IT / Data Warehousing / Business Intelligence / OLAP and Data Warehousing
| ID | 2757913 |
|---|---|
| Classroom | 2 days 9:00-16:30 |
| Webinar | 4 days 9:00-12:30 |
| Method | Lecture and discussion |
| Prequisite | General database knowledge |
| Audience | Business Intelligence Developer |
A data warehouse (DWH) is a database used for reporting and data analysis. It is a central repository of data which is created by integrating data from one or more disparate sources. Data warehouses store current as well as historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons. Online Analytical Processing (OLAP) is an approach to answering multi-dimensional analytical queries swiftly. OLAP is part of the broader category of business intelligence, which also encompasses relational database, report writing and data mining. This training walks you through the typical Business Intelligence project and helps you to understand the elements and architecture of a DWH and the usage scenarios for OLAP.
Goals of a Data Warehouse - Components of a Data Warehouse: Operational Source Systems, Data Staging Area, Data Presentation, Data Access Tools - Dimensional Modeling: Fact Tables, Dimension Tables
Operational Data - The Data Warehouse and Data Models: The Data Warehouse Data Model, The Midlevel Data Model, The Physical Data Model - Normalization and Denormalization - Metadata - Technical and Physical Architecture - Deploying and Supporting the DW/BI System
The Relational Model - The Multidimensional Model - Snowflake Structures - Differences between the Models - Independent Data Marts - OLAP and Aggregations - OLAP Operations
ETL (Extract, Transformation, and Load) - Designing the Staging Area - Data Structures in the ETL System - Data Flow: Extracting, Cleaning and Conforming - Loading Fact Tables - Integrating OLAP Processing into the ETL System - Development Options of ETL - Data Latency - Data Quality
The Basic Structure of a Dimension - The Grain of a Dimension - Flat Dimensions and Snowflaked Dimensions - Date and Time Dimensions - Big and Small Dimensions - Dimensional Roles - Degenerate Dimensions - Slowly Changing Dimensions - Ragged Hierarchies and Bridge Tables
Business Intelligence Applications: Direct Access Query and Reporting Tools, Standard Reports, Analytic Applications, Dashboards and Scorecards - Data Mining: Data Mining Overview, Data Mining in the Applications Architecture
Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.
He works as an IT-consultant and project manager. He developed various Business Intelligence systems for industry clients and the public sector. For several years now, he is responsible for a BI-team in India which is mainly involved in BI and OLAP projects, reporting systems as well as statistical analysis and Data Mining.
He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.
