Develop a custom, agile data warehousing and business intelligence architecture Empower your users and drive better decision making across your enterprise with detailed instructions and best practices from an expert developer and trainer. The Data Warehouse Mentor: Practical Data Warehouse and Business Intelligence Insights shows how to plan, design, construct, and administer an integrated end-to-end DW/BI solution. Learn how to choose appropriate components, build an enterprise data model, configure data marts and data warehouses, establish data flow, and mitigate risk. Change management, data governance, and security are also covered in this comprehensive guide. Understand the components of BI and data warehouse systems Establish project goals and implement an effective deployment plan Build accurate logical and physical enterprise data models Gain insight into your company's transactions with data mining Input, cleanse, and normalize data using ETL (Extract, Transform, and Load) techniques Use structured input files to define data requirements Employ top-down, bottom-up, and hybrid design methodologies Handle security and optimize performance using data governance tools Robert Laberge is the founder of several Internet ventures and a principle consultant for the IBM Industry Models and Assets Lab, which has a focus on data warehousing and business intelligence solutions.

Develop a custom, agile data warehousing and business intelligence architecture Empower your users and drive better decision making across your enterprise with detailed instructions and best practices from an expert developer and trainer. The Data Warehouse Mentor: Practical Data Warehouse and Business Intelligence Insights shows how to plan, design, construct, and administer an integrated end-to-end DW/BI solution. Learn how to choose appropriate components, build an enterprise data model, configure data marts and data warehouses, establish data flow, and mitigate risk. Change management, data governance, and security are also covered in this comprehensive guide. Understand the components of BI and data warehouse systems Establish project goals and implement an effective deployment plan Build accurate logical and physical enterprise data models Gain insight into your company's transactions with data mining Input, cleanse, and normalize data using ETL (Extract, Transform, and Load) techniques Use structured input files to define data requirements Employ top-down, bottom-up, and hybrid design methodologies Handle security and optimize performance using data governance tools Robert Laberge is the founder of several Internet ventures and a principle consultant for the IBM Industry Models and Assets Lab, which has a focus on data warehousing and business intelligence solutions.

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.

"Binding: PB"--

In the era of "Big Data," effective information management has become crucial to the success of virtually all organizations. Unfortunately, few IT practitioners know today's best practices for successfully managing enterprise information resources. Patterns for Information Management offers the solution: a multi-disciplinary patterns-based approach that reflects where information comes from, how it is distributed, protected, governed, monitored -- and, ultimately, utilized.

Written in lucid language, this valuable textbook brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The textbook is written to cater to the needs of undergraduate students of computer science, engineering and information technology for a course on data mining and data warehousing. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models and NoSQL are discussed in detail. Pedagogical features including unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding.

Relational Database Design and Implementation: Clearly Explained, Fourth Edition, provides the conceptual and practical information necessary to develop a database design and management scheme that ensures data accuracy and user satisfaction while optimizing performance. Database systems underlie the large majority of business information systems. Most of those in use today are based on the relational data model, a way of representing data and data relationships using only two-dimensional tables. This book covers relational database theory as well as providing a solid introduction to SQL, the international standard for the relational database data manipulation language. The book begins by reviewing basic concepts of databases and database design, then turns to creating, populating, and retrieving data using SQL. Topics such as the relational data model, normalization, data entities, and Codd's Rules (and why they are important) are covered clearly and concisely. In addition, the book looks at the impact of big data on relational databases and the option of using NoSQL databases for that purpose. - Features updated and expanded coverage of SQL and new material on big data, cloud computing, and object-relational databases - Presents design approaches that ensure data accuracy and consistency and help boost performance - Includes three case studies, each illustrating a different database design challenge - Reviews the basic concepts of databases and database design, then turns to creating, populating, and retrieving data using SQL