Presented By O'Reilly and Cloudera
December 5-6, 2016: Training
December 6–8, 2016: Tutorials & Conference

Organizing the data lake

Mark Madsen (Teradata)
5:05pm–5:45pm Thursday, December 8, 2016
Production-ready Hadoop
Location: Summit 2 Level: Beginner
Average rating: ****.
(4.60, 5 ratings)

Prerequisite Knowledge

  • A basic understanding of databases (relational and nonrelational), system architectures, and how data is used in an organization

What you'll learn

  • Learn a reference architecture to help understand when to save immutable data, when to standardize it, and how to manage its delivery and use over time


Building a data lake involves more than installing and using Hadoop. The focus in the market has been on all the different technology components, ignoring the more important part: the data architecture that the code implements, which lies at the core of the system.

Just like a data warehouse, a data lake has a data architecture. If you expect any longevity from the platform, the architecture should be designed rather than accidental.

But what are the design principles that lead to good functional design and a workable data architecture? What are the assumptions that limit old approaches? How can one integrate with or migrate from the older environments? How does this affect an organization’s data management? Answering these questions is key to building long-term infrastructure.

The goal in most organizations is to build multiuse data infrastructure that is not subject to past constraints. Mark Madsen discusses hidden design assumptions, reviews design principles to apply when building multiuse data infrastructure, and provides a reference architecture. This reference architecture has been used across many organizations to work toward a unified analytic infrastructure.

Photo of Mark Madsen

Mark Madsen


Mark Madsen is a Fellow at Teradata, where he’s responsible for understanding, forecasting, and defining analytics ecosystems and architectures. Previously, 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, machine learning, data collection, and data management infrastructure for companies worldwide.