Big Data Engineering Practice
Tony Shan
This full-day tutorial introduces the practice of Big Data Engineering (BDE), defined as the pragmatic application of a systematic, disciplined, quantifiable approach to the end-to-end life cycle of Big Data solutioning. BDE is a holistic body of knowledge comprising several modules. The core is composed of Big Data Discipline Areas (BDDA) and Big Data Lifecycle (BDLC). BDDA focuses on eight crucial areas: Methodology, Program, Governance, Resources, Quality, Risk Mitigation, KPI & Financials, and Competency. BDLC systematically addresses individual stages of Big Data solutions: Inception, Requirement, Analysis, Modeling, Platform, Design, Development, Integration, Testing, Runtime, Deployment, and Operation. Each of these eight areas and 12 stages comprises specific elements as sub-disciplines.

We will drill down to selected components and aspects. For example, the NoSQL platform options include key-value, column-based, document-oriented, graph, NewSQL, and in-memory stores. Case studies and working examples will be discussed in great detail to illustrate the practical use of BDE in real-world implementations. Best practices and lessons learned are articulated as well during the session. Topics you will learn about include: Engineering discipline‚Ä®, life cycle, methodology, governance and best practices.

Level : Intermediate