Data Pipelines API
The Data Pipelines API allows you to create continuous data pipelines that export your Mixpanel data to external warehouses like BigQuery, Snowflake, S3, and more.Base URL
Authentication
Use Service Account or Project Secret with HTTP Basic Auth.Pipeline Types
Raw Exports
Export raw JSON data to cloud storage:- Amazon S3: Raw event data to S3 buckets
- Google Cloud Storage: Raw event data to GCS buckets
- Azure Blob Storage: Raw event data to Azure containers
Schematized Exports
Export structured data to data warehouses:- BigQuery: Schematized data to Google BigQuery
- Snowflake: Schematized data to Snowflake
- AWS Glue: Schematized data with AWS Glue catalog
- Azure Synapse: Schematized data to Azure
Available Endpoints
Pipelines Management
- List Pipelines: Get all pipelines for a project
- Create Pipeline: Set up a new data pipeline
- Edit Pipeline: Modify pipeline configuration
- Delete Pipeline: Remove a pipeline
- Pause/Resume Pipeline: Control pipeline execution
Warehouse Connectors
- List Imports: Get all warehouse imports
- Create Import: Configure warehouse data imports
- Update Import: Modify import settings
- Delete Import: Remove an import
Common Parameters
Your Mixpanel project ID
Pipeline type:
gcs-raw, s3-raw, azure-raw, bigquery, snowflake, aws, azure-blob, gcs-schemaWhat to export:
events or peopleDefault: eventsStart date in
YYYY-MM-DD formatEnd date in
YYYY-MM-DD format. Leave empty for continuous export.Export frequency:
hourly or dailyDefault: dailyQuick Start
Best Practices
Start with a trial pipeline
Start with a trial pipeline
Use
trial: true to test with 30 days of data before committing to a full pipeline.Choose the right schema type
Choose the right schema type
- Monoschema: All events in one table (easier to query)
- Multischema: Each event type in its own table (better for large volumes)
Enable sync for data integrity
Enable sync for data integrity
Set
sync: true to automatically update exported data when changes occur in Mixpanel.Use appropriate frequency
Use appropriate frequency
- Hourly: For real-time analytics needs
- Daily: For most analytical use cases (more efficient)