Fully updated and expanded from the previous edition, A Practical Guide to Database Design, Second Edition is intended for those involved in the design or development of a database system or application. It begins by illustrating how to develop a Third Normal Form data model where data is placed “where it belongs”. The reader is taken step-by-step through the Normalization process, first using a simple then a more complex set of data requirements. Next, usage analysis for each Logical Data Model is reviewed and a Physical Data Model is produced that will satisfy user performance requirements. Finally, each Physical Data Model is used as input to create databases using both Microsoft Access and SQL Server. The book next shows how to use an industry-leading data modeling tool to define and manage logical and physical data models, and how to create Data Definition Language statements to create or update a database running in SQL Server, Oracle, or other type of DBMS. One chapter is devoted to illustrating how Microsoft Access can be used to create user interfaces to review and update underlying tables in that database as well as tables residing in SQL Server or Oracle. For users involved with Cyber activity or support, one chapter illustrates how to extract records of interest from a log file using PERL, then shows how to load these extracted records into one or more SQL Server “tracking” tables adding status flags for analysts to use when reviewing activity of interest. These status flags are used to flag/mark collected records as “Reviewed”, “Pending” (currently being analyzed) and “Resolved”. The last chapter then shows how to build a web-based GUI using PHP to query these tracking tables and allow an analyst to review new activity, flag items that need to be investigated, and finally flag items that have been investigated and resolved. Note that the book has complete code/scripts for both PERL and the PHP GUI.

Typically, analysis, development, and database teams work for different business units, and use different design notations. With UML and the Rational Unified Process (RUP), however, they can unify their efforts -- eliminating time-consuming, error-prone translations, and accelerating software to market. In this book, two data modeling specialists from Rational Software Corporation show exactly how to model data with UML and RUP, presenting proven processes and start-to-finish case studies. The book utilizes a running case study to bring together the entire process of data modeling with UML. Each chapter dissects a different stage of the data modeling process, from requirements through implementation. For each stage, the authors cover workflow and participants' roles, key concepts, proven approach, practical design techniques, and more. Along the way, the authors demonstrate how integrating data modeling into a unified software design process not only saves time and money, but gives all team members a far clearer understanding of the impact of potential changes. The book includes a detailed glossary, as well as appendices that present essential Use Case Models and descriptions. For all software team members: managers, team leaders, systems and data analysts, architects, developers, database designers, and others involved in building database applications for the enterprise.

A Practical Guide to SysML, Third Edition, fully updated for SysML version 1.4, provides a comprehensive and practical guide for modeling systems with SysML. With their unique perspective as leading contributors to the language, Friedenthal, Moore, and Steiner provide a full description of the language along with a quick reference guide and practical examples to help you use SysML. The book begins with guidance on the most commonly used features to help you get started quickly. Part 1 explains the benefits of a model-based approach, providing an overview of the language and how to apply SysML to model systems. Part 2 includes a comprehensive description of SysML that provides a detailed understanding that can serve as a foundation for modeling with SysML, and as a reference for practitioners. Part 3 includes methods for applying model-based systems engineering using SysML to specify and design systems, and how these methods can help manage complexity. Part 4 deals with topics related to transitioning MBSE practice into your organization, including integration of the system model with other engineering models, and strategies for adoption of MBSE. - Learn how and why to deploy MBSE in your organization with an introduction to systems and model-based systems engineering - Use SysML to describe systems with this general overview and a detailed description of the Systems Modeling Language - Review practical examples of MBSE methodologies to understand their application to specifying and designing a system - Includes comprehensive modeling notation tables as an appendix that can be used as a standalone reference

Giving comprehensive, soup-to-nuts coverage of database administration, this guide is written from a platform-independent viewpoint, emphasizing best practices.

"Highlights of this book include: the MDA framework, including the Platform Independent Model (PIM) and Platform Special Model (PSM); OMG standards and the use of UML; MDA and Agile, Extreme Programming, and Rational Unified Process (RUP) development; how to apply MDA, including PIM-to-PSM and PSM-to-code transformations for Relational, Enterprise JavaBean (EJB), and Web models; transformations, including controlling and tuning, traceability, incremental consistency, and their implications; metamodeling; and relationships between different standards, including Meta Object Facility (MOF), UML, and Object Constraint Language (OCL)."--Jacket.

Build faster and efficient real-world applications on the cloud with a fitting database model that's perfect for your needs Key Features Familiarize yourself with business and technical considerations involved in modeling the right database Take your data to applications, analytics, and AI with real-world examples Learn how to code, build, and deploy end-to-end solutions with expert advice Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the age of lightning-speed delivery, customers want everything developed, built, and delivered at high speed and at scale. Knowledge, design, and choice of database is critical in that journey, but there is no one-size-fits-all solution. This book serves as a comprehensive and practical guide for data professionals who want to design and model their databases efficiently. The book begins by taking you through business, technical, and design considerations for databases. Next, it takes you on an immersive structured database deep dive for both transactional and analytical real-world use cases using Cloud SQL, Spanner, and BigQuery. As you progress, you’ll explore semi-structured and unstructured database considerations with practical applications using Firestore, cloud storage, and more. You’ll also find insights into operational considerations for databases and the database design journey for taking your data to AI with Vertex AI APIs and generative AI examples. By the end of this book, you will be well-versed in designing and modeling data and databases for your applications using Google Cloud.What you will learn Understand different use cases and real-world applications of data in the cloud Work with document and indexed NoSQL databases Get to grips with modeling considerations for analytics, AI, and ML Use real-world examples to learn about ETL services Design structured, semi-structured, and unstructured data for your applications and analytics Improve observability, performance, security, scalability, latency SLAs, SLIs, and SLOs Who this book is for This book is for database developers, data engineers, and architects looking to design, model, and build database applications on the cloud with an extended focus on operational consideration and taking their data to AI. Data scientists, as well ML and AI engineers who want to use Google Cloud services in the data to AI journey will also find plenty of useful information in this book. It will also be useful to data analysts and BI developers who want to use SQL impactfully to generate ML and generative AI insights from their data.

This textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge. The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics). This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.

The management of clinical data, from its collection during a trial to its extraction for analysis, has become a critical element in the steps to prepare a regulatory submission and to obtain approval to market a treatment. Groundbreaking on its initial publication nearly fourteen years ago, and evolving with the field in each iteration since then, the third edition of Practical Guide to Clinical Data Management includes important updates to all chapters to reflect the current industry approach to using electronic data capture (EDC) for most studies. See what’s new in the Third Edition: A chapter on the clinical trial process that explains the high level flow of a clinical trial from creation of the protocol through the study lock and provides the context for the clinical data management activities that follow Reorganized content reflects an industry trend that divides training and standard operating procedures for clinical data management into the categories of study startup, study conduct, and study closeout Coverage of current industry and Food and Drug Administration (FDA) approaches and concerns The book provides a comprehensive overview of the tasks involved in clinical data management and the computer systems used to perform those tasks. It also details the context of regulations that guide how those systems are used and how those regulations are applied to their installation and maintenance. Keeping the coverage practical rather than academic, the author hones in on the most critical information that impacts clinical trial conduct, providing a full end-to-end overview or introduction for clinical data managers.