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 (Third Nature)
9:0012:30 Tuesday, 22 May 2018
Data engineering and architecture
Location: Capital Suite 14 Level: Intermediate

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

Description

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 explores design assumptions and principles to apply when building multi-use data infrastructure and walks 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

Third Nature

Mark Madsen is a research analyst at Third Nature, where he advises companies on data strategy and technology planning. Mark has designed analysis, data collection, and data management infrastructure for companies worldwide. He focuses on two types of work: the business applications of data and guiding the construction of data infrastructure. As a result, Mark does as much information strategy and IT architecture work as he does analytics.

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