Migrate Legacy Code to Pandas
The PyFluent platform migrates legacy analytics and data processing workflows into Pandas, enabling efficient **data manipulation, transformation, and analysis within a Python environment.
Pandas supports structured data operations such as **data cleaning, aggregation, filtering, statistical analysis, and feature engineering. It allows seamless integration with CSV, Excel, JSON, Parquet, SQL databases, and APIs**, making it ideal for handling structured datasets.
The migration process converts legacy SAS data structures, tables, and scripts** into Pandas-based workflows, enabling businesses to modernize their data exploration, reporting, and analytics with Python's rich ecosystem.


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Automate your Code Migration

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

PyFluent 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).
Pandas SQL Notebooks: Pushes converted code directly into Pandas Workspaces for seamless collaboration.

Pandas Workflows: 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 Pandas SQL or Pandas-compatible scripts.
Schema Translation: Adapt legacy database schemas to Pandas 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 Pandas Workspaces for immediate use.

Pull Models: Use Pandas to interact with PyFluent APIs for data transformation and migration, enabling processing of structured datasets from storage solutions like S3, databases, or local files.
Code Optimization: Refactor inefficient or outdated logic to enhance Pandas processing performance. For larger datasets requiring scalability and distributed execution, integration with Dask, Modin, or PySpark can be leveraged.













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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 Pandas 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 pandas.
Data Type Validation: Ensures that column types (e.g., numeric, string, date) in the legacy system are correctly translated into pandas-native formats.
Metadata Comparison: Compares metadata (e.g., table structures, indexes) between legacy and pandas 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 Pandas

Businesses transitioning from static, on-premise analytics systems to modern Python-based data processing can enhance efficiency and flexibility with Pandas.

 Versatile Data Processing: Handle structured and semi-structured data, enabling data cleaning, transformation, aggregation, and statistical analysis.
High-Performance In-Memory Computation: Perform efficient operations on large datasets using Pandas' vectorized computations and optimized built-in functions.
Seamless Data Integration: Easily read and write data in multiple formats, including CSV, Excel, JSON, Parquet, and SQL databases, enabling smooth interoperability with cloud storage and databases.
Enhanced Data Reliability: Ensure data integrity with built-in methods for handling missing values, duplicates, and complex transformations.
Scalability Strategies: While Pandas operates in-memory, large datasets can be managed using efficient chunking, optimized I/O operations, or integrations with Dask and Modin for improved scalability.
Cloud & Remote Accessibility: Run Pandas notebooks in Jupyter, Google Colab, or VS Code, enabling teams to analyze and process data in cloud-based environments.

Frequently Asked Questions

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

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

PyFluent 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! 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.

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

Absolutely! PyFluent seamlessly integrates into your current workflows and Pandas environment.

PyFluent automates ETL migrations to Pandas by converting workflows into Pandas-based data transformation pipelines. It supports push (direct deployment to Pandas-based processing environments) and pull (API-driven extraction from storage like S3 or databases) models. Additionally, PyFluent ensures accuracy through automated validation and performance optimization, while leveraging Pandas for structured data manipulation in local or in-memory environments.

Yes! Your data never leaves your network.

Yes, PyFluent converts legacy machine learning models into MLFlow-compatible formats for seamless integration into modern AI/ML pipelines. It supports model tracking, experimentation, and deployment, ensuring end-to-end functionality within MLFlow and Python-based machine learning frameworks. This allows businesses to modernize and scale their AI/ML workflows efficiently using libraries such as Scikit-learn, TensorFlow, PyTorch, and Pandas for data preprocessing.

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

PyFluent 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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Azure + PySpark

Enabling seamless data analysis, storage, and access across different cloud environments, all while maintaining a high level of security and performance.

AWS + Pandas

Pandas is an efficient in-memory data processing library but does not natively scale across distributed systems. However, businesses can handle large datasets by integrating Pandas with AWS services such as Amazon S3 for storage and AWS Lambda, EC2, or Fargate for computation. For large-scale data processing, frameworks like Dask, Modin, or PySpark provide Pandas-like functionality while leveraging AWS's elastic infrastructure.

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.