Stars, Flakes, Vaults and the Sins of Denormalisation

2019-10-18T03:01:05+00:00Categories: Data Governance Level 2, Innovation & Tech (CTO) Curriculum Electives, Data Governance Curriculum Electives, Executive Curriculum Electives, Innovation & Tech (CTO) Level 2, Stephen Brobst, Data Engineering Curriculum, Data Management, AI Engineering Curriculum, Executive Level 2, Data Engineering Level 1, AI Engineering Level 1, All Academy Courses|Tags: , , , |

Providing both performance and flexibility are often seen as contradictory goals in designing large scale data implementations. In this talk we will discuss techniques for denormalisation and provide a framework for understanding the performance and flexibility implications of various design options. We will examine a variety of logical and physical design approaches and evaluate the trade offs between them. Specific recommendations are made for guiding the translation from a normalised logical data model to an engineered-for-performance physical data model. The role of dimensional modeling and various physical design approaches are discussed in detail. Best practices in the use of surrogate keys is also discussed. The focus is on understanding the benefit (or not) of various denormalisation approaches commonly taken in analytic database designs.

Overcoming Information Overload with Advanced Practices in Data Visualisation

2019-10-24T04:46:56+00:00Categories: Data Culture Electives, Innovation & Tech (CTO) Curriculum Electives, Data Science Curriculum, Data Science Level 1, Data Culture Level 2, Innovation & Tech (CTO) Level 2, Stephen Brobst, Executive Curriculum, Data Visualisation, Data Management, AI Engineering Curriculum, Executive Level 2, Big Data, AI Engineering Level 1, All Academy Courses|Tags: , , , , , , , |

In this workshop, we explore best practices in deriving insight from vast amounts of data using visualisation techniques. Examples from traditional data as well as an in-depth look at the underlying technologies for visualisation in support of geospatial analytics will be undertaken. We will examine visualisation for both strategic and operational BI.

Agile Data Management Architecture

2019-10-24T04:46:19+00:00Categories: Predictive Analytics & AI, Data Culture Curriculum, Data Science Level 2, Data Science Curriculum Electives, Executive Curriculum Electives, Data Governance Curriculum, Innovation & Tech (CTO) Level 2, Stephen Brobst, Fraud and Security, Data Engineering Curriculum, Innovation & Tech (CTO) Curriculum, Data Governance Level 1, Data Management, AI Engineering Curriculum, Executive Level 2, Big Data, Data Engineering Level 1, AI Engineering Level 1, All Academy Courses|Tags: , , , |

This full-day workshop examines the trends in analytic technologies, methodologies, and use cases. The implications of these developments for deployment of analytic capabilities will be discussed with examples in future architecture and implementation. This workshop also presents best practices for deployment of next generation analytics.

Innovating with Best Practices to Modernise Delivery Architecture and Governance

2019-10-24T04:48:00+00:00Categories: AI Engineering Curriculum Electives, Data Science Level 2, Data Science Curriculum Electives, Executive Curriculum Electives, Data Governance Curriculum, Innovation & Tech (CTO) Level 2, Stephen Brobst, Data Engineering Curriculum, Innovation & Tech (CTO) Curriculum, Data Governance Level 1, Data Management, Executive Level 2, Infrastructure & Technologies, Big Data, Data Engineering Level 1, AI Engineering Level 2, All Academy Courses|Tags: , , , , , |

Organisations often struggle with the conflicting goals of both delivering production reporting with high reliability while at the same time creating new value propositions from their data assets. Gartner has observed that organizations that focus only on mode one (predictable) deployment of analytics in the construction of reliable, stable, and high-performance capabilities will very often lag the marketplace in delivering competitive insights because the domain is moving too fast for traditional SDLC methodologies. Explorative analytics requires a very different model for identifying analytic opportunities, managing teams, and deploying into production. Rapid progress in the areas of machine learning and artificial intelligence exacerbates the need for bi-modal deployment of analytics. In this workshop we will describe best practices in both architecture and governance necessary to modernise an enterprise to enable participation in the digital economy.

