamilton import driver
from hamilton.plugins.mlflow_extensions import MLFlowTracker
import mlflow
import training_module

# Configure MLflow
mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("customer_churn")

with mlflow.start_run():
    # Create MLflow adapter
    mlflow_tracker = MLFlowTracker(
        experiment_name="customer_churn",
        run_name="baseline_model_v1",
        tags={"model_type": "random_forest", "version": "1.0"}
    )

    # Hamilton driver with MLflow tracking
    dr = driver.Builder()\
        .with_config({'model_type': 'random_forest'})\
        .with_modules(training_module)\
        .with_adapters(mlflow_tracker)\
        .build()

    results = dr.execute(
        ['trained_model', 'metrics'],
        inputs={'training_data': train_df}
    )

    # Log additional metrics
    mlflow.log_metrics(results['metrics'])
    mlflow.log_param("features_count", len(results['features']))

Weights & Biases Integration

"""Hamilton with W&B tracking."""
import wandb
from hamilton import driver
import experiment_module

# Initialize W&B
wandb.init(project="ml-experiments", name="experiment-42")

# Configure Hamilton
config = {
    'learning_rate': 0.001,
    'batch_size': 32,
    'epochs': 10
}

# Log config to W&B
wandb.config.update(config)

dr = driver.Driver(config, experiment_module)

results = dr.execute(
    ['trained_model', 'validation_metrics'],
    inputs={'data_path': '/data/train.csv'}
)

# Log results to W&B
wandb.log({
    "val_accuracy": results['validation_metrics']['accuracy'],
    "val_loss": results['validation_metrics']['loss']
})

wandb.finish()

Flask Integration

"""Hamilton with Flask."""
from flask import Flask, request, jsonify
from hamilton import driver
import service_module

app = Flask(__name__)
service_driver = driver.Driver({}, service_module)

@app.route('/api/process', methods=['POST'])
def process_data():
    """Process data endpoint."""
    data = request.get_json()

    try:
        results = service_driver.execute(
            ['processed_result'],
            inputs=data
        )
        return jsonify(results)
    except Exception as e:
        return jsonify({'error': str(e)}), 500

@app.route('/health')
def health():
    return jsonify({'status': 'healthy'})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)

dbt Integration

Use Case: Hamilton for transformations, dbt for SQL

"""Hamilton after dbt transformations."""
import subprocess
from hamilton import driver
import post_dbt_module

def run_dbt() -> dict:
    """Run dbt pipeline."""
    result = subprocess.run(['dbt', 'run'], capture_output=True)
    return {'status': 'success' if result.returncode == 0 else 'failed'}

def dbt_output_path(run_dbt: dict) -> str:
    """Get dbt output location."""
    return './target/output.csv'

# Rest of Hamilton DAG uses dbt output
def post_dbt_analysis(dbt_output_path: str) -> pd.DataFrame:
    """Analyze dbt output."""
    return pd.read_csv(dbt_output_path)

Kedro Integration

"""Use Hamilton within Kedro pipelines."""
from kedro.pipeline import Pipeline, node
from hamilton import driver
import hamilton_transformations

def run_hamilton_node(**inputs):
    """Execute Hamilton as Kedro node."""
    dr = driver.Driver({}, hamilton_transformations)
    return dr.execute(['output'], inputs=inputs)

def create_pipeline(**kwargs) -> Pipeline:
    return Pipeline([
        node(
            func=run_hamilton_node,
            inputs=["raw_data", "parameters"],
            outputs="hamilton_results",
            name="hamilton_transformation"
        )
    ])

Best Practices

1. Initialize driver once - Reuse driver across requests in web services
2. Separate concerns - Orchestrator handles scheduling, Hamilton handles dataflow
3. Use async - FastAPI + async Hamilton for I/O-bound workflows
4. Track everywhere - Add HamiltonTracker to production integrations
5. Health checks - Expose health endpoints for monitoring
6. Error handling - Wrap Hamilton execution in try/except
7. Configuration - Pass environment-specific config to Hamilton

Choosing the Right Integration

Use Case	Tool	When to Use
Schedule pipelines	Airflow, Dagster	Daily/weekly batch processing
Serve predictions	FastAPI, Flask	Real-time ML inference
Interactive dashboards	Streamlit	Business intelligence, exploration
Development	Jupyter	Prototyping, analysis
Experiment tracking	MLflow, W&B	ML model development
SQL + Python	dbt	Most data in warehouse, some Python logic

Additional Resources

• For core Hamilton patterns, use /hamilton-core
• For observability, use /hamilton-observability
• Examples: github.com/apache/hamilton/tree/main/examples