<description>&lt;b&gt;Most observability platforms are built around siloed signals, metrics, logs, and traces; not the rich context engineers actually need. The architecture of observability tooling hasn’t kept pace with the architecture of today’s distributed systems. &lt;/b&gt;&lt;br /&gt;&lt;b&gt;&lt;/b&gt;&lt;br /&gt;&lt;b&gt;In this episode, David sits down with Charity Majors, co-founder and CTO of Honeycomb.io and co-author of Observability Engineering, to rethink how modern teams understand production systems. Charity argues that the “three pillars” model is outdated, making the case for wide, structured events that preserve context and embrace high-cardinality data so engineers can ask better questions in real time.&lt;/b&gt;&lt;br /&gt;&lt;b&gt;&lt;/b&gt;&lt;br /&gt;&lt;b&gt;Drawing on lessons from operating Parse  and its challenging acquisition by Facebook, Charity explains how those experiences shaped Honeycomb’s fast, custom-built observability engine. The two also  explore how AI agents could soon close the loop between code changes and production validation, and Charity previews the upcoming second edition of Observability Engineering, including a new focus on observability governance for CTOs. &lt;/b&gt;&lt;br /&gt;&lt;b&gt;&lt;/b&gt;&lt;br /&gt;&lt;b&gt;Links for Charity:&lt;/b&gt;&lt;br /&gt;&lt;b&gt;&lt;a href="http://charitydotwtf.substack.com" target="_blank" rel="noreferrer noopener"&gt;charitydotwtf.substack.com&lt;/a&gt;,&lt;/b&gt;&lt;br /&gt;&lt;b&gt;&lt;a href="http://honeycomb.io/blog" target="_blank" rel="noreferrer noopener"&gt;honeycomb.io/blog&lt;/a&gt;&lt;a href="https://honeycomb.io/blog," target="_blank" rel="noreferrer noopener"&gt;,&lt;/a&gt; &lt;/b&gt;&lt;br /&gt;&lt;b&gt;&lt;a href="http://x.com/mipsytipsy" target="_blank" rel="noreferrer noopener"&gt;x.com/mipsytipsy&lt;/a&gt;&lt;/b&gt;</description>

Most observability platforms are built around siloed signals, metrics, logs, and traces; not the rich context engineers actually need. The architecture of observability tooling hasn’t kept pace with the architecture of today’s distributed systems. ...

Most observability platforms are built around siloed signals, metrics, logs, and traces; not the rich context engineers actually need. The architecture of observability tooling hasn’t kept pace with the architecture of today’s distributed systems.  In this episode, David sits down with Charity Majors, co-founder and CTO of Honeycomb.io and co-author of Observability Engineering, to rethink how modern teams understand production systems. Charity argues that the “three pillars” model is outdated, making the case for wide, structured events that preserve context and embrace high-cardinality data so engineers can ask better questions in real time. Drawing on lessons from operating Parse  and its challenging acquisition by Facebook, Charity explains how those experiences shaped Honeycomb’s fast, custom-built observability engine. The two also  explore how AI agents could soon close the loop between code changes and production validation, and Charity previews the upcoming second edition of Observability Engineering, including a new focus on observability governance for CTOs. Links for Charity: <a href="http://charitydotwtf.substack.com" target="_blank" rel="noreferrer noopener">charitydotwtf.substack.com</a>, <a href="http://honeycomb.io/blog" target="_blank" rel="noreferrer noopener">honeycomb.io/blog</a><a href="https://honeycomb.io/blog," target="_blank" rel="noreferrer noopener">,</a>  <a href="http://x.com/mipsytipsy" target="_blank" rel="noreferrer noopener">x.com/mipsytipsy</a>

Breaking the Pillars: Rethinking Observability with Charity Majors

<description>Most CI/CD pipelines are built to detect failure, not to resolve it. As software systems grow more distributed and complex, that limitation is becoming a bottleneck for resilience.&lt;br /&gt;&lt;br /&gt;In this episode, David sits down with Tomer Karin, a seasoned software architect in the automotive industry, to explore a new paradigm he calls Continuous Integration and Continuous Solution (CI/CS). Tomer argues that the future  of automated software development isn’t just faster feedback loops, it's systems that can autonomously remediate failures using AI.&lt;br /&gt;&lt;br /&gt;Tomer shares how his years of working on large-scale automotive software revealed the inefficiencies of traditional development pipelines, where engineers spend significant time diagnosing and fixing issues instead of building new capabilities. With CI/CS, AI agents don’t just identify failing builds, they apply fixes that are verified through existing test pipelines, allowing teams to start the next day with a healthier codebase.&lt;br /&gt;&lt;br /&gt;Join us as we discuss:&lt;ul&gt;&lt;li&gt;Why traditional CI/CD breaks down as systems scale and complexity increases &lt;/li&gt;&lt;li&gt;What Continuous Integration and Continuous Solution looks like in practice&lt;/li&gt;&lt;li&gt;How AI agents can safely diagnose and fix software failures&lt;/li&gt;&lt;li&gt;Where human oversight fits into autonomous development pipelines&lt;/li&gt;&lt;li&gt;The economic and productivity impact of autonomous software workflows&lt;/li&gt;&lt;/ul&gt;</description>

Most CI/CD pipelines are built to detect failure, not to resolve it. As software systems grow more distributed and complex, that limitation is becoming a bottleneck for resilience.

