Auditing ADF Activities with Diagnostic Logs

In this lesson, we'll explore the crucial aspects of auditing and logging in ADF, providing valuable insights into data integration workflows and ensuring compliance with regulatory requirements.

Auditing and logging Azure resources

Azure services efficiently capture and store logs in a centralized repository hosted in Azure Monitor. The log analytics workspace serves as the transactional data store within Azure Monitor, enabling seamless querying and analysis of logs using Kusto Query Language (KQL) or the log analytics dashboard. Furthermore, logs can be conveniently exported to dashboards and BI tools like Power BI for extended visibility and analysis.

Azure Log Analytics workspace

Azure Log Analytics workspace is a centralized platform in Azure that collects, stores, and analyzes log and telemetry data from various Azure resources and systems, providing a unified view for real-time monitoring, advanced analytics, and troubleshooting. When connected to ADF, it configures diagnostic settings to enable logging for ADF components, streamlining access to logs for comprehensive monitoring and troubleshooting.

The workflow above illustrates how Azure services capture and store logs in a centralized repository hosted in Azure Monitor. The log analytics workspace serves as the transactional data store within Azure Monitor, storing all the logs and enabling users to query and analyze them using Kusto Query Language (KQL) or the log analytics dashboard. Additionally, logs can be exported to dashboards and BI tools like Power BI for further insights and analysis.

Diagnostic monitoring of data pipelines

Let’s try and understand how auditing and logging concepts can be translated to data pipelines and what are some of the key aspects of diagnostic monitoring concerning ADF.

1. Data pipeline design: The first step in auditing and logging data pipelines is to ensure that the pipeline is designed in a way that supports auditing and logging. This includes defining the data inputs, outputs, transformations, and storage, as well as identifying the key metrics and indicators that will be used to evaluate the pipeline’s performance and quality.

2. Data lineage: Auditing and logging data pipelines requires the ability to trace the lineage of data as it flows through the pipeline. This means capturing information about the source of the data, any transformations or enrichments that are applied to the data, and the ultimate destination of the data.

3. Data quality checks: Auditing data pipelines requires verifying that the data flowing through the pipeline is accurate, complete, and consistent. This includes performing data quality checks at each stage of the pipeline to identify any issues that could impact the reliability or validity of the data.

4. Compliance and security: Auditing and logging data pipelines is crucial for ensuring compliance with regulatory requirements and protecting data security and privacy. This involves monitoring the pipeline for any suspicious activity, identifying and mitigating potential security risks, and tracking who has accessed the data and what changes have been made.

5. Logging and monitoring: Logging data pipelines involves recording information about pipeline activities in a way that can be easily audited and analyzed. This includes logging information about data inputs and ...