Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Architecting a data platform for enterprise use

Mark Madsen (Think Big Analytics), Todd Walter (Teradata)
9:0012:30 Tuesday, 22 May 2018
Data engineering and architecture
Location: Capital Suite 14 Level: Intermediate
Secondary topics:  Data Platforms

Who is this presentation for?

  • Architects, system designers, and managers who would like to understand the broader picture of analytics infrastructure

Prerequisite knowledge

  • A basic understanding of how data is used in organizations, application and platform architecture, and the concepts behind databases and other data platforms

What you'll learn

  • Understand how data architecture influences your technology architecture
  • Explore analytic workloads and their impact on data architecture and technology choices
  • Learn how to separate infrastructure from application in your data systems and when to save immutable data, when to standardize data, and how to govern its delivery and use over time
  • Discover the trade-offs and considerations for multi-application and multi-use data infrastructure


The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints, but the focus in our market has been on acquiring technology, ignoring the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform, the architecture should be designed rather than accidental.

Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. But what are the design principles that lead to good design and a functional data architecture, and what are the assumptions that limit older approaches? How can one integrate with, migrate from, or modernize an existing data environment? How will this affect an organization’s data management practices?

Mark Madsen and Todd Walter explore design assumptions and principles to apply when building multi-use data infrastructure and walk you through a reference architecture to use as you work to unify your analytics infrastructure.

Topics include:

  • A brief history of data infrastructure and past design assumptions
  • Categories of data and data use in organizations
  • Analytic workload characteristics and constraints
  • Data architecture
  • Functional architecture
  • Trade-offs between different classes of technology
  • Technology planning assumptions and guidance
Photo of Mark Madsen

Mark Madsen

Think Big Analytics

Mark Madsen is the global head of architecture at Think Big Analytics where he is responsible for understanding, forecasting, and defining the analytics landscape and architecture. Prior to this he was CEO of Third Nature, where he advised companies on data strategy and technology planning, and vendors on product management. Mark has designed analysis, data collection, and data management infrastructure for companies worldwide.

Photo of Todd Walter

Todd Walter


Clients look to Todd Walter for guidance on their analytic architectures. As a pragmatic visionary, he helps business leaders, analysts and technologists better understand all of the astonishing possibilities of big data and analytics in view of emerging and existing capabilities of information infrastructures. Walter is a sought-after speaker and educator on analytics strategy, big data architecture and exposing the virtually limitless business opportunities that can be realized by architecting with the most advanced analytic intelligence platforms and solutions. Walter has been with Teradata for more than 30 years, holds more than a dozen patents, is Chief Technologist and a Teradata Fellow.

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