31:32
Cloud-Native Data: What Is it? Will it Solve the Data-DevOps Divide?
The panelists debate whether data's answer to cloud-native will mirror DevOps or not.
50:00
Next Steps in Stateful Streaming with Apache Flink
Stephan Ewan talks about how Apache Flink is making stateful stream processing even more expressive and flexible to support applications in streaming that were previously not considered streamable.
42:47
Data-Based Coaching Brings Humanness to Agile Teams
Bazil Arden discusses how ‘data-based coaching’ helps to surface and tackle cognitive biases, focus on the wider system and counter political forces, enhancing psychological safety.
49:35
Data Decisions with Real-Time Stream Processing
Serhat Yilmaz talks about how Facebook is using stream processing at scale, the difficulties they have encountered and the solutions they have created to date.
52:47
Managing Data in Microservices
Randy Shoup discusses managing data in microservices and shares proven patterns and practical advice that has been successful at Google, eBay, and Stitch Fix.
01:09:08
Designing Visualizations for Action
Chris Varosy discusses strategies for designing data visualizations and dashboards that bring the insight users need to make decisions.
49:23
Data Consistency in Microservice Using Sagas
Chris Richardson discusses messaging, durability, and reliability in microservice architectures leveraging the Saga Pattern, explaining how sagas work and introduces a saga framework for Java.
28:52
Systems That Learn
Stephen Buckley discusses the Systems That Learn initiative which aims to create systems that learn by combining expertise in Systems and Machine Learning.
47:06
Homoiconicity: It Is What It Is
Stuart Sierra demonstrates the power that comes from having the same data representation at all layers: programming language, specification, database, inter-process communication, and user interface.
45:00
Data-Driven Coaching - Safely Turning Team Data into Coaching Insights
Troy Magennis shows how to expose data to teams in order for them to retrospect productively, determine if a process experiment is panning out as expected, and to explore process change opportunities.
40:26
Machine Learning in Academia and Industry
Deborah Hanus discusses some of the challenges that can arise when working with data.
39:23
AI-Based Data Extraction
George Roth presents the challenges of data extraction from unstructured content in the context of preparing the data for Data Analytics.
