This guide addresses the growing challenge of fragmented data science environments caused by reliance on multiple programming languages, particularly the coexistence of legacy SAS language systems and modern open-source tools such as Python, R, and SQL. It explains why rewriting or migrating existing SAS language applications is costly, risky, and often impractical. The guide introduces a hybrid development approach that allows organizations to run and extend SAS language programs without third-party compilers while seamlessly integrating open-source languages. It highlights how visual workflows, code-optional development, and shared environments enable collaboration between data scientists, engineers, statisticians, and business users. Governance, deployment, and cloud readiness are empha

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