Book Image

Business Intelligence Cookbook: A Project Lifecycle Approach Using Oracle Technology

By : John Heaton
Book Image

Business Intelligence Cookbook: A Project Lifecycle Approach Using Oracle Technology

By: John Heaton

Overview of this book

Oracle Database 11g is a comprehensive database platform for data warehousing and business intelligence that combines industry-leading scalability and performance, deeply-integrated analytics, and embedded integration and data-quality all in a single platform running on a reliable, low-cost grid infrastructure. This book steps through the lifecycle of building a data warehouse with key tips and techniques along the way. Business Intelligence Cookbook: A Project Lifecycle Approach Using Oracle Technology outlines the key ways to effectively use Oracle technology to deliver your business intelligence solution. This is a practical guide starting with key recipes for project management then moving onto project delivery. Business Intelligence Cookbook: A Project Lifecycle Approach Using Oracle Technology is a practical guide for performing key steps and functions on your project. This book starts with setting the foundation for a highly repeatable efficient project management approach by assessing your current methodology to see how suitable it is for a business intelligence program. We also learn to set up the project delivery phases to consistently estimate the effort for a project. Along the way we learn to create blueprints for the business intelligence solution that help to connect and map out the destination of the solution. We then move on to analyze requirements, sources, and data. Finally we learn to secure the data as it is an important asset within the organization and needs to be secured efficiently and effectively.

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Business Intelligence Cookbook: A Project Lifecycle Approach Using Oracle Technology
John Heaton
Jul, 2012 | 368 pages
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Table of Contents (21 chapters)
Business Intelligence Cookbook: A Project Lifecycle Approach Using Oracle Technology
Business Intelligence Cookbook: A Project Lifecycle Approach Using Oracle Technology
Credits
Credits
About the Author
About the Author
Acknowledgement
Acknowledgement
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Defining a Program
Defining a Program
Introduction
Program or a project
Mapping your business culture
Adapting your project delivery methodology
Assessing your project team
Organizing your project team
2
Establishing the Project
Establishing the Project
Introduction
Creating Definition work practice
Creating Data Discovery work practice
Creating Development work practice
Creating Testing work practice
Creating Promote work practice
Creating Production work practice
Building a standard work breakdown structure
Identifying and quantifying the effort drivers
Creating your estimation tool
3
Controlling the Project
Controlling the Project
Introduction
Requirements Traceability Matrix
Creating an effective risk register
Creating an effective issue management register
Creating a defect and enhancement register
Creating a cyclical build and test process
Setting up a collaborative environment
Installing client tools for a collaborative environment
4
Wrapping Up the Project
Wrapping Up the Project
Introduction
Creating OWB code artifacts
Creating OBIEE code artifacts
Creating APEX code artifacts
Creating ODI code artifacts
Creating script artifacts
Building a continuous development capability for enhancements
Creating a constant feedback and communications loop
5
The Blueprint
The Blueprint
Introduction
Outlining your business processes
Categorizing your metrics, analysis, and reports within the business process
Decomposing your analysis and reports to define business entities
Developing your semantic data model
Identifying your source of record for your business entities
Building the blueprint
6
Analyzing the Requirements
Analyzing the Requirements
Introduction
Decomposing the reports and requirements
Defining the business rules
Categorizing the business requirements by business drivers
Prioritizing the business requirements
Adding hierarchies to your semantic data model
Adding metrics to your semantic data model
Defining your data dictionary
Defining your security requirements
Defining your data retention requirements
7
Architecture and Design
Architecture and Design
Introduction
Choosing your database type
Defining your database layout
Selecting the Third Normal Form or a Dimensional model
8
Analyzing the Sources
Analyzing the Sources
Introduction
Validating and enhancing a conceptual data model
Creating a business process matrix
Creating a report requirements matrix
Creating a source matrix
Developing the data lineage
Defining the detailed transformations
9
Analyzing the Data
Analyzing the Data
Introduction
Building high and low data profiling scripts
Building domain/distinct values profiling
Building record count profiling scripts
Building density data profiling scripts
Building hierarchy data profiling scripts
Building data lengths data profiling scripts
Building significant columns data profiling scripts
Building changing information data profiling scripts
Building automated data profiling with Oracle Warehouse Builder
10
Constructing the Data Model
Constructing the Data Model
Introduction
Connecting Oracle SQL Data Modeler to Subversion
Importing data models into Subversion
Checkout data models from Subversion
Synchronizing data model changes with Subversion
How to import data models
How to reverse engineer your relational data model to a logical data model
Creating your domains
Creating your glossary
Adding Standard columns to your data model
How to forward engineer your logical data model to a relational data model
Creating your enterprise data models
11
Defining the ETL/ELT
Defining the ETL/ELT
Introduction
Abstracting your source system
Separating your extraction from your loading and transforming routines
Adding additional columns to facilitate error trapping and correction
Designing ETL error trapping and detection routines
Designing ETL data reconciliation routines
Designing a notification routine
12
Enhancing the Data
Enhancing the Data
Introduction
Creating your application schema
Creating your application tables
Developing the journal tables to track changes
Defining the audit triggers
Defining the APEX Upload application
Creating the Upload interface
13
Optimizing the Access
Optimizing the Access
Introduction
Developing your standards and guidelines
Abstracting your tables using aliases
Developing level-based hierarchies
Creating multi-table hierarchies
Consolidating reports using the column selector
Enabling dynamic column headings
Enabling dynamic descriptions
Enabling multi-language for the shared captions
14
Security
Security
Introduction
Creating an APEX custom authentication procedure
Creating a row-level Virtual Private Database (VPD)
Creating a column-level Virtual Private Database
Creating Virtual Private application context
Configuring OBIEE for multiple security providers
Integrating Microsoft Active Directory into OBIEE
Creating and configuring OBIEE roles
Configuring OBIEE privileges
Configuring OBIEE catalog security
Enabling Virtual Private Database in OBIEE

Building data lengths data profiling scripts

Understanding the scale, precision, and length of common columns is important to be able to construct your data model.

Getting ready

Gather the source user name and passwords for the source system.

How to do it...

Understanding the scale and precision will allow you to appropriately plan for the correct data structures within the data warehouse:

  1. 1. Connect to the source system using Oracle SQL Developer as the schema owner of the objects you are profiling.

  2. 2. Determine the datatypes you may be working with.

    Sample SQL statement: 

    select distinct data_type
from user_tab_columns
where data_type not like '%$%';

  3. 3. Check the lengths of character datatypes.

    Sample SQL statement: 

    select distinct data_type, data_length from user_tab_columns where data_type like '%CHAR%' and data_length > 1 order by data_length;

  4. 4. Check the scale and precision of numeric datatypes.

    Sample SQL statement: 

    select distinct data_type, nvl(to_char(data_precision),'Default'...
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