In this episode, David sits down with Tomer Karin, a seasoned...

Most CI/CD pipelines are built to detect failure, not to resolve it. As software systems grow more distributed and complex, that limitation is becoming a bottleneck for resilience. In this episode, David sits down with Tomer Karin, a seasoned software architect in the automotive industry, to explore a new paradigm he calls Continuous Integration and Continuous Solution (CI/CS). Tomer argues that the future  of automated software development isn’t just faster feedback loops, it's systems that can autonomously remediate failures using AI. Tomer shares how his years of working on large-scale automotive software revealed the inefficiencies of traditional development pipelines, where engineers spend significant time diagnosing and fixing issues instead of building new capabilities. With CI/CS, AI agents don’t just identify failing builds, they apply fixes that are verified through existing test pipelines, allowing teams to start the next day with a healthier codebase. Join us as we discuss:<ul><li>Why traditional CI/CD breaks down as systems scale and complexity increases </li><li>What Continuous Integration and Continuous Solution looks like in practice</li><li>How AI agents can safely diagnose and fix software failures</li><li>Where human oversight fits into autonomous development pipelines</li><li>The economic and productivity impact of autonomous software workflows</li></ul>

How to Transform Dev Workflows with CI/CS and AI Agents with Tomer Karin

<description>Most databases are designed for success cases. Real systems fail– the difference is whether they’re built and tested for it. &lt;br /&gt;&lt;br /&gt;In this episode, David sits down with Spencer Kimball, co-founder and CEO of Cockroach Labs, to explore the architectural and testing decisions behind CockroachDB, and why validating systems under worst-case conditions is essential to building reliable infrastructure at scale. Spencer shares the origin story of CockroachDB, tracing it back to his time at Google, where working on large-scale data systems and helping build Google Spanner exposed the limitations of existing database technologies. Motivated by the limits of existing database designs, Spencer and his co-founders left Google to build a resilient, scalable, open-source database designed for a world where failure isn’t an edge case, it’s the norm.&lt;br /&gt;&lt;br /&gt;Plus, Spencer and David discuss how emerging AI workloads are reshaping expectations for database infrastructure by increasing scale, stressing latency budgets, and raising the cost of failure.&lt;br /&gt;&lt;br /&gt;Join us as we discuss:&lt;br /&gt;&lt;ul&gt;&lt;li&gt;How Google Spanner inspired the creation of CockroachDB&lt;/li&gt;&lt;li&gt;Designing a database that assumed failure by default &lt;/li&gt;&lt;li&gt;What resilience means at massive scale&lt;/li&gt;&lt;li&gt;How AI is changing the demands placed on modern databases&lt;/li&gt;&lt;li&gt;How the Cockroach Labs–IBM partnership reflects a broader shift in enterprise modernization &lt;/li&gt;&lt;li&gt;Spencer’s futuristic vision for databases, from AI automation to outer space&lt;/li&gt;&lt;/ul&gt;</description>

Most databases are designed for success cases. Real systems fail– the difference is whether they’re built and tested for it. 

In this episode, David sits down with Spencer Kimball, co-founder and CEO of Cockroach Labs, to explore the architectural...

Most databases are designed for success cases. Real systems fail– the difference is whether they’re built and tested for it.  In this episode, David sits down with Spencer Kimball, co-founder and CEO of Cockroach Labs, to explore the architectural and testing decisions behind CockroachDB, and why validating systems under worst-case conditions is essential to building reliable infrastructure at scale. Spencer shares the origin story of CockroachDB, tracing it back to his time at Google, where working on large-scale data systems and helping build Google Spanner exposed the limitations of existing database technologies. Motivated by the limits of existing database designs, Spencer and his co-founders left Google to build a resilient, scalable, open-source database designed for a world where failure isn’t an edge case, it’s the norm. Plus, Spencer and David discuss how emerging AI workloads are reshaping expectations for database infrastructure by increasing scale, stressing latency budgets, and raising the cost of failure. Join us as we discuss: <ul><li>How Google Spanner inspired the creation of CockroachDB</li><li>Designing a database that assumed failure by default </li><li>What resilience means at massive scale</li><li>How AI is changing the demands placed on modern databases</li><li>How the Cockroach Labs–IBM partnership reflects a broader shift in enterprise modernization </li><li>Spencer’s futuristic vision for databases, from AI automation to outer space</li></ul>

