IT / Data Warehousing / Business Intelligence / OLAP and Data Warehousing

Business Intelligence - OLAP and Data Warehousing

Details

ID 2757913
Classroom 2 days
Webinar 4 days
Method Lecture and discussion
Prequisite General database knowledge
Audience Business Intelligence Developer

Services:

  • Lunch / Catering
  • Assistance for hotel / travel bookings
  • Comelio certificate
  • Flexible: Free cancellation up until 10 days before the training

Summary

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.

Training Dates

  • 2020-Oct-19 - Oct-22
  • 2020-Dec-28 - Dec-31
  • 2021-Mar-08 - Mar-11
  • 2021-May-17 - May-20

590 EUR +VAT

Location | Enrollment


Agenda

Business Intelligence, OLAP, and Data Warehousing

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

The Data Warehouse and Design

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 and the Multidimensional Models

The Relational Model - The Multidimensional Model - Snowflake Structures - Differences between the Models - Independent Data Marts - OLAP and Aggregations - OLAP Operations

ETL - Loading the Data Warehouse

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

Dimension Tables

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

OLAP and Data Mining

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

Trainer

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.

Publications
  • XML Schema ISBN 978-3-939701-54-5
  • Das Java Codebook ISBN 3827322359
  • Java EE 5 Das Handbuch ISBN 978-3446400238
  • Oracle PL/SQL ISBN 978-3-939701-40-8
  • MS SQL Server - T-SQL Programmierung und Abfragen ISBN 978-3-939701-02-6
  • XML: Standards und Technologien ISBN 978-3-939701-21-7
Projects

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.

Research

He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.