Azure Data Factory for Beginners - Build Data Ingestion - Understanding the Pipeline Log and Related Tables

The video tutorial covers the pipeline log table used for auditing data ingestion, explaining its standalone nature and relationship with the batch run table. It details how to query these tables, interpret batch run statuses, and measure latency. The tutorial also discusses the pipeline log's role in tracing log files and its integration with Azure Data Factory.

What is the primary purpose of the pipeline log table?

• To manage user permissions

• To store user data

• To track ingestion pipelines

• To generate reports

Why is the pipeline log table considered standalone?

• It is not used in any queries

• It stores only temporary data

• It processes multiple transactions concurrently

• It has no relationship with any other table

What does the run ID in the batch run table signify?

• The server location

• The type of data being processed

• The specific batch run of an ingestion pipeline

• The user who initiated the run

How is the duration of a batch run calculated?

• By subtracting the start time from the end time

• By adding the start and end times

• By multiplying the start time by the end time

• By dividing the start time by the end time

What does a 'completed successfully' status indicate in a batch run?

• The run encountered an error

• The run finished without issues

• The run is currently in progress

• The run has not started

What is the role of the pipeline ID in the pipeline log?

• To determine the server location

• To identify the user

• To calculate the run duration

• To trace log files for a specific run

What information does the source to target view provide?

• Data ingestion paths

• Error logs

• User permissions

• Server configurations