Javatpoint: Azure Data Factory
Azure Data Factory (ADF) is a managed cloud service designed for hybrid data integration, enabling the creation of ETL (Extract, Transform, Load) pipelines via a visual, code-free interface. It orchestrates data movement and transformation across varied sources using key components like pipelines, linked services, and Integration Runtimes. For more details, visit Microsoft Learn . Azure Data Factory - Data Integration Service
Triggers
: Triggers determine when a pipeline execution should start, whether on a set schedule, a manual request, or an event-based occurrence. The ADF Workflow Process javatpoint azure data factory
// Print pipeline run status for (PipelineRun pipelineRun : pipelineRuns) System.out.println(pipelineRun.status()); Azure Data Factory (ADF) is a managed cloud
Azure Data Factory - Data Integration Service - Microsoft Azure Control plane: REST API and portal for authoring
Security and Compliance
Step 5: Build a Pipeline with Copy Activity
- Control plane: REST API and portal for authoring and management.
- Data plane: Executes data movement and transformation via Integration Runtimes.
- Global service endpoints: Manage orchestration, metadata, and monitoring.
- Components: Pipelines, Activities, Datasets, Linked Services, Integration Runtimes, Triggers, Monitoring.
- Create a Data Factory instance in the Azure portal.
- Define Linked Services for each source and destination (e.g., Blob Storage, SQL Server).
- Create Datasets pointing to specific files or SQL tables.
- Build a Pipeline with a Copy Activity to move data from source to sink.
- Add Transformation Activities (e.g., Data Flow or Databricks Notebook) to clean/aggregate data.
- Attach a Trigger (e.g., schedule at 1 AM daily) to automate the pipeline.
- Monitor pipeline runs using Azure Monitor, SDKs, or built-in monitoring views.
@pipeline().parameters.tableName@dataset().folderPath