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.
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.
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 provides a framework for Big Data exploitation along with recommendations for architectural deployment of Big Data solutions.
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.
Real-time analytics is rapidly changing the landscape for deployment of decision support capability. The challenges of supporting extreme service levels in the areas of performance, availability, and data freshness demand new methods for data warehouse construction. Particular attention is paid to architectural topologies for successful implementation and the role of frameworks for Microservices deployment. In this workshop we will discuss evolution of data warehousing technology and new methods for meeting the associated service levels with each stage of evolution.