Marius Bogoevici demonstrates how to create complex data processing pipelines that bridge the big data and enterprise integration together and how to orchestrate them with Spring Cloud Data Flow.
Thomas Risberg discusses developing big data pipelines with Spring, focusing around the code needed and he also covers how to set up a test environment both locally and in the cloud.
Uses of Big Data by a Non-Profit Engaged in Conducting Events Funded in Part by Third Party Sponsors
Thomas Grilk discusses how a non-profit can efficiently use data from customers/athletes in its marketing and sponsorship activities while respecting the privacy and confidentiality of its customers.
Rajat Monga talks about why Google built TensorFlow, an open source software library for numerical computation using data flow graphs, and what were some of the technical challenges in building it.
David Fisher discusses via example how to build a data navigation language into visualizations, providing an intuitive user experience via the mechanism of subtle visual cuing.
Lawrence Chernin describes best practices and validation methods used to deal with large unstructured data, including a suite of unit tests covering the implementations of algorithmic equations.
Beach Clark talks about the technological and cultural challenges of turning data science into a vital part of the business model at Georgia Aquarium.
Alok Aggarwal overviews Artificial Intelligence and discusses a use case, “Voice of Cancer Patients” that uses ML and NLP algorithms to analyze unstructured text written by cancer patients.
The panelists discuss how Data Science can help solve various problems for business.
Jonathan Gray introduces Hydrator, an open source framework and user interface for creating data lakes for building and managing data pipelines on Spark, MapReduce, Spark Streaming and Tigon.
Ali Jalali presents how to develop a machine learning predictive analytics engine for big data analytics.
Sameer Farooqui demos connecting to the live stream of Wikipedia edits, building a dashboard showing what’s happening with Wikipedia datasets and how people are using them in real time.