Modernising Your Data Warehouse and Analytic Ecosystem

2019-10-24T04:49:10+00:00Categories: Data Governance Level 2, Executive Curriculum Electives, Data Governance Curriculum, Innovation & Tech (CTO) Level 2, Stephen Brobst, Data Engineering Curriculum, Innovation & Tech (CTO) Curriculum, Data Management, AI Engineering Curriculum, Infrastructure & Technologies, Big Data, Data Engineering Level 1, AI Engineering Level 1, All Academy Courses|Tags: , , , , |

This full-day workshop examines the emergence of new trends in data warehouse implementation and the deployment of analytic ecosystems.  We will discuss new platform technologies such as columnar databases, in-memory computing, and cloud-based infrastructure deployment.  We will also examine the concept of a “logical” data warehouse – including and ecosystem of both commercial and open source technologies.  Real-time analytics and in-database analytics will also be covered.  The implications of these developments for deployment of analytic capabilities will be discussed with examples in future architecture and implementation. This workshop also presents best practices for deployment of next generation analytics using AI and machine learning. 

Cost-Based Optimisation: Obtaining the Best Execution Plan for Complex Queries

2019-10-24T04:52:00+00:00Categories: Data Governance Level 2, Predictive Analytics & AI, Innovation & Tech (CTO) Curriculum Electives, Data Science Level 2, Data Science Curriculum Electives, Data Governance Curriculum, Innovation & Tech (CTO) Level 2, Stephen Brobst, Data Engineering Curriculum, Data Engineering Level 2, AI Engineering Curriculum, Big Data, AI Engineering Level 2, All Academy Courses|Tags: , , , |

Optimiser choices in determining the execution plan for complex queries is a dominant factor in the performance delivery for a data foundation environment. The goal of this workshop is to de-mystify the inner workings of cost-based optimisation for complex query workloads. We will discuss the differences between rule-based optimisation and cost-based optimisation with a focus on how a cost-based optimization enumerates and selects among possible execution plans for a complex query. The influences of parallelism and hardware configuration on plan selection will be discussed along with the importance of data demographics. Advanced statistics collection is discussed as the foundational input for decision-making within the cost-based optimiser. Performance characteristics and optimiser selection among different join and indexing opportunities will also be discussed with examples. The inner workings of the query re-write engine will be described along with the performance implications of various re-write strategies.

Optimising Your Big Data Ecosystem

2019-10-24T04:48:58+00:00Categories: Predictive Analytics & AI, Data Science Level 2, Data Science Curriculum Electives, Executive Curriculum Electives, Innovation & Tech (CTO) Level 2, Stephen Brobst, Data Engineering Curriculum, Innovation & Tech (CTO) Curriculum, Data Management, AI Engineering Curriculum, Executive Level 2, Infrastructure & Technologies, Big Data, Data Engineering Level 1, AI Engineering Level 1, All Academy Courses|Tags: , , , , |

Big Data exploitation has the potential to revolutionise the analytic value proposition for organisations that are able to successfully harness these capabilities. However, the architectural components necessary for success in Big Data analytics are different than those used in traditional data warehousing. This workshop will provide a framework for Big Data exploitation along with recommendations for architectural deployment of Big Data solutions.

Social Network Analysis: Practical Use Cases and Implementation

2019-10-24T04:51:00+00:00Categories: Data Governance Level 2, Predictive Analytics & AI, Data Culture Electives, Innovation & Tech (CTO) Curriculum Electives, Data Science Curriculum, Data Governance Curriculum Electives, Executive Curriculum Electives, Marketing, Data Science Level 1, Data Culture Level 2, Innovation & Tech (CTO) Level 2, Stephen Brobst, Fraud and Security, Data Management, AI Engineering Curriculum, Executive Level 2, Big Data, AI Engineering Level 2, All Academy Courses|Tags: , , , |

Social networking via Web 2.0 applications such as LinkedIn and Facebook has created huge interest in understanding the connections between individuals to predict patterns of churn, influencers related to early adoption of new products and services, successful pricing strategies for certain kinds of services, and customer segmentation. We will explain how to use these advanced analytic techniques with mini case studies across a wide range of industries including telecommunications, financial services, health care, retailing, and government agencies.