Kubecon ‘24 recap: Patent Trolls, OTel Lessons at Scale, and Principle Platform Abstractions

4 MIN READ
4 MIN READ

Last week I had the pleasure of attending KubeCon 2024 in Salt Lake City, Utah. If you’re not familiar with Kubecon, it’s an open source and Kubernetes-focused conference put on by the Cloud Native Computing Foundation (CNCF). Here are some of my highlights and insights from this year’s conference.

Kubecon 2024 in Salt Lake City, Utah.

Keynote Themes: Happy 10 Years Kubernetes, AI, and Patent Trolls

Kubernetes turned 10 years old this KubeCon so there were talks about the evolution of Kubernetes, to never stop growing, and without community—how it wouldn’t have happened. So Happy Birthday Kubernetes!

However, the keynote highlighted a larger problem emerging: patent trolls. Patent trolls are those who claim patent infringement, in this case, on open source projects. This is a huge hindrance to smaller projects and companies from thriving. The litigation costs are beyond what many companies can afford, forcing them to settle out of court. The CNCF, however, is fighting back at these trolls, working on patent lawsuit deterrence with their cloud native heroes challenge. This program encourages developers to have “prior art” or evidence like documentation to indicate that an invention in the troll’s patent application isn’t “new.” 

Slide from keynote how the opensource community needs to be like musk oxen and band together to find strength in numbers against patent trolls

There was also a lot of talk about artificial intelligence (AI), and how it has transformed from niche use cases to general use. Some of the challenges being faced now are how to keep PII data private when processing through AI, scaling, and how to automate the coding process. 

Session: Lessons Learned Adopting OpenTelemetry at Scale

Heroku undertook the task of adopting OpenTelmetry internally. Alex Arnell, a member of the telemetry team at Heroku, took some of the principles from Dale Carneige’s book How to Win Friends and Influence People in convincing others to move to OTel. The highlight here is make others say yes enthusiastically (Principle #5), let the other person do a great deal of talking (Principle #6), dramatize your ideas with demo days (Principle #11), and throw down a challenge (Principle #12) - in this case a mandated observability vendor swap. 

The lessons learned here are:

  1. Explicit histogram values may be prewritten in the code, which may cause some of the desired visibility to disappear. 
  2. Exponential histograms are useful so they can handle large loads and give the appropriate level of visibility and have that bell curve diagram.
  3. Plan your future with standards and how they will be adopted.
  4. Modularize or codify your dashboards and alerts so they are easy to reuse. 

Session: Evolving Reddit’s Infrastructure via Principle Platform Abstractions

Reddit talked about how their platform evolved, specifically how they hit an inflection point in 2022 with the impending IPO and expansion of serving stacks in multiple regions, making them realize they needed to move to platform abstractions. This was due to growth in advertisements and machine learning. They presented two case studies of what they experienced internally - Kubernetes Namespaces and Legacy Kubernetes Clusters. We’ll focus on Kubernetes Namespaces.

They were finding it would take up to a week or more to build a Kubernetes Namespace due to the tedious process of copy and pasting YAML files, infrastructure complexity, internal reviews, and then hope for no failures, or they would start all over again. It was not fun, pretty, or efficient. With the help of Principled Platform Abstractions, they worked towards a platform engineering trifecta of obviousness, consistency, and predictability for their internal developer platform.

Example code of how Reddit implemented Principle Platform Abstractions

Their “tldr;” at the end was “When companies reach a certain maturity, they need platform abstractions to operate efficiently, especially as they grow.” Why? This is because automation enables admin and technical scale, empowers both application and infrastructure engineers, and Kubernetes can act as a universal control plane. 

Mezmo At Kubecon: Introducing Mezmo Flow

Mezmo had a booth at Kubecon and we got to show off our new mascot and our guided onboarding experience - Mezmo Flow. We’re field testing this new experience and got some feedback at our booth, but will continue to get feedback from users like you. You can try it here

We also showcased our revised version of our O’Reilly report, The Fundamentals of Telemetry Pipelines. This is a great report for gaining a better understanding of telemetry pipelines, and how they can help you with understanding, optimizing, and responding to your data. 

