Doug Daniels discusses the cloud-based platform they have built at DataDog and how it differs from a traditional datacenter-based analytics stack, pros and cons and the tooling built.
Tom Gianos and Dan Weeks discuss Netflix' overall big data platform architecture, focusing on Storage and Orchestration, and how they use Parquet on AWS S3 as their data warehouse storage layer.
Mike Olson presents several use cases where big data is collected and analyzed to gather insights from the automotive, insurance, financial, and other sectors.
Scott Clark introduces Bayesian Global Optimization as an efficient way to optimize ML model parameters, explaining the underlying techniques and comparing it to other standard methods.
Debraj GuhaThakurta discusses ML and data analysis processes in Spark using examples written in Python and R.
Oleg Zhurakousky discusses the Hadoop ecosystem – Hadoop, HDFS, Yarn-, and how projects such as Hive, Atlas, NiFi interact and integrate to support the variety of data used for analytics.
Victor Gamov and Neil Stevenson present using Spring Data for a Hazelcast project, built on the KeyValue module and providing infrastructure components for creating repository abstractions.
Chun-Ho Hung and Nikhil Garg discuss Quanta, Quora's counting system powering their high-volume near-real-time analytics, describing the architecture, design goals, constraints, and choices made.
Gil Tene presents the current state of Java SE and OpenJDK, the role of Java in the Big Data and Infrastructure components, JCP, the ecosystem, trends, etc.
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.