Migrate Legacy Code to PySpark
The SAS2PY platform migrates legacy code into PySpark. Supporting a wide range of inputs like SAS (Base, DI Studio, EG/EM, Viya), Snowflake, SQL (Oracle Big Query, Teradata, DB2, Netezza), and ETL tools like IBM DataStage and more...

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Automate your Code Migration

Convert your legacy scripts, macros, data steps, and SQL queries into PySpark. Migrate 100,000 lines of code in 10 minutes!

SAS2PY Platform

  • ETL Workflows to Native Processes
  • Code Optimization Engine
  • Data Lineage Tracking
  • AI: Validates & Reconciles
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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).
PySpark SQL Notebooks: Pushes converted code directly into PySpark Workspaces for seamless collaboration.

PySpark Workflows: DB invokes SAS2PY API to convert code in S3 or elsewhere.
STEP 2: Code Conversion
Syntax Conversion: Parse SAS, SQL, or ETL workflows and convert them into PySpark SQL or PySpark-compatible scripts.
Schema Translation: Adapt legacy database schemas to PySpark’ Delta Lake architecture, ensuring ACID compliance and optimal performance.
ETL Modernization: Replace legacy ETL workflows (e.g., Informatica) with Delta Lake-native pipelines for scalable, modern data processing.
Push Models: Export converted workflows directly to PySpark Workspaces for immediate use.

Pull Models: Use PySpark to invoke SAS2PY APIs to process and migrate code from storage solutions like S3.
Code Optimization: Refactor inefficient or outdated logic to maximize PySpark’ performance capabilities, leveraging the Lakehouse platform for scalability and speed.













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STEP 3: GenAI Validation & Testing

Leverage cutting-edge Generative AI to analyze, optimize, and validate the converted legacy code, ensuring a fully optimized solution within PySpark.
Data Validation: Automate checks to confirm parity between legacy outputs and PySpark results, ensuring the integrity of data migration.

Regression Testing: Compare outputs of migrated workflows with legacy systems to maintain consistency across operations.

Error Handling: Identify and resolve syntax errors, data inconsistencies, or logic gaps during the testing phase to ensure production readiness.
Data Matching
Automated Schema Mapping: Automatically maps source schemas (e.g., SAS, Oracle, Teradata) to Snowflake.
Data Type Validation: Ensures that column types (e.g., numeric, string, date) in the legacy system are correctly translated into Snowflake-native formats.
Metadata Comparison: Compares metadata (e.g., table structures, indexes) between legacy and Snowflake 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 PySpark

Businesses transitioning from static, on-premise systems to scalable cloud solutions can revolutionize their operations with PySpark.

 Unified Data Platform: Combine structured, semi-structured, and unstructured data into a single, unified Lakehouse for analytics and machine learning.
Scalable Performance: Seamlessly handle massive data volumes with PySpark’ elastic infrastructure.
Delta Lake for Reliability: Ensure data consistency, reliability, and ACID compliance, making it ideal for real-time and batch processing.
Global Accessibility: Access and analyze your data from anywhere, enabling distributed teams to collaborate effortlessly.

Real-Time Collaboration: Work collaboratively using PySpark notebooks to share insights, develop models, and accelerate innovation.

Frequently Asked Questions

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

SAS2PY automates the conversion of legacy systems like SAS, SQL, and ETL workflows into PySpark-native formats. It delivers faster, more accurate migrations at significantly lower costs.

SAS2PY accelerates migration timelines by up to 10X, reducing the process from months to weeks. For example, it can convert 100,000 lines of code in just 10 minutes.

Absolutely! SAS2PY 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! SAS2PY eliminates costly legacy software licensing fees and reduces migration expenses by up to 75%.

SAS2PY 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. SAS2PY automates the process, delivering faster, more accurate results while reducing costs.

SAS2PY redirects all data operations to Delta tables, offering enhanced performance and consistency with ACID compliance.

Absolutely! SAS2PY seamlessly integrates into your current workflows and Databrick environment.

SAS2PY automates ETL migrations to PySpark by converting workflows into PySpark pipelines optimized for Delta Lake. It supports both push (direct deployment to PySpark) and pull (API-driven conversion from storage like S3) models. Additionally, SAS2PY ensures accuracy through automated validation and performance optimization tailored for PySpark' scalability.

Yes! Your data never leaves your network.

Yes, SAS2PY converts legacy machine learning models into MLFlow-compatible formats for seamless integration into PySpark. It supports model tracking, experimentation, and deployment, ensuring end-to-end functionality in PySpark' Lakehouse platform. This allows businesses to modernize and scale their AI/ML workflows efficiently.

SAS2PY uses rule-based reconciliation and anomaly detection to resolve mismatches automatically, ensuring a smooth transition.

SAS2PY offers unparalleled automation, speed, and accuracy, transforming legacy systems into PySpark-native formats up to 10x faster. It provides advanced features like Delta Lake integration, PySpark optimization, and MLFlow instrumentation, ensuring a comprehensive migration process. With SAS2PY, businesses save up to 70% in costs while maintaining data integrity and scalability.

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Google Cloud + PySpark

Providing a flexible, scalable, and secure way to store, analyze, and share large datasets across different cloud platforms, while also accessing Google Cloud's powerful analytics and machine learning tools to extract deeper insights from your data.