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Data analysis for database design is a subject of great practical value to systems analysts and designers. This classic text has been updated to include chapters on distributed database systems, query optimisation and object-orientation.The SQL content now includes features of SQL92 and SQL 99.

With new databases coming online all the time and the general expansion of the information age, it is increasingly important to ensure that the analysis and model of a database design is accurate and robust. This is an ideal book for helping you to ensure that your database is well designed and therefore user friendly.



Increased material on SQL including the latest developments
Practical approach to explaining techniques and concepts
Contains many questions and answer pointers

Data Analysis in the Cloud introduces and discusses models, methods, techniques, and systems to analyze the large number of digital data sources available on the Internet using the computing and storage facilities of the cloud.

Coverage includes scalable data mining and knowledge discovery techniques together with cloud computing concepts, models, and systems. Specific sections focus on map-reduce and NoSQL models. The book also includes techniques for conducting high-performance distributed analysis of large data on clouds. Finally, the book examines research trends such as Big Data pervasive computing, data-intensive exascale computing, and massive social network analysis.

• Introduces data analysis techniques and cloud computing concepts
• Describes cloud-based models and systems for Big Data analytics
• Provides examples of the state-of-the-art in cloud data analysis
• Explains how to develop large-scale data mining applications on clouds
• Outlines the main research trends in the area of scalable Big Data analysis

Data Architecture: From Zen to Reality provides an introduction to the basic principles of data architecture, focusing on the development and implementation of enterprise-level information architecture.
The book is organized into five sections. Section 1 discusses the data architecture and data design principles needed to reintroduce architecture into the business and software development process. Section 2 deals with the business and organizational issues of modern businesses and how structured and architected approaches may help them remain flexible and responsive. Section 3 focuses on the software-application development process, defining tools, techniques, and methods that ensure repeatable results. Section 4 discusses the artifact results of the process and the ways to make output products more efficient in real-world environments. Section 5 covers specialty databases and the various issues that arise in the business data processing environment, including data warehousing, objects and objects relational databases, and distributed databases.
This book was written for people in business management involved with corporate data issues and information technology decisions. It is also a reference tool for those in a higher-level education process involved in data or information technology management.

• Presents fundamental concepts of enterprise architecture with definitions and real-world applications and scenarios
• Teaches data managers and planners about the challenges of building a data architecture roadmap, structuring the right team, and building a long term set of solutions
• Includes the detail needed to illustrate how the fundamental principles are used in current business practice

Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can’t be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist.

Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You’ll be able to:

• Turn textual information into a form that can be analyzed by standard tools.
• Make the connection between analytics and Big Data
• Understand how Big Data fits within an existing systems environment
• Conduct analytics on repetitive and non-repetitive data

• Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it
• Shows how to turn textual information into a form that can be analyzed by standard tools.
• Explains how Big Data fits within an existing systems environment
• Presents new opportunities that are afforded by the advent of Big Data
• Demystifies the murky waters of repetitive and non-repetitive data in Big Data