A data warehouse consolidates data into a common store and provides a single view of that data across disparate sources. For example, the data warehouse can bring together information from various departments in an organization, enabling a central view across the enterprise. Inadequate planning and management of data warehouse projects can lead to failed implementations and dissatisfied stakeholders. A successful data warehousing project involves precise planning and analysis to implement a data store as a basis for the business decision-making process. In this course, you gain the skills needed to develop an effective data warehouse solution.

A successful data warehouse project involves a set of concepts and methods designed to implement a data store as a basis for the business decision-making process. In this course, you gain the skills needed to develop an effective roadmap to implementing a robust data warehouse solution. Specifically, you learn to apply dimensional modeling to improve business decisions; extract, transform and load operational data based on organizational needs; and employ business intelligence (BI) tools to slice and dice data.

By the end of this course, delegates will be able to:

· Deploy and maintain a data warehousing project

· Apply dimensional modeling of the business process to evaluate data

· Profile data to ensure source data quality

· Extract, transform and load operational data

· Employ Business Intelligence (BI) tools to slice and dice data

· Explore data-mining techniques to provide business value

· Learn how to match business processes to dimensions

· Learn how to design facts and dimensions

· Complete a database design using a modeling tool

· Forward engineering a database from a logical model

· Extract, transform and load data & employ key performance indicators

Database administrators, data architects, IT managers, information architects, BI developers, report writers and business analysts, anyone interested in implementing a data warehouse solution in their organization, and those who need to reengineer an existing data warehouse, anyone interested in implementing a data warehouse solution in their organization

Introduction to Data Warehousing

· Identifying data warehouse components

· Analysing failed installations

· Top-down vs. bottom-up approach

· Selecting the appropriate technique for your environment

Modelling the Business Process

· Relating business processes to dimensions with a Bus Matrix

· Reviewing demoralization techniques to optimize database read performance

· Identifying the advantages of star schema over snowflake schemas

· Differentiating between normalized and renormalized system design

· Enabling drill-up and drill-down reporting through the use of hierarchies

Building Fact Tables

· Setting the grain of your fact tables to maintain a balance between flexibility and performance

· Identifying primary keys and foreign keys

· Tailoring business analysts' needs through the use of summary tables

Creating Dimensions

· Resolving date dimension issues

· Exploring connections between separate events with conformed dimensions

· Increasing model flexibility through the use of role-playing dimensions

· Substituting surrogate keys for natural keys

Architecting the Physical Database

· Mapping the logical data model to the physical data model

· Evaluating hardware considerations

· Choosing a method of data storage

· Indexing and materialized views for performance

Ensuring Data Quality

· Profiling source data

· Configuring the database for change data capture

· Refreshing the data warehouse

Extracting Data from Multiple Sources

· Connecting to different data sources

· Databases

· Spreadsheets

· Text files

Transforming Data during Migration

· Cleansing and scrubbing data

· Mapping data elements and data types

· Conforming dimensions and facts

Loading Data into a Data Warehouse

· Employing a staging database

· Automating incremental data loading

· Verifying success for data integrity

Identifying Levels in the BI Stack

· Demonstrating SharePoint as a deployment platform

· Utilizing Excel as a client tool for data analysis

· Querying data from the OLAP server

Exploring Business Intelligence Tools

· Multidimensional cubes, Slicing and dicing your data

· Surfacing Key Performance Indicators (KPIs) to evaluate the health of your organization

Taking Advantage of Industry-Standard Data-Mining Techniques

· Identifying data-mining methodologies

· Choosing the right data-mining tasks based on organizational needs

Planning for the Future

· Big Data and the impact on traditional data warehousing

· Supplementing the traditional data warehouse to handle unstructured data

· Dealing with large data sets

COURSE LOCATIONS