Problem Statement:
Client organization currently relies on SAS (Statistical Analysis System) for extensive data analytics, statistical modelling, and data management. To improve scalability, reduce costs, and enhance performance, we aim to migrate these existing SAS workloads to our Azure Databricks environment. Databricks, which utilizes Apache Spark, provides a strong, scalable platform for both data engineering and data science workflows.
The core objective is to migrate existing SAS workloads to Azure Databricks with minimal disruption while ensuring data integrity, performance optimization, and leveraging Databricks’ capabilities for advanced analytics and machine learning.
Solution Overview:
- Requirement Analysis and Stakeholder Engagement:
Conduct detailed requirement analysis sessions with stakeholders.
Document current SAS workflows, dependencies, and performance benchmarks.
Define success criteria and key performance indicators (KPIs) for the migration. - Code Translation and Optimization:
Translate SAS scripts to PySpark/Spark code.
Optimize the translated code to exploit Spark’s distributed computing capabilities.
Ensure equivalent or improved functionality and performance in Databricks. - Data Migration:
Migrate data from on-premises or other storage systems to Azure or other Databricks-compatible storage solutions.
Ensure data integrity and consistency through checksum validation and error checking mechanisms. - Integration and Testing:
Integrate translated code into the Databricks environment.
Implement automated testing scripts to validate functionality and performance.
Conduct performance tuning to meet or exceed original SAS workload performance metrics. - Deployment and Monitoring:
Deploy the migrated workloads to the production Databricks environment.
Set up monitoring using Databricks tools and Azure CloudWatch to track performance and detect issues.
Implement automated alerts for performance degradation or failures. - Documentation and Training:
Document the entire migration process, including technical details of the new workflows.
Provide comprehensive training sessions and materials for end-users and support staff.
Tech Stack Leveraged:
Azure Databricks, Apache Spark, PySpark, Azure Storage, Azure CloudWatch, and Python.
Benefits Delivered:
• Migrating SAS workloads to Azure Databricks leverages Apache Spark’s distributed computing capabilities, enabling the organization to handle larger datasets and more complex analytics tasks with improved performance.
• Moving to a cloud-based environment like Azure Databricks reduces the need for expensive on-premises infrastructure, leading to significant cost savings in hardware, maintenance, and licensing fees associated with SAS.
• The migration allows the organization to tap into Databricks’ advanced analytics, machine learning, and AI capabilities, providing a more versatile and powerful platform for data-driven decision-making.
• Azure Databricks offers seamless integration with other Azure services and supports collaborative data science and engineering workflows, enhancing team productivity and accelerating time-to-insight.
• The project includes setting up monitoring tools and automated alerts, ensuring that the migrated workloads run reliably in production with proactive detection and resolution of performance issues.
