Tracking Azure Data Factory Migration Progress with Audit Tables
Data migration is a critical process, and ensuring its accurate tracking can significantly impact its success. In this article, we will explore how Azure Data Factory provides an efficient solution for tracking data migration progress. By leveraging its powerful features, we can easily monitor the status of each step in the migration process and take appropriate actions to address any failures or incomplete steps, achieving a seamless monitoring and troubleshooting experience.
Our Data Migration Framework
At Zingmind Technologies our data migration framework, seamlessly integrated into Azure Data Factory (ADF), is designed to simplify the migration of data from diverse sources straight to the Azure Cloud. This comprehensive solution offers specific configuration tables for effortless setup, a centralized control table for handling source and destination data, and, crucially, a sophisticated tracking system to ensure the smooth progression of every migration step. With email notifications for pipeline outcomes, Azure Data Factory’s orchestration provides dependable execution. Collaboration with PowerShell scripts facilitates error resolution, and the ability to rerun improves robustness. A user-friendly PowerShell script simplifies pipeline control and gives status updates, while an upsert migration approach maintains data synchronization. The goal of this framework is to provide a comprehensive approach to reliable and effective data migration.
Detailed Step-by-Step Monitoring with Dedicated Audit Tables
Understanding the necessity for granular tracking, a specialized audit table has been put in place. This tables are structured to meticulously capture the status of each migration step. Such an arrangement is essential to swiftly identify and address any glitches encountered during the migration phase.
The audit tables comprises:
- Step_id: Unique identifier for every step.
- Process_id: The process ID associated with the step.
- Status: Real-time status (such as success, failure, or in-progress).
- start_Datetime: The timestamp marking the initiation of the step.
- end_Datetime: The timestamp recording the completion of the step.
- Description: A succinct explanation denoting the step’s relevance.
Tracking Process-Level Failures with “ETL_RUN_STATUS” Audit Table
To capture process-level failures, we have introduced the “ETL_RUN_STATUS” table. This table records information about the status of each migration process. In case of a failure being encountered by a process, it is logged here, thereby enabling a high-level overview of the migration’s progress.
The “ETL_RUN_STATUS” table includes Process_id, Status, start_Datetime, end_Datetime, Description.
- Process_id: 12345
- Status: Failure
- start_Datetime: 2023-09-14T08:00:00
- end_Datetime: 2023-09-14T08:15:00
- Description: Error encountered while transferring data from source to destination.
Enhancing Clarity with Descriptive Entries
Static descriptions for each completed step have been integrated into the audit tables. These narrative entries clarify the purpose and functionality of every step, enriching the audit trail.
Centralizing Data with the “ETL_STEP_RUN_STATUS” Audit Table
The “ETL_STEP_RUN_STATUS” audit table acts as the cornerstone of our tracking system. As a centralized database, it methodically stores every data point related to the migration steps. This centralized approach facilitates effortless data accessibility and analysis.
For instance, a failed migration during Step 5 will reflect in the audit table with details like:
- Step_id: 5
- Process_id: 12345
- Status: Failure
- start_Datetime: 2023-07-20T12:00:00
- end_Datetime: 2023-07-20T12:15:00
- Description: Encountered an issue while fetching data from the legacy system.
Enhancing Efficiency with Re-Execution of Failed Steps
Our tracking solution goes beyond mere tracking and incorporates an advanced feature that allows for the re-execution of failed steps. In scenarios where a process step’s status signifies failure, we have designed the system to automatically rerun only the specific failed steps. This approach minimises redundancy and ultimately saves valuable time and resources.
Example of Rerunning Failed Steps
To illustrate this advanced capability, let’s consider a scenario where a process consists of seven steps (let’s call it Process 4). In the first pipeline run, three steps successfully execute while the remaining four encounter failures. In the subsequent run, only the four previously failed steps will be executed.
Furthermore, if during the second run, two steps fail and two succeed among the four previously failed steps, there will be no re-execution of the steps that have already been marked as completed. This optimised approach removes redundancies and allows for a streamlined tracking experience.
Efficiently tracking data migration progress is a vital aspect of any migration process. With Azure Data Factory, we have introduced a robust solution that empowers users to monitor the status of each step, troubleshoot failures, and re-execute failed steps if needed. By leveraging the capabilities of Azure Data Factory, data migration becomes a seamless and well-tracked process, ultimately leading to enhanced operational efficiency.