TDWI Big Data Fundamentals: Creating Value from Non-Traditional Data Sets Training in Republic of the Philippines

Business and data analysts; BI and analytics program and project managers; BI and data warehouse architects, designers, and developers; data governance and data quality professionals getting started with big data; anyone seeking to cut through the hype to understand the opportunities, challenges, and realities of the big data phenomenon.

• Common definitions of big data and the implications of each
• Key characteristics of big data and why size is not among the top five
• The structures that can be found in “unstructured” data
• Types of big data sources—streaming data, social data, sensor data, etc.
• Value opportunities and common applications for big data
• Considerations when adapting architectures, organizations, and cultures to incorporate big data
• The scope of big data processes, tools, and technologies

Module 1 – Big Data Basics

• What Is Big Data?
  • Definitions
  • Characteristics (3 V’s plus 2)
  • Types of Big Data
• Why Big Data Analytics – Extending Advanced Analytics Capabilities
• Big Data Use Cases
  • Customer Understanding and Targeting
  • Business Process Optimization
  • Healthcare Advances
  • Law Enforcement and Public Safety
  • Sports Performance Improvement
  • Public Transportation and Infrastructure Advances
• Why Big Data Now? – The Driving Forces
• Kinds of Big Data – Data Variety
• Sources of Big Data
  • Web and Social Media
  • Machine to Machine
  • Other Sources (Big Transaction Data, Biometrics, Human Generated Data,
  • Publicly Available Data, Legacy Documents)
• Working with Big Data – The Big Picture

Module 2 – Big Data Processes

• Business Case
  • Business Needs and Opportunities
  • Areas of Insight
  • Expected Outcomes
  • Business Value Projection
• Technical Case – Big Data Rationale
• Data Sourcing – Getting Big Data
• Data Preparation and Storage
  • Data Selection
  • Data Cleansing
  • Data Integration
  • Data Reduction
• Big Data Analytics
  • Problem Framing
  • Analytic Purpose
  • Analytic Modeling
  • Data Visualization
  • Consumption and Application

Module 3 – Big Data Architecture

• The Role of Architecture
  • What and why
• Data Architecture
  • Data Storage
  • Data Access
  • Data Analysis
  • Data Consumption
• Process Architecture
  • Data Governance Processes
  • Data Integration and Quality
• Analytics Architecture
  • Machine Learning
  • Predictive Analytics
  • Prescriptive Analytics
  • Descriptive Analytics
  • Reporting
• Technology Architecture
  • Search and Visualization
  • Data Management and Data Access
  • Hadoop and NoSQL
• Big Data ... Big Architecture – Summary

Module 4 – Big Data Technology

• The Technology Landscape – Overview
• Infrastructure
  • Databases
  • Development and Deployment Environment
• Analytics – Data Analysis
• Data Sources – Big Data Providers
• The Core Technologies
• MapReduce
• Hadoop

Module 5 – Getting Started with Big Data

• Readiness Assessment – Check Your Position
• Planning and Preparation – Charting the Course
• Execution – Navigating the Course
• Post-Project Activities – At the Destination
• Best Practices – Lessons Learned
• Mistakes to Avoid – More Lessons Learned

Module 6 – Summary and Conclusion

• Summary of Key Points – A Quick Review
• References and Resources – To Learn More