What are the pros and cons of using proc sql for complex joins and aggregations in SAS?

PROC SQL in SAS is a robust tool that leverages SQL for efficient data querying and manipulation. It simplifies database management, allowing users to select, filter, sort, group, summarize, and join data directly in SAS. For example, you can filter data using the 'WHERE' clause and join tables with 'INNER JOIN'. PROC SQL also enables access to external data sources like databases and spreadsheets. To optimize performance, focus on selecting only necessary columns and using indexes effectively. Adopting these best practices enhances your data analysis capabilities.

Also, when there is a need to merge many-to-many records from datasets , PROC SQL is a powerful tool. For example, if we have multiple adverse events records in adverse events datasets and multiple medication records in concomitant medication datasets, and we need to see which medications were taken for the adverse events, PROC SQL can compare the dates from adverse events and concomitant medication datasets and merge records accordingly.

Please correct me if I am wrong, I find proc means/summary much easier to use than proc sql. It gets little complicated to generate ODS table outputs for proc means or any summary statistics in sql.

Using PROC SQL can greatly improve efficiency and performance with large datasets. It optimizes query execution by utilizing indexes, views, and parallel processing, which reduces input/output operations. For example, analyzing a large sales dataset becomes faster with PROC SQL. Additionally, the pass-through facility allows direct SQL statements to external databases, minimizing data transfer. The implicit pass-through feature automatically converts SAS dataset references to SQL tables, ensuring smooth integration with external databases. Implementing these practices can maximize your data analysis capabilities.

Nested PROC SQL code can get bigger and harder to understand, eventually taking more time to debug the code when things go wrong. Retaining variable value probably is not easy to do in PROC SQL.

While PROC SQL offers powerful capabilities, it can be less consistent than data steps for certain tasks. For instance, when sorting data, the "ORDER BY" clause may alter the order of observations within groups unexpectedly. To maintain variable values across observations, you’d rely on the MONOTONIC function, which can be unreliable and lacks SAS support. Additionally, PROC SQL may not preserve the attributes of your input dataset—like labels, formats, lengths, or indexes—in the output. To ensure data integrity, consider using data steps for sorting and preserving attributes. Always validate your results post-query. For complex operations, a hybrid approach - leveraging both PROC SQL and data steps - can yield optimal results.

With its basic SAS engine, PROC SQL does not incorporate common SQL features that regular SQL programmers may be familiar with. For example, PROC SQL does not allow useful operations such as window functions.