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ADF Migration Strategy and Planning

🔎 What are the key considerations when planning a migration of SSIS packages to ADF?

  • Assess the complexity and dependencies of existing SSIS packages.
  • Identify which packages can be migrated as-is using Azure-SSIS Integration Runtime and which need to be rebuilt as ADF pipelines.
  • Plan for data source and destination compatibility (e.g., on-premise vs. cloud).
  • Consider performance, scalability, and cost implications of the migration.
  • Prioritize based on business criticality and frequency of execution.
  • Start with simpler packages to build confidence and gain experience.
  • Address complex or high-impact packages later, with a well-tested approach.

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Technical Migration Scenarios

  • Replace the Script Component with ADF Mapping Data Flows or Azure Functions for custom logic.
  • Use Data Flow transformations (e.g., Derived Column, Aggregate) for simpler logic.
  • If the Script Component interacts with external systems, use Web Activities or Custom Activities in ADF.
  • Rebuild the functionality using native ADF components or Azure Functions.
  • If the third-party component is critical, consider using Azure-SSIS Integration Runtime to run the package as-is.
  • Evaluate if the third-party component has a cloud-native alternative.
  • Use ADF’s Change Data Capture (CDC) capabilities or Temporal Tables in Azure SQL Database.
  • Implement incremental load patterns using Watermarking or Lookup Activities in ADF.
  • Use Mapping Data Flows to handle transformations for incremental data.

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Performance Optimization in ADF Migration

  • Optimize Data Flows by reducing unnecessary transformations and using partitioning.
  • Use Integration Runtime scaling (e.g., increase Data Integration Units or use Self-Hosted IR for on-premise sources).
  • Leverage PolyBase or COPY INTO for high-performance data loading into Synapse or SQL Database.
  • Use partitioning in source and target systems to parallelize data transfer.
  • Implement incremental loads to reduce the volume of data transferred.
  • Use compression and binary format for efficient data transfer.

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Error Handling and Monitoring

  • Use ADF’s Fault Tolerance settings in Data Flows to handle errors (e.g., skip or redirect erroneous rows).
  • Implement Activity Retries and Timeouts for transient failures.
  • Use Azure Monitor and Log Analytics for centralized logging and alerting.
  • Use Azure Monitor to track pipeline runs, durations, and failures.
  • Set up Alerts for pipeline failures or performance bottlenecks.
  • Use Log Analytics to analyze detailed execution logs and troubleshoot issues.

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Data Security and Compliance

  • Use Azure Key Vault to store and manage sensitive information (e.g., connection strings, passwords).
  • Implement Data Masking or Encryption for sensitive data in transit and at rest.
  • Ensure compliance with data protection regulations (e.g., GDPR, HIPAA) by using Azure Policy and Role-Based Access Control (RBAC).
  • Use Data Validation Activities to compare source and target data.
  • Implement Checksum or Row Count validation to ensure data completeness.
  • Use Transaction Management in target systems (e.g., Azure SQL Database) to ensure atomicity.

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Advanced ADF Features

  • Use Source and Sink transformations to read and write data.
  • Apply Transformations (e.g., Derived Column, Aggregate, Join) to replicate SSIS logic.
  • Use Parameterization and Expression Builder for dynamic configurations.
  • Use Watermarking to track the last processed record.
  • Implement Lookup Activities to compare source and target data.
  • Use Filter Transformation in Mapping Data Flows to process only new or updated records.

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Real-World Migration Scenarios

  • Use Self-Hosted Integration Runtime to connect to the on-premise SQL Server.
  • Rebuild the transformation logic using Mapping Data Flows or Databricks.
  • Load the transformed data into the cloud data warehouse (e.g., Azure Synapse or SQL Database) using Copy Data Activity.
  • Use FTP Linked Service in ADF to connect to the FTP server.
  • Use Copy Data Activity to transfer files to Azure Blob Storage or ADLS.
  • Optionally, use Azure Logic Apps or Azure Functions for advanced file processing.

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Post-Migration Validation

  • Compare source and target data using Data Validation Activities or custom queries.
  • Validate pipeline execution logs for errors or performance issues.
  • Conduct User Acceptance Testing (UAT) with stakeholders to ensure business requirements are met.
  • Maintain a backup of the original SSIS packages and databases.
  • Use Version Control (e.g., Git) for ADF pipelines to revert changes if needed.
  • Implement Automated Testing to detect issues early in the migration process.
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