Example Workflows

Real-world examples to get you started quickly

Copy these examples, customize them for your needs, and deploy production-ready workflows in minutes.

ETL Pipeline Examples

Database Transfer

Description:

Copy data from MySQL to PostgreSQL

# AI Prompt:

"Extract data from MySQL users table and load to PostgreSQL daily at 2 AM"

Time to Create: 2 minutes

File Processing

Description:

Process CSV files and load to BigQuery

# AI Prompt:

"Process CSV files from S3, clean the data, and load to BigQuery"

Time to Create: 3 minutes

API Data Sync

Description:

Sync data from external API to database

# AI Prompt:

"Fetch data from REST API every hour and store in PostgreSQL"

Time to Create: 2 minutes

Machine Learning Examples

Model Training

Description:

Train ML model and validate performance

# AI Prompt:

"Train ML model on customer data, validate performance, and save results"

Time to Create: 5 minutes

Feature Engineering

Description:

Process features for ML pipeline

# AI Prompt:

"Process raw data, create features, and store in feature store"

Time to Create: 4 minutes

Data Quality Examples

Data Validation

Description:

Validate data quality and send alerts

# AI Prompt:

"Run data quality checks on customer data and send Slack alerts for failures"

Time to Create: 3 minutes

Anomaly Detection

Description:

Detect anomalies in data

# AI Prompt:

"Monitor sales data for anomalies and send alerts"

Time to Create: 4 minutes

Business Intelligence Examples

Report Generation

Description:

Generate and email reports

# AI Prompt:

"Generate daily sales report and email to stakeholders"

Time to Create: 2 minutes

Dashboard Updates

Description:

Update dashboards with fresh data

# AI Prompt:

"Update dashboard metrics every hour with latest data"

Time to Create: 3 minutes

How to Use These Examples

1
Choose an example

Match your use case

2
Copy the AI prompt

Paste into DAGForge

3
Customize parameters

For your environment

4
Review generated code

Make adjustments

5
Deploy to production

Monitor execution

Best Practices

Do's

Start with basic examples and gradually add complexity
Always test your DAG locally before deploying
Give your DAGs and tasks descriptive names

Don'ts

Don't skip testing - always validate before production
Avoid overly complex workflows in single DAGs
Don't ignore error handling and retry logic

Need More Examples?

Check out our Template Library for more pre-built workflows, or contact us at [email protected] for custom examples.