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  • In Person

Modern Data Platforms for BI & Analytics

Type:

  • Workshop

Topic(s):

  • Data Architecture
  • BI & Analytics

The adoption of modern, cloud-based data platforms for business intelligence and analytics is radically transforming the way organizations work with data.

There are a number of features that collectively enable this innovation: provisioning environments at the click of a button; extracting insight from data of different shapes, sizes, and velocities; spinning up and scaling resources on-demand; performing rapid experimentation to test hypotheses; integrating advanced analytics and machine learning workloads into your standard pipelines; the list goes on.

The economies of cloud computing, the plethora of new tools available, and the application of a DevOps mindset open a world of possibilities that can revolutionize the way you operate, and the way serve your customers. And it continues at pace to evolve and mature.

Join us for this free interactive online workshop on November 26th as we explore this world with you and dive into some of the key features of the modern data architecture for business intelligence and analytics delivery.

What we’ll cover:

  • key value drivers for cloud-based business intelligence and analytics
  • the modern data architecture, its typical components, and common design patterns
  • core concepts including data lakes, data warehouses, self-service, data science, and machine learning
  • business and IT agility: from resource provisioning, to experimentation, to the delivery of productionized, integrated advanced analytics workloads
  • use case-based demonstrations of the core components

Is it for you?

  • Would you like to gain a greater understanding of the multitude of opportunities that leveraging cloud-based modern data architectures can open up for you?
  • Are you struggling to handle large volumes of information and extract valuable insights in a timely manner?
  • Are you looking to move beyond standard business intelligence reporting and siloed analytics to rapid experimentation and industrialized advanced analytics workloads?