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thiago

Join this webinar to explore how to get from loading and exploring data to building interactive dashboards with compelling visualizations. You will learn how: How to explore and prepare data for analysis in SAS Visual Analytics. Techniques for creating effective charts, reports and dashboards. Ways to add interactivity and share insights with stakeholders. Tips for prompting SAS® Viya® Copilot in SAS Visual Analytics. Register Now

thiago

Join this webinar to learn how you can create a truly custom graph step by step. You will learn how to: Start with a plot created with SG procedures and use TMPOUT to help generate GTL code. Combine different graphs into one graph using GTL. Understand what is meant by “gutter” in a multi-cell graph. Register Now

thiago

Join this webinar to learn how to easily access the Microsoft tools you use most within your SAS Viya environment You will learn to: Make informed business decisions by enhancing Microsoft documents with analytics insights. Work with data in Microsoft applications and upload Excel files to SAS Viya. Create SAS programs and insert results from programs and SAS jobs into Excel. Register Now

thiago

Join this webinar to explore practical techniques that you can use immediately. You will learn about: Essential DATA step functions every SAS programmer should know. Useful character, date and conditional functions for everyday coding. Function-driven techniques that simplify macro programming. Register Now

doesper

My experience with PROC SQL pass-through to Amazon Redshift (and this probably applies to many other databases) is that character variables are often created much larger than they need to be. I'd like to see an option added to PROC SQL that will automatically scan the data returned from the external database to determine the maximum length needed for each character variable and apply that length when building the output SAS dataset. Granted this will increase processing time which is why it should be optional, but this would be an improvement over having to post-process the dataset to shorten the character variables as we do now. Perhaps something like this? PROC SQL SHRINK;