Most existing big data systems prefer sequential scans for processing queries. Igor Canadi and Dhruba Borthakur challenge this view, offering an overview of converged indexing: a single system called ROCKSET that builds inverted, columnar, and document indices. Converged indexing is economically feasible due to the elasticity of cloud-resources and write optimized storage engines.

Jaipaul Agonus and Daniel Monteiro do Carmo Rosa detail big data analytics and visualization practices and tools used by FINRA to support machine learning and other surveillance activities that the Market Regulation Department conducts in the AWS cloud.

Yuan Zhou, Haodong Tang, and Jian Zhang offer an overview of Spark-PMOF and explain how it improves Spark analytics performance.

Concerns are constantly being raised today about what data is appropriate to collect and how (or if) it should be analyzed. There are many ethical, privacy, and legal issues to consider, and no clear standards exist in many cases as to what is fair and what is foul. Bill Franks explores a variety of dilemmas and provides some guidance on how to approach them.

User-based real-time recommendation systems have become an important topic in ecommerce. Lu Wang, Nicole Kong, Guoqiong Song, and Maneesha Bhalla demonstrate how to build deep learning algorithms using Analytics Zoo with BigDL on Apache Spark and create an end-to-end system to serve real-time product recommendations.

As the popularity and utilization of Apache Impala deployments increases, clusters often become victims of their own success when demand for resources exceeds the supply. Tim Armstrong dives into the latest resource management features in Impala to maintain high cluster availability and optimal performance and provides examples of how to configure them in your Impala deployment.

Matt Chapman leads a walkthrough of the architecture and open source components that serve Tribune Publishing's content recommendation system, powered by online machine learning at scale. Find out how multiple publications, multiple recommendation algorithms, and one scalable architecture regularly achieve double the performance of the legacy solution.

Function as a Service is the popular solution of Serverless, which runs a short-lived function. In contrast, we will show a new Serverless: run long-lived function to deal with stream data. Our propos …

The Hadoop and FOSS revolution has reshaped the data analytics landscape. In the search for creating a cutting-edge data platform at ING, we are faced with challenging new requirements such as cloud-r …

Lev Radomislensky will talk about a Kubernetes-based edge solution for retail analytics based on spinning Kubernetes clusters at the edge. The solution relies on a combination of an MQTT broker such a …

In todays world, data is at the core of every business decision made. As data grows, companies have started implementing their own Data Lakes to store and run analytics on the data. Ceph is widely us …

This session will provide a deep-dive into the Jaeger project and the power it provides for distributed tracing data. Some quick background information will lay the foundation to discuss advanced topi …