Casey Stella presents a utility written with Apache Spark to automate data preparation, discovering missing values, values with skewed distributions and discovering likely errors within data.
Eugene Kirpichov describes the theory and practice behind Cloud Dataflow's approach to straggler elimination, and the associated non-obvious challenges, benefits, and implications of the technique.
Chinmay Soman and Yi Pan discuss how Uber and LinkedIn use Apache Samza, Calcite and Pinot along with the analytics platform AthenaX to transform data to make it available for querying in minutes.
Katharine Jarmul discusses implementation decisions for those looking for a practical recommendation on the "what" and "how" of data automation workflows.
Marco Bonzanini discusses the process of building data pipelines and all the steps necessary to prepare data, focusing on data plumbing and going from prototype to production.
Stefan Krawczyk discusses how StitchFix used the cloud to enable over 80 data scientists to be productive and have easy access, covering prototyping, algorithms used, keeping schema in sync, etc.
Mārtiņš Kalvāns and Matti Pehrs overview the Data Infrastructure at Spotify, diving into some of the data infrastructure components, such us Event Delivery, Datamon and Styx.
Mark Pollack introduces Spring Cloud Data Flow enabling one to create pipelines for data ingestion, real-time analytics and data import/export, demoing apps that are deployed onto multiple runtimes.
Randy Krum explains how to use the power of data visualization to convey actionable insights to an audience, making data clear and memorable by showing the audience what the data means.
Pat Patterson and Ted Malaska talk about current and emerging data processing technologies, and the various ways of achieving "at least once" and "exactly once" timely data processing.
Michael Wise discusses the journey from having data integrated across an organization, to employing data science to make good use of it.
S Aerni, S Ramanujam and J Vawdrey present approaches and open source tools for wrangling and modeling massive datasets, scaling Java applications for NLP on MPP through PL/Java and much more.