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From Terminals to Artificial Intelligence: Generative AI, Enterprise Data Engineering, and Adaptive Communication
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Publisher: Independently Published
Published: Sep 3 2026
Pages: 484
Weight: 2.45
Height: 0.97 Width: 8.50 Depth: 11.00
Language: English
- What does it take to transform a convincing prototype into a secure, reliable, and supportable production system?
- How can developers, data engineers, contractors, and managers trace an AI-generated answer back to authorized evidence?
- And ... when a system fails, where should troubleshooting begin?
From Terminals to Artificial Intelligence answers these questions through a practical combination of computer science, enterprise architecture, code, project experience, and lessons learned in the field.
The author is drawing on decades of projects and work orders while within Fortune500 companies, various levels of government departments, manufacturing operations, financial services, research environments, utilities, digital commerce, and consulting. The book connects technical theory with the realities of building, integrating, governing, troubleshooting, and supporting business-critical systems.
The journey begins with early C# and Microsoft Office Interop experiments in 2003, when modern CRM and ERP products were not always available to solve specialized operational problems. It follows the progression from custom applications, databases, reporting systems, and data-integration projects to cloud computing, data science, MLOps, retrieval-augmented generation, and enterprise AI.
Readers will explore Microsoft technologies including C#, .NET, Visual Studio, SQL Server, Azure, Microsoft 365, SharePoint, Dynamics 365, Microsoft Fabric, Power Platform, and GitHub Copilot. The book also covers IBM Db2 and AS/400 data foundations, Cognos ReportNet, IBM Watson, watsonx, and X-Force; Oracle database technologies; JavaScript, jQuery, Node.js, React, and Angular; Python, Jupyter, IDLE, Spyder, and VS Code; R and R Server; XML, JSON, APIs, CSV files; and Red Hat Enterprise Linux.
More than a survey of products, this is a guide to the methods that connect them. Readers learn how to:
Identify authoritative data sources and define reliable data contracts.
Build ingestion, validation, transformation, reporting, and retrieval pipelines.
Design databases, applications, reports, and AI-enabled workflows.
Configure development environments on Windows and Red Hat Enterprise Linux.
Evaluate retrieval quality, generated answers, security, bias, and traceability.
Implement access controls, monitoring, testing, deployment, and rollback.
Troubleshoot failures using reproducible evidence instead of guesswork.
Distinguish production experience from active research and development.
Code examples, diagrams, tables, walkthroughs, warnings, checklists, and practical exercises show how solutions are constructed, where they commonly fail, and how those failures can be investigated and corrected. First-person working methods allow readers to follow the reasoning of an experienced practitioner as each problem is framed, tested, documented, and carried toward production.
The book also explains how generative AI retrieves information, selects context, creates responses, adjusts language for different audiences, and must be evaluated before it can be trusted in enterprise settings.
Written for students, developers, data engineers, analysts, technical contractors, architects, managers, and technology leaders, From Terminals to Artificial Intelligence is both a textbook and a field guide. Its purpose is to help readers move beyond impressive demonstrations and build systems that are understandable, governed, reproducible, secure, supportable, and ready for responsible enterprise use.
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