Data Warehouse Fundamentals Chapter 18 Paulraj Ponniah Ppt
Jemima Torguson
Data warehousing has revolutionized the way businesses in a wide variety of industries perform analysis and make strategic decisions. Since the first edition of Data Warehousing Fundamentals, numerous enterprises have implemented data warehouse systems and reaped enormous benefits. Many more are in the process of doing so. Now, this new, revised edition covers the essential fundamentals of data warehousing and business intelligence as well as significant recent trends in the field.
The author provides an enhanced, comprehensive overview of data warehousing together with in-depth explanations of critical issues in planning, design, deployment, and ongoing maintenance. IT professionals eager to get into the field will gain a clear understanding of techniques for data extraction from source systems, data cleansing, data transformations, data warehouse architecture and infrastructure, and the various methods for information delivery.
This practical Second Edition highlights the areas of data warehousing and business intelligence where high-impact technological progress has been made. Discussions on developments include data marts, real-time information delivery, data visualization, requirements gathering methods, multi-tier architecture, OLAP applications, Web clickstream analysis, data warehouse appliances, and data mining techniques. The book also contains review questions and exercises for each chapter, appropriate for self-study or classroom work, industry examples of real-world situations, and several appendices with valuable information.
Specifically written for professionals responsible for designing, implementing, or maintaining data warehousing systems, Data Warehousing Fundamentals presents agile, thorough, and systematic development principles for the IT professional and anyone working or researching in information management.
PAULRAJ PONNIAH, PHD, with over thirty years of experience as an IT consultant, has worked with such organizations as Texaco, Sotheby's, Blue Cross/Blue Shield, NA Philips, New York-Presbyterian Hospital, Panasonic, and Bantam Doubleday Dell. He specializes in the design and implementation of data warehouse and database systems. Dr. Ponniah has published three successful books and, as Adjunct Professor, continues to teach college courses in data warehousing and database design.
Data warehousing has revolutionized the way businesses in a widevariety of industries perform analysis and make strategicdecisions. Since the first edition of Data WarehousingFundamentals, numerous enterprises have implemented datawarehouse systems and reaped enormous benefits. Many more are inthe process of doing so. Now, this new, revised edition covers theessential fundamentals of data warehousing and businessintelligence as well as significant recent trends in the field.
The author provides an enhanced, comprehensive overview of datawarehousing together with in-depth explanations of critical issuesin planning, design, deployment, and ongoing maintenance. ITprofessionals eager to get into the field will gain a clearunderstanding of techniques for data extraction from sourcesystems, data cleansing, data transformations, data warehousearchitecture and infrastructure, and the various methods forinformation delivery.
This practical Second Edition highlights the areas ofdata warehousing and business intelligence where high-impacttechnological progress has been made. Discussions on developmentsinclude data marts, real-time information delivery, datavisualization, requirements gathering methods, multi-tierarchitecture, OLAP applications, Web clickstream analysis, datawarehouse appliances, and data mining techniques. The book alsocontains review questions and exercises for each chapter,appropriate for self-study or classroom work, industry examples ofreal-world situations, and several appendices with valuableinformation.
Specifically written for professionals responsible fordesigning, implementing, or maintaining data warehousing systems,Data Warehousing Fundamentals presents agile, thorough, andsystematic development principles for the IT professional andanyone working or researching in information management.
The difference between operational data and a data warehouse is that operational data comes from multiple sources, and it is where the original, legacy, and real time data is held, before sending it out to the data warehouse. End users do not have access to operational data, only data sent to the warehouse.
Data Warehousing is one of the most important activities and subsets of business intelligence, which is the activity that contributes to the growth of any company, and essentially consists of four steps:
Imagine a company having multiple data sources like Oracle, SQL, or SAP. The company would not initially be able to visualize the data collected since it is all separated. The data collected in the three different sources would have to be integrated and processed into a data warehouse first to be able to make visualizations.
This type of data warehouse encompasses everything we have discussed about them so far in the article, such as data classification by subjects and the bringing of data together from all sources of an enterprise or organization.
The goal of this type of data warehouse is to provide a complete overview of any object in the data model. This means that after all information is gathered by the enterprise data warehouse, people from within the organization can help identify patterns to focus on to help the business grow.
Operational data stores are sometimes subject oriented and time variant, helping in the storing of transactional data that comes from one or multiple production systems and loosely integrating it.
This form of data warehousing achieves integration by making use of the structures and contents found in enterprise data warehouses. This integration process involves checking business rules for integrity and redundancy.
A data mart is a subset of a data warehouse. It is easy to implement and very cost effective when compared with a complete data warehouse because data is divided into parts and it can also be more easily controlled.
This type of data warehouse has three different types: dependent, independent, and hybrid. A dependent data mart requires the ability to fetch data from an operational data store. An independent data mart does not require this or any central data warehouse. And hybrid data marts are used when inputs from different sources are a part of a data warehouse.
Learning data warehousing can take as little as one week for those with a background in IT development, while those with little to no background in tech can take up to seven months to learn data warehousing.
This specialization is taught by two business professors from the University of Colorado, with hands-on project and certification upon completion. Students can pay the tuition cost of $49 per month to obtain a certificate, but learning without the obtainment of the certification is free.
This course is highly rated by students. It focuses strictly on data warehousing and business intelligence subjects. The instructor also has a sneak peek video you can access to gain familiarity on the course and the teaching style.
The books shared below cover a wide range of data warehousing topics within the industry, some for those with experience in the field, though with concept definition, one with any level of background should be able to gain value from the books below.
This book is written for IT professionals working with or studying information management. It covers the topics on planning, designing, deployment, and ongoing maintenance of data warehousing. This book also contains questions and exercises at the end of each chapter for self-study work, as well as real-world industry situations.
Author Paulraj Ponniah has over 30 years of experience in IT consulting and has worked with major companies like Panasonic and Texaco. He specializes in design and the implementation of data warehousing and database systems.
This book focuses on the models, methods, design, and implementation for Big Data Warehousing (BDW), which is an emerging concept that can be potentially used as a replacement for the traditional data warehouse.
This book focuses on one of the top problems in data warehousing: data integration. This book is based on research of different software integration systems to illustrate the details of its case study.
The paid version of the online courses covered in this article all offer certification on data warehousing. Below is a list of additional certification options for those already familiar with the subject.
Data warehousing is utilized across many business sectors. Knowledge in the topic benefits data scientists and business leaders alike. Anyone working in a leadership position should learn data warehousing.