Lecture "Administration and visualization: Chapter - Data modelling and databases OLTP & OLAP" provides students with content about: Overview; OLTP vs OLAP; Data warehouse modeling; Data warehouse design; Data warehouse Implementation; . Please refer to the detailed content of the lecture! | Chapter 3 Data modelling and databases OLTP amp OLAP 1 Outline Overview OLTP vs OLAP Data warehouse modeling Data warehouse design Data warehouse Implementation 2 Heterogeneous data sources 3 Why data integration To facilitate information access and reuse through a single information access point Data from different complementing information systems is to be combined to gain a more comprehensive basis to satisfy the need Improve decision making Improve customer experience Increase competitiveness Streamline operations Increase productivity Predict the future 4 Data integration challenges Physical systems Various hardwares standards Distributed deployment Various data format Logical structures Different data models Different data schemas Business organization Data security and privacy Business rules and requirements Different administrative zones in the business organization 5 Data Warehouse A single complete and consistent store of data obtained from a variety of different sources made available to end users in a what they can understand and use in a business context. Barry Devlin A data warehouse is a copy of transaction data specifically structured for query and analysis Ralph Kimball Data from several operational sources OLTP are extracted transformed and loaded ETL into a data warehouse Data Warehouse usage Three kinds of data warehouse applications Information processing supports querying basic statistical analysis and reporting using crosstabs tables charts and graphs Analytical processing multidimensional analysis of data warehouse data supports basic OLAP operations slice-dice drilling pivoting Data mining knowledge discovery from hidden patterns supports associations constructing analytical models performing classification and prediction and presenting the mining results using visualization tools 7 Data Warehouse usage Which are our lowest highest margin customers Who are my customers What is the most and what products effective distribution are they .
