Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing (Agile Software Development Series)

Agile methods, the use of a data warehouse (DW), Business Intelligence (BI), or analysis for the project much more innovation, value and quality can bring. However, traditional methods of Agile carefully DW / BI projects must conform to the unique properties. Agile Analytics, a pioneer Ken Collier shows how agile.

Collier, the oldest operating systems and a variety of commercial law and private offers special joint platform independent solutions for the integration of an agile infrastructure. Using examples of his work, skills development, and how different analyzes to support the rapid growth of large volumes of data and shows how to manage teams. Projects "back end" data management "front end" business analysis, contains, or both Collier provides optimal techniques.

Ben equipment delivery, community agile DW / BI project, the basic practices that determine the path of introducing agile techniques, focusing on project management and coordination.  You can cooperate for the success of the section

The second part of superior design, test DW-oriented development, version control, and automation projects, including the quality of the production value of the company to ensure a continuous delivery provides the technical methods

Collier, an IT decision makers, professional data storage, database administrator, specializing in business intelligence, or if you are a developer of database, now provides proven solutions can be applied. With your help, reduce project risk, improve business compliance, achieve better results and you can have fun along the way.

Say

"This book explains why and how to do Agile Analytics did a great job in the real world. Implementation of Ken and refinement of this approach are many lessons learned. Business Intelligence is an area certainly benefit from this type of discipline."

Zinkgraf Dale, Business Intelligence Architect Sr.

"The evidence of an important aspect of Agile Analytics, agile project management, design applications, the scope of the evolution of self-organizing teams, products and technical control of the management books width and automated management and continuous integration build. They plan an analysis, even if you a flavor of a large data-driven processing issues and products useful to society and will be beyond this general analysis of Ken. "

• Jim Highsmith, Senior Consultant, Thought Works, Inc. and author of Agile Project Management

"Agile methods of software development have changed and now it's time to convert the scan. Analytics Agile methods of analysis to the next delivery of the project provide the necessary knowledge to perform the conversion."

• Pramod Sadalage, author of Refactoring Databases: Design of scalable database

"This book is for the next ten years brings together the key strategies for successful business projects intelligence analysis. Ken Collier raised the bar for professional analysis is the challenge?"

Scott Ambler, IBM Rational Methodist president and founder of Lean Agile, Agile Data

"The teams that will help you deliver high-quality presentations in a scan, high value systems, and development of business intelligence software faster and more cost effective than traditional ways of working."

Ralph Hughes, author of Agile Data Warehousing

About the Author

Ken Collier worked with agile methods since 2003, and data warehousing, business intelligence and analysis to create a style with vivid that Google Analytics is an integrated agile methods pioneered. Several agile project teams, DW / BI project manager, technical director and continues to develop these ideas. Collier teams often DW / BI Analytics Agile in trains and Hedw speakers on the subject (Higher Education Data Warehouse), 2011, and some TDWI (The Data Warehousing Institute) was the global conferences. He is founder and KWC Technologies, Inc. President and Business Intelligence applications and consulting areas Cutter Consortium Senior Agile development.

Agile Data Warehousing: Delivering World-Class Business Intelligence Systems Using Scrum and XP

As a consulting firm, fast data storage is valuable information on the subject, especially the very small number of sheep and a book to gather information. This volume of development teams can develop quickly and effectively to create business intelligence applications, Ceregenics, Inc. is distilled from ten years to research. Since the creation and implementation of data warehouses can cost millions, agile data storage registers financial professionals, but also shortens the time and better quality application only. Agile movement in software engineering "to maximize the job done," produced for interesting news. 

In general, however, the movement of data management, agile systems of data collection for data storage provides only applications. With focused on high-level approaches, Ceregenics the bride and groom, to store data in a single method adapts generic Scrum and XP, in good faith, development and results of such a watch SEI maturity capacity Model. Featured such as those for the six-step plan to pass formal evaluations methodological Agile project development team to become a world-class applications professionals, data warehouse, step by step guide launched. 

In Furthermore, the introduction of a method of resistance and radical skepticism and even some systems project management information based on traditional techniques with the Department for a Fortune 500 company, may explain the repairs to avoid chaos.

About the Author

Ralph Hughes, MA, chief architect Ceregenics systems, Certified Scrum Master (CSM) and the PMI Project Management Professional (PMP) since 1982 and has built data warehouses. Fortune agile projects, aviation, telecommunications and pharmaceutical companies, 500 run, is fluent in French international literature. O in Denver, Colorado skiing and fly fishing among the living, and projects.

