This book is recommended for IT professionals, including those in consulting, working on systems that will deliver better knowledge management capability. This includes people in these positions: data architects, data analysts, SOA architects, metadata analysts, repository (metadata data warehouse) managers as well as vendors that have a metadata component as part of their systems or tools.
Best known as the “Father of Data Warehousing," Bill Inmon has become the most prolific and well-known author worldwide in the big data analysis, data warehousing and business intelligence arena. In addition to authoring more than 50 books and 650 articles, Bill has been a monthly columnist with the Business Intelligence Network, EIM Institute and Data Management Review. In 2007, Bill was named by Computerworld as one of the “Ten IT People Who Mattered in the Last 40 Years of the computer profession. Having 35 years of experience in database technology and data warehouse design, he is known globally for his seminars on developing data warehouses and information architectures. Bill has been a keynote speaker in demand for numerous computing associations, industry conferences and trade shows. Bill Inmon also has an extensive entrepreneurial background: He founded Pine Cone Systems, later named Ambeo in 1995, and founded, and took public, Prism Solutions in 1991. Bill consults with a large number of Fortune 1000 clients, and leading IT executives on Data Warehousing, Business Intelligence, and Database Management, offering data warehouse design and database management services, as well as producing methodologies and technologies that advance the enterprise architectures of large and small organizations world-wide. He has worked for American Management Systems and Coopers & Lybrand. Bill received his Bachelor of Science degree in Mathematics from Yale University, and his Master of Science degree in Computer Science from New Mexico State University.
Lowell is responsible for directing thought leadership and advisory services in the Customer Success practice of Collibra. He has been a practitioner in the data management industry for three decades and is recognized as a leader in data governance, analytics and data quality having hands-on experience with implementations across most industries. Lowell is a co-author of the book “Business Metadata; Capturing Enterprise Knowledge . Lowell is a past adjunct professor at Daniels College of Business, Denver University, a past President and current VP of Education for DAMA-I Rocky Mountain Chapter (RMC), a DAMA-I Charter member and member of the Data Governance Professionals Organization. He is also an author and reviewer on the DAMA-I Data Management Book of Knowledge (DMBOK). He focuses on practical data governance practices and has trained thousands of professionals in data governance, data warehousing, data management and data quality techniques. You can read his Data Governance Blogs at https://www.collibra.com/blog/
The text provides background as to how analytical solutions and enterprise architecture methodologies and concepts have evolved (including the roles of data warehouses, business intelligence tools, predictive analytics, data discovery, Big Data, and the impact of the Internet of Things). Then you’re taken through a series of steps by which to define a future state architecture and create a plan for how to reach that future state.
Enterprise Information Architecture for a New Age: Big Data and The Internet of Things helps you gain an understanding of the following:Implications of Big Data from a variety of new data sources (including data from sensors that are part of the Internet of Things) upon an information architectureHow establishing a vision for data usage by defining a roadmap that aligns IT with line-of-business needs is a key early stepThe importance and details of taking a step-by-step approach when dealing with shifting business challenges and changing technology capabilitiesHow to mitigate risk when evaluating existing infrastructure and designing and deploying new infrastructure
Enterprise Information Architecture for a New Age: Big Data and The Internet of Things combines practical advice with technical considerations. Author Robert Stackowiak and his team are recognized worldwide for their expertise in large data solutions, including analytics. Don’t miss your chance to read this book and gain the benefit of their advice as you look forward in thinking through your own choices and designing your own architecture to accommodate the burgeoning explosion in data that can be analyzed and converted into valuable information to drive your business forward toward success.
Begin with an introduction to high-availability and disaster recovery concepts such as Recovery Point Objectives (RPOs), Recovery Time Objectives (RTO), availability levels, and the cost of downtime. Then move into detailed coverage of implementing and configuring the AlwaysOn feature set in order to meet the business objectives set by your organization.SQL Server AlwaysOn Revealed offers real-world advice on how to build and configure the most appropriate topology to meet the high-availability and disaster recovery requirements you are faced with. Content includes strong coverage on implementing clusters, on building AlwaysOn failover clustered instances, and on configuring AlwaysOn Availability Groups. This is a practical and hand-on book to get you started quickly in using one of the most talked-about SQL Server feature sets. Teaches you to build HA and DR solutions using the AlwaysOn feature set Provides real-world advice on configuration and performance considerations Demonstrates administrative techniques for the AlwaysOn feature set
What You Will LearnUnderstand high availability and disaster recovery in SQL Server 2016 Build and configure a Windows Cluster
Who This Book Is For
Database administrators interested in growing their knowledge and skills in Microsoft SQL Server’s high-availability and disaster recovery feature set.
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 DataUnderstand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive dataDiscusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using itShows 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
The book is divided into 11 chapters, which cover the following:Overview of transaction processing application and system structureSoftware abstractions found in transaction processing systemsArchitecture of multitier applications and the functions of transactional middleware and database serversQueued transaction processing and its internals, with IBM's Websphere MQ and Oracle's Stream AQ as examplesBusiness process management and its mechanismsDescription of the two-phase locking function, B-tree locking and multigranularity locking used in SQL database systems and nested transaction lockingSystem recovery and its failuresTwo-phase commit protocolComparison between the tradeoffs of replicating servers versus replication resourcesTransactional middleware products and standardsFuture trends, such as cloud computing platforms, composing scalable systems using distributed computing components, the use of flash storage to replace disks and data streams from sensor devices as a source of transaction requests.
The text meets the needs of systems professionals, such as IT application programmers who construct TP applications, application analysts, and product developers. The book will also be invaluable to students and novices in application programming.Complete revision of the classic "non mathematical" transaction processing reference for systems professionals. Updated to focus on the needs of transaction processing via the Internet-- the main focus of business data processing investments, via web application servers, SOA, and important new TP standards. Retains the practical, non-mathematical, but thorough conceptual basis of the first edition.
You don’t need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system.Written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systemsHelps you to understand the trade-offs implicit in various models and model architecturesProvides extensive coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule inductionLays out a roadmap for exploring data, selecting model system measures, organizing adaptive feedback loops, selecting a model configuration, implementing a working model, and validating the final modelIn an extended example, applies evolutionary programming techniques to solve a complicated scheduling problemPresents examples in C, C++, Java, and easy-to-understand pseudo-codeExtensive online component, including sample code and a complete data mining workbench
The perspective of the book is from the top down: looking at the overall architecture and then delving into the issues underlying the components. This allows people who are building or using a data warehouse to see what lies ahead and determine what new technology to buy, how to plan extensions to the data warehouse, what can be salvaged from the current system, and how to justify the expense at the most practical level. This book gives experienced data warehouse professionals everything they need in order to implement the new generation DW 2.0.
It is designed for professionals in the IT organization, including data architects, DBAs, systems design and development professionals, as well as data warehouse and knowledge management professionals.* First book on the new generation of data warehouse architecture, DW 2.0.
This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projectsAddresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fieldsProvides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data