As organizations increasingly shift toward cloud-based data platforms, many businesses are moving away from legacy tools like SAS and SAS Enterprise Guide in favor of more scalable and cost-efficient solutions like the Snowflake AI Data Cloud. This transition offers numerous benefits, including enhanced performance, reduced infrastructure costs, and improved data flexibility. However, migrating from SAS to Snowflake requires careful planning and execution.
As a consultant specializing in data migrations, I have written the following article to outline the essential steps, challenges, and best practices to ensure a smooth transition.
Understanding the Migration Landscape
SAS has long been a powerful tool for data analytics and processing, but its on-premise nature and licensing costs can pose challenges for modern data-driven organizations. Snowflake, on the other hand, provides a cloud-native architecture that allows for seamless scalability, built-in data sharing, and better integration with modern analytics tools.
However, migrating from SAS to Snowflake is not a simple lift-and-shift process; it involves data transformation, code refactoring, and workflow reengineering to fully leverage Snowflake’s capabilities.
Lift-and-Shift vs. Full Modernization
When transitioning from SAS to Snowflake, businesses often face a critical decision: should they lift and shift their existing SAS processes as-is, or should they fully modernize their data workflows to take advantage of Snowflake’s optimized architecture?
While both approaches have their merits, neither is usually the best solution on its own. A pure lift-and-shift can lead to inefficiencies, as legacy code may not fully utilize Snowflake’s performance advantages. Conversely, a complete modernization can be time-consuming and may introduce unnecessary complexity if not done thoughtfully.
This is where human expertise becomes indispensable. A person is still needed between the seat and the keyboard to interpret which elements can be easily migrated and which require modernization to align with new business practices that have evolved over the years.
By carefully balancing automation with strategic intervention, organizations can ensure they get the best of both worlds—migrating efficiently while modernizing key components for future success.
Step-by-Step Migration Approach
1. Assessing the Current Environment
Before starting the migration, it’s crucial to conduct a thorough assessment of the existing SAS environment. This includes identifying all SAS programs, stored processes, and macros in use, as well as understanding data dependencies and workflows. Businesses should inventory their current ETL processes, data sources, and reporting needs to determine the complexity of the migration.
2. Planning the Migration Strategy
A well-defined migration strategy is key to success. Organizations must choose between a lift-and-shift approach—where SAS processes are moved with minimal changes—or a complete reengineering approach, which involves redesigning ETL workflows for Snowflake. Selecting the right tools for data migration, such as Snowflake’s Snowpipe or third-party ETL solutions, is also critical at this stage. Additionally, data modeling considerations, such as schema design and performance optimization, should be planned to ensure Snowflake’s full potential is realized.
3. Extracting Data from SAS
Migrating data from SAS to Snowflake requires careful handling, especially for large datasets. SAS datasets need to be exported into a format that Snowflake can efficiently process, such as CSV, Parquet, or Avro. Proper indexing, partitioning, and compression techniques should be used to optimize data loading. Additionally, ensuring data quality and consistency during extraction is essential to prevent discrepancies in the new system.
4. Transforming SAS Code & ETL Processes
One of the most complex aspects of migration is converting SAS-specific scripts, macros, and ETL processes into Snowflake-compatible SQL or Python scripts. SAS’s proprietary syntax and statistical functions may need to be rewritten using Snowflake SQL, dbt (data build tool), or other modern data transformation frameworks. Performance tuning, query optimization, and the use of Snowflake’s built-in features, such as clustering and materialized views, can help streamline this process.
5. Rebuilding Analytics & Reporting
Many organizations rely on SAS for reporting and analytics. As part of the migration, companies must evaluate alternative reporting tools such as Tableau, Power BI, or Snowflake’s built-in analytics features. Snowflake also supports integration with Python and R for advanced statistical analysis. Ensuring that key business reports and dashboards are effectively rebuilt in the new system will help maintain continuity and user satisfaction.
6. Testing & Validation
Testing is a critical phase in the migration process. It is essential to validate data accuracy by comparing outputs between SAS and Snowflake to ensure consistency. Performance benchmarking should be conducted to confirm that queries and reports run efficiently in Snowflake. User acceptance testing (UAT) should also be performed with business stakeholders to ensure that all critical use cases are met before fully transitioning to the new platform.
Overcoming Common Migration Challenges
Migrating from SAS to Snowflake presents several challenges, such as handling complex SAS macros, ensuring that statistical functions translate accurately, and managing organizational change.
Many SAS users are accustomed to procedural programming, whereas Snowflake relies on set-based SQL logic. Providing proper training and documentation for teams transitioning to Snowflake can help mitigate these challenges.
Additionally, governance, security, and access control should be carefully managed to align with compliance requirements.
Accelerating SAS to Snowflake Migration with AI
At Hakkoda, we’ve built AI-powered SAS migration accelerators that streamline every phase of the migration approach above—from discovery, to rationalization, to conversion, to migration, to testing—eliminating months of manual work.
How It Works:
- Discovery & Rationalization – AI scans the SAS environment, identifying all scripts, macros, and dependencies. It flags redundant pipelines and inefficiencies, ensuring only essential, optimized code moves to Snowflake.
- Automated Code Conversion – AI translates SAS scripts into Snowflake SQL, Snowpark Python, or dbt, handling macros, PROC statements, and transformations automatically.
- Data Transformation & Migration – AI ensures schema alignment, data type conversion, and Snowflake performance optimization, eliminating manual tuning.
- AI-Driven Testing & Validation – Automated unit tests verify accuracy, transformations, and outputs, reducing the risk of errors.
- Deployment & Optimization – Engineers validate and fine-tune auto-generated Snowflake pipelines before go-live, ensuring a seamless transition.
Why It Matters for Snowflake Sellers & Clients:
- Migrate SAS 5x faster with AI-driven automation.
- Reduce migration costs by eliminating manual code conversion.
- Improve accuracy with AI-led validation, ensuring data integrity.
- Eliminate SAS licensing costs and fully transition to Snowflake.
Migrating from SAS to Snowflake with a Data Partner You Can Trust
Successfully migrating from SAS and SAS Enterprise Guide to Snowflake requires a structured approach that includes assessment, strategy development, data extraction, transformation, testing, and optimization.
By leveraging Snowflake’s cloud-native architecture, organizations can achieve better performance, cost savings, and enhanced analytical capabilities. As a consultant, guiding businesses through this complex transition ensures that they maximize the benefits of Snowflake while minimizing disruption.
Ready to embark on your SAS to Snowflake modernization journey? Talk to one of our experts today.
