Bulusu Open Source Data Warehousing and Business Intelligence

Erscheinungsjahr 2013
ISBN: 978-1-4398-1641-7
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 432 Seiten

Open Source Data Warehousing and Business Intelligence is an all-in-one reference for developing open source based data warehousing (DW) and business intelligence (BI) solutions that are business-centric, cross-customer viable, cross-functional, cross-technology based, and enterprise-wide. Considering the entire lifecycle of an open source DW & BI implementation, its comprehensive coverage spans from basic concepts all the way through to customization.

Highlighting the key differences between open source and vendor DW and BI technologies, the book identifies end-to-end solutions that are scalable, high performance, and stable. It illustrates the practical aspects of implementing and using open source DW and BI technologies to supply you with valuable on-the-project experience that can help you improve implementation and productivity.

Emphasizing analysis, design, and programming, the text explains best-fit solutions as well as how to maximize ROI. Coverage includes data warehouse design, real-time processing, data integration, presentation services, and real-time reporting. With a focus on real-world applications, the author devotes an entire section to powerful implementation best practices that can help you build customer confidence while saving valuable time, effort, and resources.

Zielgruppe

Data warehouse developers, managers, and administrators; business analysts; and IT managers.

Autoren/Hrsg.

Bulusu, Lakshman

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Warehouse
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik

Inhaltsverzeichnis

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 Methodologies Open 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 HadoopDB Talend
The Primary Products and Tools Set: Inclusions and Exclusions Open Source Databases Open Source Data Integration Open Source Business Intelligence Open Source Business Analytics
The Primary Users: User, End-User, Customer and Intelligent Customer MySQL PostgreSQL Mondrian 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 Checklist
References

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 Components
Why and How an Open Source Architecture Delivers a Better Enterprise-wide Solution
Open Source Data Architecture: Under the Hood

Open Source Data Warehouse Architecture: Under the Hood

Open Source BI Architecture: Under the Hood

The 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 Hood
A 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 Deployment
Development Options, Dissected
Deployment 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 Interactivity

Better 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 Management

Using 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 Authentication
Best Practices for Intra- and Interapplication Integration and Interaction Continuous Activity Monitoring and Event Processing Best Practices to Leverage Cloud-Based Methodologies

Best Practices for Creative BI Reporting

Best Practices Beyond Reporting: Driving Business Value
Advanced 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 Solution Delivering Business Value Open Source Architectural Framework for a Best-Fit Open Source BI/EPM Solution Value Proposition The Road Ahead.
A BI Framework for a Reusable Predictive Analytics Model

A BI Framework for Competitive Intelligence: Time, Technology, and the Evolution of the Intelligent Customer

Best Practices for Optimization
Accelerating Application Testing: Choice, Design, and Suitability

Best Practices for Performance Testing: Online and On Demand Scenarios

A Fine Tuning Framework for Optimality

Looking Down the Customer Experience Trail, Leaving the Customer Alone: Customer Feedback Management (CFM)–Driven and APM-Oriented Tuning

Codeful and Codeless Design Patterns for Business-Savvy and IT-Friendly QOS Measurements and In-Depth Impact Analysis

Summary

Open Standards for Open Source: An EDW/BI Outlook

Summary

References

Index
Each chapter includes an Introduction and Summary

Über Autor(innen)

Lakshman Bulusu is a 20-year veteran of the IT industry with specialized expertise and academic experience in the management, supervision, mentoring, review, architectural design, and development of database, data warehousing, and business intelligence-related application development projects encompassing major industry domains such as pharmaceutical/healthcare, telecommunications, news/media, global investment and retail banking, insurance, and retail for clients across the United States, Europe, and Asia. He is well-versed in the primary Oracle technologies through Oracle11g, including SQL, PL/SQL, and SQL-embedded programming, as well as design and development of Web applications that are cross-platform and open source-based.

Mr. Bulusu has expertise in data modeling and design of enterprise data warehousing/business intelligence information architectures, with multiple customer implementations to his credit. His design of application development frameworks using PL/SQL, from design to coding to testing to debugging to performance tuning to business intelligence, has been implemented in some major Fortune 500 clients in the United States. He has implemented the Common Data Quality Framework for SQL Server, based on summarization-comparison-discrepancy isolation across disparate multivendor large-scale databases. He is also an educator who has been teaching technical courses for about a decade in the areas of Oracle design, development, and optimization, and he serves on the CNS Advisory Committee of Anthem Institute (affiliated to Anthem Education Group).

Mr. Bulusu has authored six books on Oracle and more than fifty educational/technical articles in journals and magazines in the United States and the United Kingdom; he has also presented at national and international conferences in the United States and the United Kingdom. He lives in New Jersey and likes to read, write, listen to, and lecture on English poetry and nonfiction when he is not working on IT projects. He can be reached at [e-mail].