Big Data Fundamentals, New Zealand

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Big data is a hot topic in BI and analytics. Yet it is a complex topic that is still in the early stages of evolution. Successful big data projects that deliver real business value are challenged by multiple definitions and rapidly shifting technologies. Achieving good return on your big data investment requires strategy that focuses on purpose, people, and process before exploring data and technologies. Strategy drives planning and architecture to ensure that big data complements and does not disrupt the existing BI and analytics environment. To prepare for success with big data, start by understanding all of the pieces and how they fit together.

There are no prerequisites for this course.

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

Upcoming Trainings

Join our public courses in our New Zealand facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

Classroom / Virtual Classroom

07 November 2024