Migrate Legacy Code to Azure
The PyFluent platform migrates legacy code into Snowflake-native formats. Supporting a wide range of inputs like SAS (Base, DI Studio, EG/EM, Viya), SQL (Oracle, Teradata, DB2, Netezza), and ETL tools (Informatica, IBM DataStage) and more...

Azure Benefits

  • Streamline Data Operations
  • Scale Effortlessly
  • Leverage AI/ML
  • Optimize Costs

AUTOMATED Migration

  • ETL Workflows to Native Processes
  • Code Optimization Engine
  • Data Lineage Tracking
  • GenAI/AskAI

PROVEN Results 🌟

  • Transform Massive Codebases
  • AI: Validates & Reconciles
  • 70% Lower Cost to Migration
  • 10X Faster Migrations


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STEP 1: Legacy Analysis

Automatically analyzes the legacy environment and identifies all legacy components, such as SAS Base, DI Studio, Informatica, SQL scripts, or database dependencies (e.g., Oracle, Teradata).
Native SQL Notebooks: Pushes converted code to DB workspaces.

Native Notebooks: DB invokes PyFluent API to convert code in S3 or elsewhere.
STEP 2: Code Conversion
Syntax Conversion: Parse SAS, SQL, or ETL workflows and convert them into SQL or Snowpark-compatible Python scripts.
Schema Translation: Adapt legacy database schemas to naitive multi-cluster, shared data architecture.
ETL Modernization: Replace legacy ETL workflows (e.g., Informatica) with converted code.
Push/Pull Models: Utilize push models (direct export to Notebooks) or pull models (API-driven conversion and storage).
Code Optimization: Refactor inefficient or outdated logic to maximize code performance.


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STEP 3: GenAI Validation & Testing

Leverage cutting-edge generative AI to analyze, optimize, and validate the converted legacy code into an optimized solution. We identify any errors, check code integrity, and confirm that the migrated code performs as intended in the converted code environment.
Data Validation: Automate checks to confirm parity between legacy outputs and converted code results.

Regression Testing: Compare outputs of migrated workflows with legacy systems to ensure consistency.

Error Handling: Resolve syntax and data inconsistencies uncovered during testing.
Data Matching
Automated Schema Mapping: Automatically maps source schemas (e.g., SAS, Oracle, Teradata) to converted code.
Data Type Validation: Ensures that column types (e.g., numeric, string, date) in the legacy system are correctly translated into native converted code formats.
Metadata Comparison: Compares metadata (e.g., table structures, indexes) between legacy and converted systems to guarantee structural alignment.
Metrics Comparison: Validates key metrics such as counts, sums, averages, and other aggregates between source and target systems.
Partitioned Validation: Supports aggregate checks at the partition level (e.g., by date or region) to ensure consistency across subsets of data.


The Power of Azure

Businesses seeking to transition from static, on-premise systems to scalable cloud solutions can unlock new levels of efficiency and innovation with Azure.
Global Accessibility: Access data anywhere, anytime.
Cloud-First Architecture: Eliminate hardware costs.
Real-Time Collaboration: Share live data securely with teams.

Frequently Asked Questions

What is SAS2PY, and how does it simplify Azure migration?

PyFluent is an advanced code migration platform that automates the process of converting legacy systems like SAS, SQL, and ETL workflows into native formats. It ensures faster, accurate migrations at 75% lower costs, eliminating manual effort. See it in action during a demo!

PyFluent accelerates migration by up to 10X, reducing timelines from months to weeks. For example we will convert 100,000 lines of code in 10 minutes.

Absolutely! PyFluent is built for scalability, handling enterprise-scale migrations with millions of rows of data while maintaining accuracy.

Our platform uses advanced data matching techniques like row-by-row validation, hash comparisons, and aggregate checks to ensure 100% data consistency.
Want to see how it works? Book a demo!

Yes! PyFluent eliminates costly legacy software licensing fees and reduces migration expenses by up to 75%.

PyFluent automates validation at every stage—pre-migration, during migration, and post-migration—to guarantee data integrity.

Manual migration is slow, error-prone, and resource-intensive. PyFluent automates the process, delivering faster, more accurate results while reducing costs.

Yes! PyFluent converts legacy code into Snowflake-native workflows, including Snowpark for machine learning and advanced analytics.

Absolutely! PyFluent seamlessly integrates into your current workflows and Snowflake environments as an example.

PyFluent automates ETL migrations, transforming legacy workflows into Snowflake-native pipelines with minimal disruption.

Yes! Your data never leaves your network.

Yes! PyFluent converts SAS-based models into Snowpark-compatible formats, enabling machine learning directly in Snowflake.

PyFluent uses rule-based reconciliation and anomaly detection to resolve mismatches automatically, ensuring a smooth transition.

PyFluent offers unparalleled automation, accuracy, and speed, making it the most efficient tool for migrating to Snowflake. See why top companies trust SAS2PY—schedule a demo now!