Building the Data Warehouse

Description of Building the Data Warehouse

• The data storage industry, has launched a new edition of the classic bestseller, new approaches and technologies, many of which were developed by INMO
• In addition to explaining the basics of data storage systems, the book as a data warehouse to store unstructured data processing and data across multiple storage media covers issues such as methods for
• The advantages and disadvantages of multi-dimensional design projects just relational data warehouses and how to measure return on investment analysis
• Includes improved monitoring and test data includes son
• Although the book contains valuable content worth 100 extra pages, the price is actually reduced to $ 65 $ 55 

Say

"... A thoughtful and clear text? I consider the establishment of a data warehouse, data management, study or recommend it to everyone ..." (Information Management, October 9, 2002)

"Overall, this text is clear and well thought out. I consider the establishment of a data warehouse, data management, and study or recommend it to everyone? "(General Information, December 2002) - This text refers to the issue of consolidation.

Through Back cover

The basic concepts and methods, more comprehensive data storage

Data warehouses collect data from a large amount of business organizations, analyze, store and deliver much-needed strategy. And both online and brick and mortar companies to expand their activity, the area of?? Data storage has become increasingly important. Since the first publication in 1990, WH INMO data warehouse data storage data storage industry bible book was launched and remains the ultimate building entrance. This new version has been encouraged by him INMO includes many of the latest developments in this technology.

Data warehouse design, migration of data storage technologies can be applied to a variety of strategies and provides an overview of methods used for data storage systems, an overview of all the building blocks of management loading, indexing and data. To update in more detail below, this guide offers the latest developments in data storage.

New sections:

• The basic methods for handling unstructured data in a data warehouse
• The data storage across multiple storage media
• Examine the pros and cons of relational vs. dimensional design
• Measure the profitability of investment projects data warehouse
• And test data, including surveillance issues advances in research

Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema

Agile design data warehouses (DW / BI) requirements of the data warehouse / business intelligence, and the most direct way to capture the dimensions of a step by step guide to transform them into high-performance models: model storming by stakeholders BI (brainstorming + data modeling). This data warehouses, BI, stakeholders and development teams all DW / BI to improve communication between designers? BEAM, modeling, describes a three-dimensional approach to agile. BEAM? Entity-relationship model-based tools and remote keyboard and merchandise and interact with peers, developers, and BI developers and incentive DW provides techniques. The result is that everyone thinks that the beginning of the dimensions! Developers to implement effective solutions to understand three-dimensional modeling. Business, the store owner that they create, how to use the data can not imagine that feeling and to answer your questions. Topics are:

• Events Three-dimensional modeling the Agile Business Analysis and Modeling (BEAM?)
• Model storming: data modeling faster, more inclusive, more effective and more fun, frankly, much more!
• Tales 7Ws using dimensional data (what, what, how, why and how)
• Use data to explain the history and details of topics such as modeling raven feet, abstraction,
• Explore and develop iterative storyboards to keep the data size according to plan
• Visual Modeling: The process of complex models of measurement when drawing tables, graphs and networks - not just
• The agile design documents: BEAM star, stripe? Improvement plans sizes
• Difficult DW / BI and performance proven design patterns, dimensions and ease of troubleshooting

About the Author

A data storage designer and educator Lawrence Corr. Customers to review and simplify their designs and modeling techniques of visual data, providers of data storage to help inform the Council of Decision One, as a manager. He regularly teaches courses and thousands of students worldwide dimensional modeling for the dimensions of agile DW / BI taught.

Jim Stagnitto a data warehouse architect and healthcare, financial services, specializing in data management and information services sectors. He is the founder of data warehousing and data mining consulting firm Llumino.

Building a Data Warehouse: With Examples in SQL Server

Building the Data Warehouse: With Examples in SQL Server describes how to create a data warehouse completely from scratch and shows practical examples on how to do it. Author Vincent Rainardi solutions and advice along with developers, the first data warehouse project, discusses some practical problems are likely to encounter. SQL Server 2005 or later used in the examples and the system user relational database (RDBMS), SQL Server, the version will not be a problem as long.

The book is organized as follows. This book (Part 1 6) at the beginning, for example, architecture definition, design patterns data to understand the methodology of requirements gathering, how to build a data warehouse and creating databases data. Then, in Section 7-10, for example, how the source to fill the system with the data store for data quality and use of metadata to facilitate the loading of data stores. After completing the data warehouse, in Chapters 11 to 15, reports and multidimensional databases and how the data warehouse, business intelligence, relationship management, customer data transmission, and explore ways to provide users with data for others. Sections 16 and 17 to wrap the book: After creating a data warehouse before being released to production, you should test it thoroughly. Production after the application, you must understand how to manage the operation of data storage.

What you will learn

• To create a data warehouse, a thorough understanding of what you need
• Application code to create the data store in SQL Server
• Three-dimensional modeling, methods of data mining, overhead data storage, filling the size and the fact tables, data quality, architecture and data warehouse database design
• This kind of business intelligence reports, analysis and data storage 
• Practical applications such as customer relationship management,

For whom is this book?

There are three types of audience for the book. People who participate in the first data memory. In what they can be considered a guide for a field. The second about what it would take to create a data warehouse, I want to have a good idea those users of databases or administrators. The third hearing, the governance aspects of the task before them to make decisions about data storage, and use books to learn about these issues is essential.