Until Next Time, Kubecon

There were a lot of other things that I did not have time to share and mention in here, but I want to say this again, Happy 10th Birthday Kubernetes. Never stop growing and continue scaling new heights like we did this last week!

Table of Contents

    Share Article

    RSS Feed

    Next blog post
    You're viewing our latest blog post.
    Previous blog post
    You're viewing our oldest blog post.
    What is Active Telemetry
    Launching an agentic SRE for root cause analysis
    Paving the way for a new era: Mezmo's Active Telemetry
    The Answer to SRE Agent Failures: Context Engineering
    Empowering an MCP server with a telemetry pipeline
    The Debugging Bottleneck: A Manual Log-Sifting Expedition
    The Smartest Member of Your Developer Ecosystem: Introducing the Mezmo MCP Server
    Your New AI Assistant for a Smarter Workflow
    The Observability Problem Isn't Data Volume Anymore—It's Context
    Beyond the Pipeline: Data Isn't Oil, It's Power.
    The Platform Engineer's Playbook: Mastering OpenTelemetry & Compliance with Mezmo and Dynatrace
    From Alert to Answer in Seconds: Accelerating Incident Response in Dynatrace
    Taming Your Dynatrace Bill: How to Cut Observability Costs, Not Visibility
    Architecting for Value: A Playbook for Sustainable Observability
    How to Cut Observability Costs with Synthetic Monitoring and Responsive Pipelines
    Unlock Deeper Insights: Introducing GitLab Event Integration with Mezmo
    Introducing the New Mezmo Product Homepage
    The Inconvenient Truth About AI Ethics in Observability
    Observability's Moneyball Moment: How AI Is Changing the Game (Not Ending It)
    Do you Grok It?
    Top Five Reasons Telemetry Pipelines Should Be on Every Engineer’s Radar
    Is It a Cup or a Pot? Helping You Pinpoint the Problem—and Sleep Through the Night
    Smarter Telemetry Pipelines: The Key to Cutting Datadog Costs and Observability Chaos
    Why Datadog Falls Short for Log Management and What to Do Instead
    Telemetry for Modern Apps: Reducing MTTR with Smarter Signals
    Transforming Observability: Simpler, Smarter, and More Affordable Data Control
    Datadog: The Good, The Bad, The Costly
    Mezmo Recognized with 25 G2 Awards for Spring 2025
    Reducing Telemetry Toil with Rapid Pipelining
    Cut Costs, Not Insights:   A Practical Guide to Telemetry Data Optimization
    Webinar Recap: Telemetry Pipeline 101
    Petabyte Scale, Gigabyte Costs: Mezmo’s Evolution from ElasticSearch to Quickwit
    2024 Recap - Highlights of Mezmo’s product enhancements
    My Favorite Observability and DevOps Articles of 2024
    AWS re:Invent ‘24: Generative AI Observability, Platform Engineering, and 99.9995% Availability
    From Gartner IOCS 2024 Conference: AI, Observability Data, and Telemetry Pipelines
    Our team’s learnings from Kubecon: Use Exemplars, Configuring OTel, and OTTL cookbook
    How Mezmo Uses a Telemetry Pipeline to Handle Metrics, Part II
    Webinar Recap: 2024 DORA Report: Accelerate State of DevOps
    Kubecon ‘24 recap: Patent Trolls, OTel Lessons at Scale, and Principle Platform Abstractions
    Announcing Mezmo Flow: Build a Telemetry Pipeline in 15 minutes
    Key Takeaways from the 2024 DORA Report
    Webinar Recap | Telemetry Data Management: Tales from the Trenches
    What are SLOs/SLIs/SLAs?
    Webinar Recap | Next Gen Log Management: Maximize Log Value with Telemetry Pipelines
    Creating In-Stream Alerts for Telemetry Data
