Introduction Data Warehousing and Business Intelligence: What, Why, How, When, When Not? Taking IT Intelligence to Its Apex Open Source DW and BI: Much Ado about Anything-to-Everything DW and BI, When Not, and Why So Much Ado?Taking Business Intelligence to Its Apex: Intelligent Content for Insightful Intent Data Warehousing and Business Intelligence: An Open Source Solution What Is Open Source DW and BI, and How "Open" Is This Open?What’s In, What’s Not: Available and Viable Options for Development and Deployment Semantic Analytics Testing for Optimizing Quality and Automation—Accelerated! Business Rules, Real-World Perspective, Social Context Personalization Through Customizable Measures Leveraging the Cloud for Deployment The Foundations Underneath: Architecture, Technologies, and MethodologiesOpen Source versus Proprietary DW and BI Solutions: Key Differentiators and Integrators Open Source DW and BI: Uses and Abuses An Intelligent Query Accelerator Using an Open Cache In, Cache Out Design Open Source DW & BI: Successful Players and Products Open Source Data Warehousing and Business Intelligence Technology Licensing Models Followed Community versus Commercial Open Source The Primary Vendors: Inventors and Presenters Oracle: MySQL Vendor PostgreSQL Vendor Infobright Pentaho: Mondrian Vendor Jedox: Palo Vendor EnterpriseDB Vendor Dynamo BI and Eigenbase: LucidDB Vendor GreenPlum Vendor Hadoop Project HadoopDBTalendThe Primary Products and Tools Set: Inclusions and Exclusions Open Source Databases Open Source Data Integration Open Source Business Intelligence Open Source Business AnalyticsThe Primary Users: User, End-User, Customer and Intelligent Customer MySQL PostgreSQLMondrian Customers Palo Customers EnterpriseDB Customers LucidDB Customers Greenplum Customers Talend Customers References Analysis, Evaluation, and Selection Essential Criteria for Requirements Analysis of an Open Source DW and BI solution Key and Critical Deciding Factors in Selecting a Solution The Selection-Action Preview Raising your BIQ: Five Things Your Company Can Do Now Evaluation Criteria for Choosing a Vendor-Specific Platform and Solution The Final Pick: An Information-Driven, Customer-Centric Solution, and a Best-of-Breed Product/Platform and Solution Convergence Key Indicator ChecklistReferences Design and Architecture: Technologies and Methodologies by Dissection The Primary Aspects of DW and BI from a Usability Perspective: Strategic BI, Pervasive BI, Operational BI, and BI On-Demand Design and Architecture Considerations for the Primary BI Perspectives The Case for Architecture as a Precedence Factor Information-Centric, Business-Centric, and Customer-Centric Architecture: A Three-in-One Convergence, for Better or Worse Open Source DW and BI Architecture Pragmatics and Design Patterns ComponentsWhy and How an Open Source Architecture Delivers a Better Enterprise-wide SolutionOpen Source Data Architecture: Under the Hood Open Source Data Warehouse Architecture: Under the Hood Open Source BI Architecture: Under the HoodThe Vendor/Platform Product(s)/Tools(s) That Fit into the Open DW and BI Architecture Information Integration, Usability and Management (Across Data Sources, Applications and Business Domains) EDW: Models to Management BI: Models to Interaction to Management to Strategic Business Decision Support (via Analytics and Visualization)Best Practices: Use and Reuse Operational BI and Open Source Why a Separate Chapter on Operational BI and Open Source? Operational BI by Dissection Design and Architecture Considerations for Operational BI Operational BI Data Architecture: Under the HoodA Reusable Information Integration Model: From Real- Time to Right Time Operational BI Architecture: Under the Hood Fitting Open Source Vendor/Platform Product(s)/Tools(s) into the Operational BI Architecture Talend Data Integration expressor 3.0 Community Edition Advanced Analytics Engines for Operational BI Astera’s Centerprise Data Integration Platform