SSIS to ADF Migration Questions
ADF Migration Strategy and Planning
- 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.
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.
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.
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.
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.
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.
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.
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.