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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

Business Metadata: Capturing Enterprise Knowledge is the first book that helps businesses capture corporate (human) knowledge and unstructured data, and offer solutions for codifying it for use in IT and management. Written by Bill Inmon, one of the fathers of the data warehouse and well-known author, the book is filled with war stories, examples, and cases from current projects. It includes a complete metadata acquisition methodology and project plan to guide readers every step of the way, and sample unstructured metadata for use in self-testing and developing skills. 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. First book that helps businesses capture corporate (human) knowledge and unstructured data, and offer solutions for codifying it for use in IT and management Written by Bill Inmon, one of the fathers of the data warehouse and well-known author, and filled with war stories, examples, and cases from current projects Very practical, includes a complete metadata acquisition methodology and project plan to guide readers every step of the way Includes sample unstructured metadata for use in self-testing and developing skills

Business Metadata: Capturing Enterprise Knowledge is the first book that helps businesses capture corporate (human) knowledge and unstructured data, and offer solutions for codifying it for use in IT and management.

Datamart vendors care about sales, quarterly revenue, and their own bottom line. A company's decision support systems (DSS) architectural vision may present a barrier to a quick sale. In almost every case, the datamart vendor tries to dispense with the idea of DSS architecture as quickly as possible in order to get to a sale of its product. The central issue is whether the datamart should be built directly from operational systems or whether it should be built on top of a properly constructed data warehouse. Datamart vendors say datamarts can and should be built directly from the legacy application systems because this will be faster than first building the data warehouse and then building the datamart on top. In the long run, the datamart vendors are sowing the seeds of great dissatisfaction. The long-term effect of trying to build datamarts without the data warehouse is disaster.

Data warehouse administrators in many corporations that are considering multiple data warehouses are wondering whether the presence of multiple data warehouses in one corporation would be consistent with the notion of a sound Decision Support System (DSS) architecture. It depends on how the terms are defined. The 2 most common types of data warehouses are the current-level detailed-data warehouse and the data mart. A current-level detailed-data warehouse is a storage facility in which very granular atomic data are gathered and integrated. A data mart typically contains both summarized and customized data that reflect the individual tastes and needs of the sponsoring departments. For large multinational corporations, it makes sense to consider the possibility of multiple current-level detailed-data warehouses. However, considerable overlap among databases will make it difficult, if not impossible, to achieve a high degree of corporate integration. While the approach of implementing just data marts has immediate appeal, its long-term effects are disastrous.

Metadata is data about data. Typical forms of metadata include tables, attributes, keys, indexes, and so forth. Metadata is typically kept in a repository. The repository can be active or passive. There are many uses of metadata including using metadata for analytical purposes, using metadata to compare multiple systems, establishing the lineage of data, comparing the existing environment to a proposed environment, and so forth.

The components of data architecture include the transactional environment and other operational systems, the ETL function, the data warehouse, an ODS, data marts, and Big Data. Big Data is divided into unstructured repetitive data and unstructured nonrepetitive data. At the heart of data architecture is the notion of the system of record.

In addition to corporate data there is individual data, or personal data. Personal data typically resides on the PC or workstation. In many ways the personal data environment becomes an individual “sandbox.” The essence of personal data is autonomy of processing. Because of the autonomy of processing, personal data normally is not mixed with corporate data.