What would trigger in azure data lake that would execute a load to the price table once a csv file lands in a designated "to_load" directory look like?

Here are two options for triggering the load of the "price" table in Azure Synapse Analytics when a new CSV file lands in the designated "to_load" directory in Azure Data Lake Storage (ADLS):

  1. Using Azure Data Factory (ADF):
    • Create a Pipeline: Set up an ADF pipeline with two main activities:
      • Copy Activity: Configured to copy the new CSV file from the "to_load" directory to a temporary folder within ADLS.
      • Azure Synapse Analytics Activity: Linked to the pipeline that loads data from the temporary folder into the "price" table using your chosen method (Python or T-SQL).
    • Triggering:
      • Event Trigger: Configure the pipeline to start automatically when a file arrives in the "to_load" directory. This trigger uses Azure Event Grid notifications from ADLS to detect new files.
      • Schedule Trigger: Alternatively, you can schedule the pipeline to run periodically and check for new files in the "to_load" directory.
  2. Using Azure Functions:
    • Develop an Azure Function: Write an Azure Function triggered by Azure Blob Storage notifications when a new file arrives in the "to_load" directory.
    • Function Logic:
      • Download the new CSV file from ADLS.
      • Execute the data loading process into the "price" table using your chosen method (e.g., calling an ADF pipeline or executing T-SQL commands directly).
      • Optionally, delete the processed CSV file from "to_load" for data housekeeping.

Comparison:

Feature Azure Data Factory (ADF) Azure Functions
Workflow Complexity More complex setup but visually clear Simpler code but requires Azure Function development
Scalability Highly scalable and managed service Serverless and cost-effective for sporadic events
Customization Extensive configuration options and flexibility Less customizable but focused on event-driven triggers

Additional Considerations:

  • Implement error handling and retry mechanisms in your chosen solution to ensure reliable data loading even with transient failures.
  • Consider data validation processes to ensure the integrity of the CSV files before loading them into the "price" table.
  • Utilize logging and monitoring tools within ADF or Azure Functions to track the execution of the trigger and data loading process.

Choose the trigger solution that best aligns with your needs and skills. ADF offers a robust and scalable approach for complex data pipelines, while Azure Functions provides a streamlined and serverless option for event-driven triggers.