SAS Best Practices

 💡 SAS Best Practices

"In my experience working with SAS for data analysis and reporting, I always follow certain best practices to ensure code quality, performance, and maintainability. 

Whether you're using Base SAS, SAS Viya, or Enterprise Guide — these principles help ensure reliable, efficient, and scalable solutions."

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🧠 Core SAS Best Practices

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1️⃣ Efficient Data Handling

• Minimize I/O: Use KEEP= and DROP= statements to only load the necessary variables.

• Use WHERE over IF for filtering data — WHERE is processed during data reading, making it much faster.

• Example:

data subset;
    set big_table(keep=customer_id balance transaction_date);
    where transaction_date >= '01JAN2024'd;
run;

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2️⃣ Modular Programming

• Break logic into reusable macro programs.

• Reduces code duplication and simplifies maintenance.

• Example:

%macro clean_data(dataset);
    data &dataset._clean;
        set &dataset.;
        /* cleaning rules */
    run;
%mend;

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3️⃣ Consistent Naming Conventions

• Use meaningful names for datasets, variables, and macros.

• Avoid cryptic or temporary names in production code.

• E.g., customer_balance_history is better than cbh1.

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4️⃣ Code Documentation

• Always comment code blocks for clarity.

• Helps future developers (or your future self!) understand the logic.

• Example:

/* Removing inactive customers before the next analysis step */
where status = 'Active';

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5️⃣ Performance Optimization

• Use INDEX or SORTEDBY for large datasets.

• Limit unnecessary variables with KEEP=.

• Use SAS Views instead of creating temporary datasets when possible.

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6️⃣ Error Handling

• Use OPTIONS for better debugging:

options mprint mlogic symbolgen;

• Implement IF _ERROR_ or %PUT for custom error alerts.

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7️⃣ Testing & Validation

• Use small sample datasets to validate your logic before scaling.

• Validate outputs against business rules or with peer code reviews.

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8️⃣ Security & Data Privacy

• Mask sensitive data during development.

• Use role-based access control in SAS Viya or Metadata Server.

• Avoid writing plain-text credentials in your code; use external files or encrypted passwords.

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9️⃣ Version Control & Collaboration

• Use Git, or at least maintain proper versioning in file names (if Git isn’t available).

• For teams, document changes in a CHANGELOG or commit messages.

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🔥 SAS Viya-Specific Practices

• Write modular, reusable CAS (Cloud Analytic Services) code.

• Make sure to drop unnecessary CAS tables to free memory (caslib management).

• Use parallel processing when possible to maximize distributed computing.

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💡 Final Tip for Interviews:

"Following these practices has helped me write efficient, maintainable SAS code and build scalable data pipelines — whether it’s fraud detection reports, customer analytics, or regulatory data submissions."