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{ "item_title" : "SAS(R) Data-Driven Development", "item_author" : [" Troy Martin Hughes "], "item_description" : "SAS(R) Data-Driven Development is the only comprehensive text that demonstrates how to build dynamic SAS software driven by control data. Data-driven design enables SAS practitioners to create flexible, reusable software that adapts to diverse industries, organizations, and data sources because business rules, data mappings, formatting, report style, program logic, and other dynamic elements are maintained as external control data - not as static code. Data-driven design is the key to unlocking highly configurable, codeless software that developers, SAS administrators, end users, and other stakeholders can reuse and configure - without modifying one line of codeThis text introduces high-level design concepts, patterns, and principles, after which real-world scenarios demonstrate SAS development best practices:Part I. Data-Driven Design: Learn how to harness procedural abstraction, data abstraction, iteration abstraction, software modularity, data independence, and procedural communication, with concepts drawn from object-oriented programming (OOP), master data management (MDM), table-driven design, and business rules engines (BREs).Part II. Control Data: Understand the limitless data structures that can drive SAS software, including parameters, configuration files, control tables, decision tables, SAS data sets, and CSV, Excel, XML, and CSS files. Interoperability is modeled through control data that can be accessed by SAS and other applications.Throughout the text, requirements-based examples demonstrate data ingestion, data cleaning, data analysis, data mapping, data modeling, data governance, dynamic traffic light reporting, and other use cases. Examples contrast less desirable hardcoded design with abstract, data-driven design to illustrate the clear advantages of the latter.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/79/899/594/9798995947301_b.jpg", "price_data" : { "retail_price" : "29.99", "online_price" : "29.99", "our_price" : "29.99", "club_price" : "29.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
SAS(R) Data-Driven Development|Troy Martin Hughes

SAS(R) Data-Driven Development : From Abstract Design to Dynamic Functionality, Second Edition

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Overview

SAS(R) Data-Driven Development is the only comprehensive text that demonstrates how to build dynamic SAS software driven by control data. Data-driven design enables SAS practitioners to create flexible, reusable software that adapts to diverse industries, organizations, and data sources because business rules, data mappings, formatting, report style, program logic, and other dynamic elements are maintained as external control data - not as static code. Data-driven design is the key to unlocking highly configurable, "codeless" software that developers, SAS administrators, end users, and other stakeholders can reuse and configure - without modifying one line of code

This text introduces high-level design concepts, patterns, and principles, after which real-world scenarios demonstrate SAS development best practices:

  • Part I. Data-Driven Design: Learn how to harness procedural abstraction, data abstraction, iteration abstraction, software modularity, data independence, and procedural communication, with concepts drawn from object-oriented programming (OOP), master data management (MDM), table-driven design, and business rules engines (BREs).
  • Part II. Control Data: Understand the limitless data structures that can drive SAS software, including parameters, configuration files, control tables, decision tables, SAS data sets, and CSV, Excel, XML, and CSS files. Interoperability is modeled through control data that can be accessed by SAS and other applications.

Throughout the text, requirements-based examples demonstrate data ingestion, data cleaning, data analysis, data mapping, data modeling, data governance, dynamic "traffic light" reporting, and other use cases. Examples contrast less desirable hardcoded design with abstract, data-driven design to illustrate the clear advantages of the latter.

This item is Non-Returnable

Details

  • ISBN-13: 9798995947301
  • ISBN-10: 9798995947301
  • Publisher: Data Llama Analytics
  • Publish Date: May 2026
  • Dimensions: 9.25 x 7.5 x 1.25 inches
  • Shipping Weight: 2.61 pounds
  • Page Count: 570

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