What is Azure Data Factory

Microsoft Azure Data Factory is a cloud-based data integration service provided by Microsoft Azure. It enables you to create, manage, and automate data workflows that move and transform data from different sources to various destinations. Essentially, ADF allows you to design, orchestrate, and manage data pipelines, making it easier to work with large volumes of data across on-premises and cloud environments.

In Microsoft Azure Data Factory, a pipeline is a set of activities that run in a specific sequence or in parallel to process data. Each pipeline can have parameters, triggers, and can be scheduled or executed on demand.

Why Azure Data Factory Monitoring Matters

As organizations scale their cloud data operations, Azure Data Factory (ADF) often plays a critical role in orchestrating complex data pipelines across hybrid environments.

However, without a robust Azure Data Factory monitoring integration, teams risk losing visibility into pipeline run metrics, trigger failures, and integration runtime performance—which can lead to delayed insights, compliance risks, or broken SLAs.

Schematic diagram showing how Azure Data Factory integrates with Azure SQL, on-prem SQL Server in hybrid environments and the role of SSIM

Figure 1: SSIS is Microsoft’s on-premises ETL (Extract, Transform, Load) tool used for building high-performance data integration workflows. Azure Data Factory allows you to lift and shift existing SSIS packages into the cloud where ADF runs them inside the Azure-SSIS Integration Runtime

Native tools such as Azure Monitor and Log Analytics can provide a starting point, but often lack the depth needed for end-to-end observability or root cause analysis across interconnected workloads. This is why real-time alerts, role-based monitoring access, and support for advanced analytics (such as anomaly detection) are essential.

Effective Azure Data Factory monitoring can enable data engineering teams to proactively detect anomalies, reduce pipeline downtime, and improve performance. With the right monitoring integration strategy, enterprises can turn ADF into a transparent, accountable, and highly optimized data orchestration layer.

Monitoring Azure Data Factory enables you to:

  • Ensure Data pipeline health
  • Track the movement of data
  • Optimize resources
  • Ensure data security and compliance

New for eG Enterprise v7.5 – Azure Data Factory Monitoring

From version 7.5 eG Enterprise has added custom-built monitoring of Azure Data Factory. Out-of-the-box thresholds and alerts are automatically set up to give you instant notification of problems with ADF.

Screenshot showing eG Enterprise providing an overview of monitoring of Azure Data Factory including pipeline runs success/failures

Figure 2: Instantly identify failed pipeline runs, activity run failures and trigger run failures

Screenshot showing eG Enterprise monitoring SSIS integration with Azure Data Factory in real time including runtime execution metrics and runtime resource metrics

Figure 3: Identify resource utilization and failed executions when SSIS is integrated with Azure Data Factory

Screenshot showing MVNET Integration with Azure Data Factory being monitored in real time by eG Enterprise

Figure 4: Identify failures and bottlenecks when MVNet is integrated with Azure Data Factory

Screenshot of eG Enterprise monitoring an Airflow integration with Azure Data Factory in real time

Figure 5: Identify failures or bottlenecks when Airflow is integrated with Azure Data Factory

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Screenshot showing eG Enterprise monitoring Azure Data Factory pipeline runs - metrics include long running pipelines and in progress runs

Figure 6: Be alerted to pipeline run failures and queued pipeline runs. Identify the pipelines with maximum pipeline run failures.

Screenshot of eG Enterprise monitoring activity runs by pipeline including data read / write and data flow execution metrics

Figure 7: Data pipeline monitoring. Be alerted to activity run failures and queued activity runs for each pipeline. Identify the pipelines with maximum activity run failures.

With eG Enterprise’s proactive monitoring for Azure Data Factory you can now instantly:

  • Identify the count of pipelines and triggers created on Microsoft Azure Data Factory
  • Detect the pipeline with maximum number of failed runs/queued runs
  • Identify the pipeline with maximum number of failed activity runs/queued activity runs
  • Understand integration runtime performance and how well pipelines and triggers are established when Azure Data Factory is integrated with SSIS, MVNet, Apache Airflow etc.

To learn more about eG Enterprise’s capabilities for monitoring Microsoft Azure and Azure Services, please see: Azure Cloud Monitoring Tools for IaaS, PaaS, SaaS | eG Innovations.

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About the Author

Babu is Head of Product Engineering at eG Innovations, having joined the company back in 2001 as one of our first software developers following undergraduate and masters degrees in Computer Science, he knows the product inside and out. Based within our Singapore R&D Management team, Babu has undertaken various roles in engineering and product management becoming a certified PMP along the way.