Episodes
Episode 399: Sumit Kumar on Building Maps using Leaflet
Sumit Kumar, Head of Engineering at SHARE NOW, talks about creating mapping applications in JavaScript using the Leaflet library. Jeremy Jung spoke with Sumit about what leaflet is and when to use it; sources for street map tile layers; using GeoJSON to store markers and shapes; raster vs vector data; working with live sources like […]
Episode 398: Apache Kudu with Adar Lieber-Dembo
Adar Lieber-Dembo from Cloudera discusses Apache Kudu, which is a columnar data storage system for fast analytics and fast ingestion of large datasets. Kudu takes its inspiration from systems in the Hadoop ecosystem, but it addresses many of their shortcomings. SE Radio’s Akshay Manchale spoke with Adar about motivations behind building Kudu, features available for […]
Episode 397: Pat Helland on Data Management with Microservices
Pat Helland, who has been working on building data management systems since 1978, talks to host Akshay Manchale about Data Management at scale with Microservices. Pat talks about trends in storage and computing, state management, immutable data storage, and idempotent computation. He also talks about various ways to look at data — “inside” vs. “outside,” “prescriptive” […]
Episode 396: Barry O’Reilly on Antifragile Architecture
Barry O’Reilly of Black Tulip Technology discusses Antifragile Architecture, an approach for designing systems that actually improve in the face of complexity and disorder. Host Jeff Doolittle spoke with O’Reilly about the characteristics of antifragility and the nature of complexity in software systems and business environments. Various processes and practices were discussed for applying the […]
Episode 395: Katharine Jarmul on Security and Privacy in Machine Learning
Katharine Jarmul of DropoutLabs discusses security and privacy concerns as they relate to Machine Learning. Host Justin Beyer spoke with Jarmul about attacks that can be leveraged against data pipelines and machine learning models; attack types – adversarial example, model inference, deanonymization; and how they can be utilized to manipulate model outcomes; the dangers of […]