AI, Market Cycles, and the Systems Built to Outlast Them with Cockroach Labs CEO & Co-founder Spencer Kimball

<description>In this episode, David sits down with &lt;a href="https://www.linkedin.com/in/ramannishant/" target="_blank" rel="noreferrer noopener"&gt;Nishant Raman&lt;/a&gt;, a seasoned data infrastructure expert, to explore the evolving world of data engineering, AI integration, and building scalable systems from the ground up. With experience across logistics, healthcare, and fintech, Nishant shares hard-won insights from years of building resilient, cost-conscious infrastructure that scales with company growth and technical complexity.&lt;br /&gt;&lt;br /&gt;From choosing the right database to navigating the post-LLM explosion in tooling, this conversation offers a rare behind-the-scenes look at what it takes to thrive as the first data hire and beyond.&lt;br /&gt;&lt;br /&gt;Join us as we discuss:&lt;br /&gt;&lt;ul&gt;&lt;li&gt;Nishant’s accidental path into data engineering and lessons from startup life&lt;/li&gt;&lt;li&gt;The tradeoffs that can come with building scalable, cost-efficient data systems&lt;/li&gt;&lt;li&gt;The growing role of AI in data infrastructure and what’s coming next for data teams&lt;/li&gt;&lt;/ul&gt;</description>

In this episode, David sits down with https://www.linkedin.com/in/ramannishant/, a seasoned data infrastructure expert, to explore the evolving world of data engineering, AI integration, and building scalable systems from the ground up. With...

In this episode, David sits down with <a href="https://www.linkedin.com/in/ramannishant/" target="_blank" rel="noreferrer noopener">Nishant Raman</a>, a seasoned data infrastructure expert, to explore the evolving world of data engineering, AI integration, and building scalable systems from the ground up. With experience across logistics, healthcare, and fintech, Nishant shares hard-won insights from years of building resilient, cost-conscious infrastructure that scales with company growth and technical complexity. From choosing the right database to navigating the post-LLM explosion in tooling, this conversation offers a rare behind-the-scenes look at what it takes to thrive as the first data hire and beyond. Join us as we discuss: <ul><li>Nishant’s accidental path into data engineering and lessons from startup life</li><li>The tradeoffs that can come with building scalable, cost-efficient data systems</li><li>The growing role of AI in data infrastructure and what’s coming next for data teams</li></ul>

How to Scale Data Infrastructure from Startup to Enterprise

<description>In this episode, David sits down with &lt;a href="https://www.linkedin.com/in/mhoshi/" target="_blank" rel="noreferrer noopener"&gt;Masaru Hoshi&lt;/a&gt;, a cloud architect at Qlik, to explore his unexpected journey into cloud architecture, the cultural influences that shaped his career, and the evolving disciplines transforming enterprise technology today.  Join as we discuss:&lt;br /&gt;&lt;ul&gt;&lt;li&gt;Masaru’s path from developer to cloud architect and his open-source contributions&lt;br /&gt;&lt;br /&gt;&lt;/li&gt;&lt;li&gt;The rising importance of site reliability engineering (SRE) and finops in modern enterprises&lt;br /&gt;&lt;br /&gt;&lt;/li&gt;&lt;li&gt;The role of AI in reshaping SRE and predictive reliability management&lt;/li&gt;&lt;/ul&gt;&lt;ul&gt;&lt;li&gt;What songs should go in a playlist for a Site Reliability Engineer&lt;/li&gt;&lt;/ul&gt;</description>

In this episode, David sits down with https://www.linkedin.com/in/mhoshi/, a cloud architect at Qlik, to explore his unexpected journey into cloud architecture, the cultural influences that shaped his career, and the evolving disciplines transforming...

In this episode, David sits down with <a href="https://www.linkedin.com/in/mhoshi/" target="_blank" rel="noreferrer noopener">Masaru Hoshi</a>, a cloud architect at Qlik, to explore his unexpected journey into cloud architecture, the cultural influences that shaped his career, and the evolving disciplines transforming enterprise technology today.  Join as we discuss: <ul><li>Masaru’s path from developer to cloud architect and his open-source contributions </li><li>The rising importance of site reliability engineering (SRE) and finops in modern enterprises </li><li>The role of AI in reshaping SRE and predictive reliability management</li></ul><ul><li>What songs should go in a playlist for a Site Reliability Engineer</li></ul>

Code, Cloud and Karate: The Unlikely Path to Cloud Architecture

Cockroach Lab’s Big Ideas in App Architecture is a podcast for architects and engineers building modern data-intensive applications and systems. In every weekly episode, an innovator joins the show to share useful insights from their experiences building reliable, scalable, maintainable systems. Welcome to Big Ideas in App Architecture!

Cockroach Lab’s Big Ideas in App Architecture is a podcast for architects and engineers building modern data-intensive applications and systems. In every weekly episode, an innovator joins the show to share useful insights from their experiences...

Big Ideas in App Architecture

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