pic_cooming_soon

Online Lectures:
Further Reading:

1. Narayanan: Oracle SQL Developer Data Modeler for Database Design Mastery (Oracle Press) Paperback – 1 Jun 2015

oracle

In this practical guide, Oracle ACE Director Heli Helskyaho explains the process of database design using Oracle SQL Developer Data Modeler―the powerful, free tool that flawlessly supports Oracle and other database environments, including Microsoft SQL Server and IBM DB2. Oracle SQL Developer Data Modeler for Database Design Mastery covers requirement analysis, conceptual, logical, and physical design, data warehousing, reporting, and more. Create and deploy high-performance enterprise databases on any platform using the expert tips and best practices in this Oracle Press book.

  • Configure Oracle SQL Developer Data Modeler
  • Perform requirement analysis
  • Translate requirements into a formal conceptual data model and process models
  • Transform the conceptual (logical) model into a relational model
  • Manage physical database design
  • Generate data definition language (DDL) scripts to create database objects
  • Design a data warehouse database
  • Use subversion for version control and to enable a multiuser environment
  • Document an existing database
  • Use the reporting tools in Oracle SQL Developer Data Modeler
  • Compare designs and the database
  • Paperback: 336 pages
  • Publisher: McGraw-Hill Education (1 Jun. 2015)
  • Language: English
  • ISBN-10: 0071850090
  • ISBN-13: 978-0071850094
  • Product Dimensions: 18.7 x 1.9 x 23.2 cm

Buy at Amazon.co.uk

2. Lindstedt and Olschimke: Building a Scalable Data Warehouse with Data Vault 2.0 Paperback – 15 Oct 2015

data_ware

The Data Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical data warehousing failures. “Building a Scalable Data Warehouse” covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. The book discusses how to build the data warehouse incrementally using the agile Data Vault 2.0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss: * How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes. * Important data warehouse technologies and practices. * Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture. * Provides a complete introduction to data warehousing, applications, and the business context so readers can get-up and running fast * Explains theoretical concepts and provides hands-on instruction on how to build and implement a data warehouse* Demystifies data vault modeling with beginning, intermediate, and advanced techniques* Discusses the advantages of the data vault approach over other techniques, also including the latest updates to Data Vault 2.0 and multiple improvements to Data Vault 1.0

  • Paperback: 684 pages
  • Publisher: Morgan Kaufmann Publishers In (15 Oct. 2015)
  • Language: English
  • ISBN-10: 0128025107
  • ISBN-13: 978-0128025109
  • Product Dimensions: 19 x 3.3 x 23.1 cm

Buy at Amazon.co.uk

3. Inmon and Lindstedt: Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault. Paperback– 26 Nov 2014

data_arc

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 DataUnderstand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data.

  • Paperback: 378 pages
  • Publisher: Morgan Kaufmann (26 Nov. 2014)
  • Language: English
  • ISBN-10: 012802044X
  • ISBN-13: 978-0128020449
  • Product Dimensions: 19 x 2.2 x 23.4 cm

Buy at Amazon.co.uk