> ## Documentation Index > Fetch the complete documentation index at: https://support.entegrata.com/llms.txt > Use this file to discover all available pages before exploring further. # Pipeline Management > Comprehensive guide to creating, managing, and monitoring Entegrata data pipelines ## Overview Pipeline management in Entegrata provides a centralized interface for organizing and controlling your data integration workflows. From this dashboard, you can create new pipelines, monitor their execution status, deploy changes to production, and manage the entire lifecycle of your data processing jobs. ## Accessing Pipeline Management To access the pipeline management interface: 1. Log in to the [Entegrata Admin Portal](https://admin.entegrata.com) 2. Navigate to the **Pipelines** tab in the main navigation 3. You'll see the pipeline list view with all pipelines in your instance ## Pipeline List View The pipeline list view displays all pipelines with the following information: ### Pipeline Columns The descriptive name of the pipeline (e.g., "Client Data Processing Pipeline") A brief description of what the pipeline does and what data it processes When the pipeline configuration was last updated When the pipeline was last executed (manually or automatically) Current pipeline status combining deployment status and run status: * **Draft** - Pipeline has not been deployed yet * **Deployed** - Pipeline is deployed and ready to run * **Active** - Pipeline is deployed and scheduled to run automatically * **Paused** - Pipeline is deployed but paused from automatic execution * **Running** - Pipeline is currently executing * **Failed** - Pipeline last run encountered errors * **Success** - Pipeline last run completed successfully Quick action menu for pipeline operations (see below) ### Pipeline Actions Menu Each pipeline has an actions menu (⋮) that provides quick access to common operations: * **Edit** - Modify pipeline name and description * **Duplicate** - Create a copy of the pipeline with all its mappings * **Run** - Execute the pipeline immediately (manual trigger) * **Dry Run** - Test the pipeline without writing data to production * **Deploy** - Publish all mapped data types to production * **Pause/Activate** - Toggle automatic execution on or off * **View Mappings** - Navigate to the data mapping editor * **Delete** - Remove the pipeline (requires confirmation) Deleting a pipeline is permanent and cannot be undone. Ensure you have backups or duplicates if needed before deleting. ## Pipeline Lifecycle Understanding the pipeline lifecycle helps you manage your data integration effectively: When first created, pipelines are in draft status. You can configure data type mappings, but the pipeline is not yet available for execution in production. After deploying, the pipeline's DLT scripts are generated and published to your data warehouse. The pipeline can now be executed but won't run automatically unless activated. Active pipelines are deployed and configured to run automatically based on their trigger settings (e.g., scheduled execution). Paused pipelines remain deployed but won't execute automatically. This is useful for temporarily disabling a pipeline without deleting it. ## Pipeline Trigger Types When creating or editing a pipeline, you can configure how it should be triggered: ### Manual Trigger Pipelines run only when explicitly triggered by a user through the "Run" action. Best for: * Ad-hoc data migrations * Testing and development * One-time data loads ### Event-Driven Trigger Pipelines run automatically when specific events occur (e.g., new data detected in source). Best for: * Real-time or near-real-time data processing * Event-based workflows * Triggered by external systems Event-driven triggers require additional configuration with your Collector and may not be available for all data sources. ### No Trigger (Manual Only) Pipeline has no automatic trigger configured and must be run manually. ## Searching and Filtering Pipelines Use the search bar at the top of the pipeline list to quickly find pipelines by name. The search supports: * Partial name matching * Case-insensitive search * Real-time filtering as you type The pipeline list is paginated to handle large numbers of pipelines efficiently. Use the page controls at the bottom to navigate through multiple pages. ## Pipeline Execution Monitoring Monitor pipeline execution through several indicators: ### Run Status Indicators * **Success** (Green) - Pipeline completed without errors * **Running** (Blue) - Pipeline is currently executing * **Failed** (Red) - Pipeline encountered errors during execution * **Pending** (Gray) - Pipeline is queued for execution ## Best Practices Follow these best practices for effective pipeline management: 1. **Use Descriptive Names** - Choose pipeline names that clearly indicate their purpose and data scope 2. **Monitor Regularly** - Check pipeline status after scheduled runs to catch issues early 3. **Start Small** - Begin with a single data type, validate it works, then add more mappings ## Common Operations ### Creating a New Pipeline Step-by-step guide to creating a new pipeline from scratch ### Modifying Existing Pipelines Update pipeline name, description, and settings Create a copy of an existing pipeline Move mapping configuration from one pipeline into another See what differs between two pipelines, read-only ### Execution and Deployment Execute pipelines manually or on schedule Publish pipelines to production Pause, activate, or monitor pipeline status ## Related Topics Learn how to configure data type mappings within pipelines Create and manage the data types your pipelines process ## Troubleshooting ### Pipeline Won't Run * Check that the pipeline is deployed (not in draft status) * Verify the pipeline is activated (not paused) * Ensure your Collector data sources are connected and accessible ### Pipeline Failing Consistently * Check source data quality and schema changes * Verify required fields have valid mappings or default values * Review data type field requirements ### Pipeline Running Too Long * Check for inefficient field mappings or complex transformations * Verify source queries are optimized with appropriate filters * Review data volumes and adjust processing strategies For additional support, contact your Entegrata administrator or submit a ticket through the [Support Portal](https://support.entegrata.com).