Reid Draper shows how real world distributed database work, communicate and are tested, trading RPC for messaging, unit-tests for QuickCheck, and micro-benchmarks for multi-week stress tests.
James Richardson, Nat Pryce discuss some of the challenges faced using Neo4J for interactive analysis of large data imports (80K nodes, 150k relationships) and how they overcame them.
John Davies shows a Spring work-flow consuming 7.4kB XML messages, binding them to 25kB Java but storing them in just 450 bytes each, 10 million derivative contracts in-memory on a laptop.
Lin Qiao discusses the architecture of Gobblin, LinkedIn’s framework for addressing the need of high quality and high velocity data ingestion.
Simon Marlow explains how to use Haxl to automatically batch and overlap requests for data from multiple data sources.
Eugene Mandel discusses challenges of conforming data sources and compares processing stacks: Hadoop+Redshift vs Spark, showing how the technology drives the way the problem is modeled.
Michael Widenius walks through the features of MariaDB 10.0 and 10.1, outlining the performance benefits resulting from switching to MariaDB.
Peter Harrington explains what you do with machine learning, and what are the building blocks for an application that uses machine learning from collected data to creating predictions for customers.
Matt Stine presents how combine Spring Boot, Spring Data, Spring Reactor, Spring XD, Hadoop and run them in the cloud.
Dean Wampler takes a look at SQL’s resurgence and specific example technologies, including: NewSQL, Hybrid SQL, SQL abstractions on top of file-based data, SQL as a functional programming language.
Mark Madsen explains the history of databases and data processing over the past decades and looks where the industry will go.
Michael Hunger and Lorenzo Speranzoni show how easy it is to get started with Spring Data Neo4j using Spring Boot.