Actuate BIRT BI Platform JasperSoft Enterprise Pentaho Enterprise BI Suite KNIME (Konstanz Information Miner) Pervasive DataRush Pervasive DataCloud2 Best Practices: Use and Reuse Development and DeploymentDevelopment Options, DissectedDeployment Options, Dissected Integration Options, Dissected Multiple Sources, Multiple Dimensions DW and BI Usability and Deployment: Best Solution versus Best-Fit Solution Leveraging the Best-Fit Solution: Primary Considerations Better, Faster, Easier as the Hitchhiker’s Rule Dynamism and Flash—Real Output in Real Time in the Real World InteractivityBetter Responsiveness, User Adoptability, and Transparency Fitting the Vendor/Platform Product(s)/tTools(s): A Development and Deployment Standpoint Best Practices: Use and Reuse Best Practices for Data Management Best Fit of Open Source in EDW Implementation Best Practices for Using Open Source as a BI-Only Methodology for Data/Information Delivery Mobile BI and Pervasive BI Best Practices for the Data Lifecycle in a Typical EDW Lifecycle Data Quality, Data Profiling, and Data Loss Prevention Components The Data Integration Component Best Practices for the Information Lifecycle as It Moves into the BI Lifecycle The Data Analysis Component: The Dimensions of Data Analysis in Terms of Online Analytics vs. Predictive Analytics vs. Real-Time Analytics vs. Advanced Analytics Data to Information Transformation and Presentation Best Practices for Auditing Data Access, as It Makes Its Way via the EDW and Directly Bypassing the EDW) to the BI Dashboard Best Practices for Using XML in the Open Source EDW/BI Space Best Practices for a Unified Information Integrity and Security Framework Object to Relational Mapping: A Necessity or Just a Convenience? Synchrony Maintenance Dynamic Language Interoperability Best Practices for Application ManagementUsing Open Source as an End-to-End Solution Option: How Best a Practice Is It?Accelerating Application Development: Choice, Design, and Suitability Aspects Visualization of Content: For Better or Best Fit Best Practices for Autogenerating Code: A Codeless Alternative to Information Presentation Automating Querying: Why and When How Fine Is Fine-Grained? Drawing the Line between Representation of Data at the Lowest Level and a Best-Fit Metadata Design and Presentation Best Practices for Application Integrity Sharing Data between EDW and the BI Tiers: Isolation or a Tightrope Methodology Breakthrough BI: Self-Serviceable BI via a Self-Adaptable Solution Data-In, Data-Out Considerations: Data-to-Information I/O Security Inside and Outside Enterprise Parameters: Best Practices for Security beyond User AuthenticationBest Practices for Intra- and Interapplication Integration and InteractionContinuous Activity Monitoring and Event Processing Best Practices to Leverage Cloud-Based Methodologies Best Practices for Creative BI Reporting Best Practices Beyond Reporting: Driving Business ValueAdvanced Analytics: The Foundation for a Beyond-Reporting Approach (Dynamic KPI, Scorecards, Dynamic Dashboarding, and Adaptive Analytics)Large Scale Analytics: Business-centric and Technology-centric Requirements and Solution Options Business-centric Requirements Technology-centric Requirements Accelerating Business Analytics: What to Look for, Look at, and Look Beyond Delivering Information on Demand and Thereby Performance on Demand Design Pragmatics Demo Pragmatics EDW/BI Development Frameworks From the Big Bang to the Big Data Bang: The Past, Present, and Future A Framework for BI Beyond Intelligence Raising the Bar on BI Using Embeddable BI and BI in the Cloud Raising the Bar on BI: Good to Great to Intelligent Raising the Bar on the Social Intelligence Quotient (SIQ) Raising the Bar on BI by Mobilizing BI: BI on the Go A Pragmatic Framework for a Customer-Centric EDW/BI Solution A Next-Generation BI Framework Taking EDW/BI to the Next Level: An Open Source Model for EDW/BI–EPM Open Source Model for an Open Source DW–BI/EPM Solut
