Dan Luu discusses how to estimate performance using back of the envelope calculations that can be done in minutes or hours, even for applications that take months or years to implement.
Angie Terrell discusses the current state of conversational interfaces and human-centered design principles to guide the design of conversational apps.
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.
Elliot Chow discusses the data pipeline that they built with Kafka, Spark Streaming, and Cassandra to process Netflix user activities in real time for the Trending Now row.
Lynn Langit and Martin Thompson explore the individual practices and techniques that can help bring out the engineer in us.
Katharine Jarmul discusses implementation decisions for those looking for a practical recommendation on the "what" and "how" of data automation workflows.
Eric Horesnyi and Albert Bifet discuss how hedge funds have moved beyond High Frequency Trading using AI and real-time data processing.
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.
Cliff Click talks about SCORE, a solution for doing Trade Surveillance using H2O, Machine Learning, and a whole lot of domain expertise and data munging.
Alexandre Freire discusses what his team has learned through experiments, from the very fundamentals of what a team needs to be successful to Modern Agile engineering techniques.
Danny Yuan discusses how Uber builds its next generation of stream processing system to support real-time analytics as well as complex event processing.
Akshat Vig and Khawaja Shams discuss DynamoDB Streams and what it takes to build an ordered, highly available, durable, performant, and scalable replicated log stream.