What are the triggers in ADF?

Different types of triggers in Azure Data Factory (ADF some common use cases:

1. Schedule Trigger:

Use Case: Daily ETL Pipeline Refresh

A scheduled trigger can be used to automatically execute an ETL (Extract, Transform, Load) pipeline in Azure Data Factory at a specific time each day. This ensures that the data is refreshed and up-to-date for reporting and analysis purposes.

2. Event Trigger:

Use Case: Real-time Data Ingestion

An event trigger can be configured to monitor a specified data source (e.g., Azure Blob Storage, Azure Event Hubs) for new data arrival. When new data is detected, the trigger can initiate an ADF pipeline to ingest and process the data in near real-time, enabling timely analytics or downstream processing.

3. Data Driven Trigger:

Use Case: Dynamic Partitioning

In scenarios where data arrives in different partitions or folders based on certain criteria (e.g., date, region), a data-driven trigger can be used. For example, if new data is added to a specific folder in Azure Data Lake Storage, the trigger can automatically start a corresponding ADF pipeline to process only the newly arrived data, optimizing resource utilization and reducing processing time.

4. Manual Trigger:

Use Case: Ad-hoc Data Processing

Sometimes, data processing tasks need to be initiated manually, such as on-demand data validations or one-time data migrations. A manual trigger allows users to trigger ADF pipelines manually through the Azure portal or programmatically via REST API, providing flexibility and control over when the pipeline execution occurs.

5.Tumbling Window Trigger:

Use Case: Rolling Window Aggregations

For scenarios requiring periodic aggregation of data over fixed time intervals (e.g., hourly, daily), a tumbling window trigger can be employed. This trigger can schedule ADF pipelines to run at regular intervals, aggregating data within each window to produce summary statistics, trends, or reports.