    Creating Re-Usable Components for Telemetry Pipelines
    Optimizing Data for Service Management Objective Monitoring
    More Value From Your Logs: Next Generation Log Management from Mezmo
    A Day in the Life of a Mezmo SRE
    Webinar Recap: Applying a Data Engineering Approach to Telemetry Data
    Dogfooding at Mezmo: How we used telemetry pipeline to reduce data volume
    Unlocking Business Insights with Telemetry Pipelines
    Why Your Telemetry (Observability) Pipelines Need to be Responsive
    How Data Profiling Can Reduce Burnout
    Data Optimization Technique: Route Data to Specialized Processing Chains
    Data Privacy Takeaways from Gartner Security & Risk Summit
    Mastering Telemetry Pipelines: Driving Compliance and Data Optimization
    A Recap of Gartner Security and Risk Summit: GenAI, Augmented Cybersecurity, Burnout
    Why Telemetry Pipelines Should Be A Part Of Your Compliance Strategy
    Pipeline Module: Event to Metric
    Telemetry Data Compliance Module
    OpenTelemetry: The Key To Unified Telemetry Data
    Data optimization technique: convert events to metrics
    What’s New With Mezmo: In-stream Alerting
    How Mezmo Used Telemetry Pipeline to Handle Metrics
    Webinar Recap: Mastering Telemetry Pipelines - A DevOps Lifecycle Approach to Data Management
    Open-source Telemetry Pipelines: An Overview
    SRECon Recap: Product Reliability, Burn Out, and more
    Webinar Recap: How to Manage Telemetry Data with Confidence
    Webinar Recap: Myths and Realities in Telemetry Data Handling
    Using Vector to Build a Telemetry Pipeline Solution
    Managing Telemetry Data Overflow in Kubernetes with Resource Quotas and Limits
    How To Optimize Telemetry Pipelines For Better Observability and Security
    Gartner IOCS Conference Recap: Monitoring and Observing Environments with Telemetry Pipelines
    AWS re:Invent 2023 highlights: Observability at Stripe, Capital One, and McDonald’s
    Webinar Recap: Best Practices for Observability Pipelines
    Introducing Responsive Pipelines from Mezmo
    My First KubeCon - Tales of the K8’s community, DE&I, sustainability, and OTel
    Modernize Telemetry Pipeline Management with Mezmo Pipeline as Code
    How To Profile and Optimize Telemetry Data: A Deep Dive
    Kubernetes Telemetry Data Optimization in Five Steps with Mezmo
    Introducing Mezmo Edge: A Secure Approach To Telemetry Data
    Understand Kubernetes Telemetry Data Immediately With Mezmo’s Welcome Pipeline
    Unearthing Gold: Deriving Metrics from Logs with Mezmo Telemetry Pipeline
    Webinar Recap: The Single Pane of Glass Myth
    Empower Observability Engineers: Enhance Engineering With Mezmo
    Webinar Recap: How to Get More Out of Your Log Data
    Unraveling the Log Data Explosion: New Market Research Shows Trends and Challenges
    Webinar Recap: Unlocking the Full Value of Telemetry Data
    Data-Driven Decision Making: Leveraging Metrics and Logs-to-Metrics Processors
    How To Configure The Mezmo Telemetry Pipeline
    Supercharge Elasticsearch Observability With Telemetry Pipelines
    Enhancing Grafana Observability With Telemetry Pipelines
    Optimizing Your Splunk Experience with Telemetry Pipelines
    Webinar Recap: Unlocking Business Performance with Telemetry Data
    Enhancing Datadog Observability with Telemetry Pipelines
    Transforming Your Data With Telemetry Pipelines
    6 Steps to Implementing a Telemetry Pipeline
    Webinar Recap: Taming Data Complexity at Scale