Architecting a data platform for enterprise use

Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build a multiuse data infrastructure that isn't subject to past constraints. Mark Madsen and Todd Walter explore design assumptions and principles and walk you through a reference architecture to use as you work to unify your analytics infrastructure.

The goal in most organizations is to build multiuse data infrastructure that isn’t 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 you expect 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 you 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 multiuse data infrastructure and walk you through a reference architecture to use as you work to unify your analytics infrastructure.