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About this Podcast
Intended for architects and senior developers.
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Ben Sigelman, Co-Creator of Dapper & OpenTracing API, on Observability
Ben Sigelman is the CEO of Lightstep and the author of the Dapper paper that spawned distributed tracing discussions in the software industry. Sigelman discusses with Wes Reisz observability, and his thoughts on logging, metrics, and tracing. The two discuss detection and refinement as the real problem when it comes to diagnosing and troubleshooting incidents with data.
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Pam Selle on Serverless Observability
On this podcast, Pam Selle (an engineer for IOPipe who builds tooling for serverless observability) talks about the case for serverless and the challenges for developing observability solutions. Some of the things discussed on the podcast include tips for creating boundaries between serverless and non-serverless resources and how to think of distributed tracing in serverless environments.
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Charity Majors on Honeycomb.io, the Social Side of Debugging and Testing in Production
In this podcast, recorded live at Strange Loop 2017, Wesley Reisz talks to Charity Majors, cofounder and CEO of honeycomb.io. They discuss the social side of debugging and her Strangeloop talk “Observability for Emerging Infra: What Got You Here Won't Get You There”. Other topics include advice for testing in production, shadowing and splitting traffic, and sampling and aggregation.
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Twitter's Yao Yue on Latency, Performance Monitoring, & Caching at Scale
Yao Yue spent the majority of her career working on caching systems at Twitter. She created a performance team that deals with edge performance outliers often exposed by the enormous scale of Twitter. In this podcast, she discusses standing up the performance team, thoughts on instrumenting applications, and interesting performance issues (and strategies for solving them) they’ve seen at Twitter.
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Martin Hadley on R and the Modern R Ecosystem
In this podcast Werner Schuster talks to Martin Hadley, data scientist at University of Oxford. They discuss the state of the R language, the rich R ecosystem that covers development (RStudio), notebooks for publication (R Notebooks, RPubs), writing web apps (Shiny), and the pros/cons of the different data frames implementations.
