3rd Wave Observability: Open or Bust
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This document discusses the evolution of observability from network/device monitoring (Wave 1) to application performance monitoring (Wave 2) to the current focus on observability (Wave 3). It notes that observability moves past proprietary data formats and interdependence of early solutions by enabling instrumentation once and observation anywhere. The document advocates for teams to own end-to-end observability of their services and dependencies, and notes that observability data can help teams understand where to invest engineering time to most directly impact customers and business goals.
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An Overview of the Emerging Graph Landscape (Oct 2013)
An Overview of the Emerging Graph Landscape (Oct 2013)
Complex Carrier Network Performance Data on Vertica Yields Performance and Cu...
Complex Carrier Network Performance Data on Vertica Yields Performance and Cu...
Keeping Up with the ELK Stack: Elasticsearch, Kibana, Beats, and Logstash
Keeping Up with the ELK Stack: Elasticsearch, Kibana, Beats, and Logstash
2016 - 10 questions you should answer before building a new microservice
2016 - 10 questions you should answer before building a new microservice
Splunk Enterprise for InfoSec Hands-On Breakout Session
Splunk Enterprise for InfoSec Hands-On Breakout Session
2019 Performance Monitoring and Management Trends and Insights
2019 Performance Monitoring and Management Trends and Insights
Runbook Automation: Old News or a Key to Unlock Performance? [DOES2020]
Runbook Automation: Old News or a Key to Unlock Performance? [DOES2020]
Big Data Analytics - Best of the Worst : Anti-patterns & Antidotes
Big Data Analytics - Best of the Worst : Anti-patterns & Antidotes
El camino hacia el éxito con las bases de datos de grafos, la ciencia de dato...
El camino hacia el éxito con las bases de datos de grafos, la ciencia de dato...
Más de Paige Cruz
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)
Because observability is such a broad topic – and often something we learn on the job – it can feel like there’s too much to learn at once. But you don’t have to tackle everything and can start with the basics and build from there!
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
No matter what tooling is in place, there are still observability fundamentals that developers should know. That’s why I’ve put together a primer on the different telemetry types, when to use them, how to understand the data journey, and what to look for in time series graphs.
Power Up with Podman - Kubernetes Community Day LA
Curious about containers beyond Docker? There’s a new generation of containers on the scene, Podman! Supporting secure, rootless containers for Kubernetes microservices, it was designed and built with the cloud in mind. Benefitting from the lessons learned out in the open from Docker, this next generation of containers will quickly become a trusted daily driver in your dev workflow.
Covering what you need to know as an end-user from the UI to the backend, sharing a real world use case leveraging Podman for open source observability workshops https://o11y-workshops.gitlab.io. Paige will share how Podman and the adorable seal mascots Caitlín, Maighréad and Róisín have transformed her local development!
99.99% of Your Traces Are (Probably) Trash (SRECon NA 2024).pdf
Distributed tracing is still finding its footing in many organizations today, despite being an industry concept for 14 years! One challenge to overcome is managing the data volume. Keeping 100% of traces is expensive and unnecessary - enter sampling. From head-based, tail-based or mixing and matching there is a buffet of configuration options available with OpenTelemetry. Let’s review the tradeoffs associated with different sampling configurations and their impact to the cost and value of your tracing data.
OTel Orientation: How to Train Teams (OTel in Practice)
This document provides guidance on training teams in observability using OpenTelemetry. It discusses identifying learning gaps and developing training workshops. A case study example is provided of an "OpenTelemetry 101: Let's Instrument!" workshop that teaches developers how to instrument applications with OpenTelemetry. The workshop uses a sample application and Jaeger to demonstrate OpenTelemetry concepts and skills. Further resources on practical learning theory and OpenTelemetry documentation are also referenced.
Avoiding Alert Bankruptcy and Burnout
The signal to noise ratio can be frustratingly high for on-call alerts and downright mystifying when joining a new company or rotation. If you treat PagerDuty like the boy who cried wolf you’ve certainly mired in monitoring debt. Monitoring debit is tragically common and with investment mitigatable! Just how much of a risk are false positives? The data shows that a high number of false alerts trains workers to assume most alerts will be false. Repetition of the same alerts causes even greater alert fatigue. For clinicians the likelihood of acknowledging an alert dropped 30% for each reminder. Paige will share the nuts and bolts of how to start auditing your own alerts to begin your journey towards an on-call oasis.
Tracing Adventures from PR - Production
Many things can go awry on the journey from pull request (PR) open to merge to production deployment. Issues can arise from the application code, layers of YAML configuration, underlying infrastructure or pipeline logic itself. How can distributed tracing and trace-derived metrics bring developers and operators together for troubleshooting paradise? I’ll unpack a deploy gone bad from both vantage points, gaining an empathy for the engineer who needs to deploy their changes and an ops engineer who is responsible for keeping the system up and running. With signals from OpenTelemetry I will show how increasing the observability of your deploy system can facilitate better collaboration and quicker troubleshooting.
Threat Modeling in the Cloud
Threat modeling is a key part of securing your cloud computing environment and is unique to each organization's people, practices and processes. Without a threat model you could end up going on wild goose chases and miss the forest for the trees. This talk will introduce the concept of threat modeling including how to get started, guiding questions and recap findings from the Argo Project Threat Model from 2021. Learn how and why to start threat modeling today!
There's No Place Like Production
There’s a reason “i test in prod” isn’t a cheeky take but a lived reality. And that reason is there is no place like production. Not local dev or staging or other environment. This became clear when I deployed a tiny config change that passed all checks, reviews and pre-production environments that triggered a SEV-1.
Examining each step of the journey from PR to production I uncovered the snafu that had occurred (unsurprisingly it relates to overwriting key blocks on nested YAML files).
I’ll share how difficult it was to reconstruct the chain of events in the system compared to the ideal case of a highly observable system and how to share your own incident learnings since we all test in prod!
Taming Feral DevOps
The misunderstanding that devops is a tool or team or role has tragically increased the gulf between developers and operators.If you have been unofficially on-call 24/7, toiled away in turmoil, managed initiatives across multiple silos sans project manager with a directive to “influence without authority”...surprise you have lived the contradiction that is feral devops!
Consequences of letting this fester are: burn out, learned helplessness and missing out on the speed that happens when developers and operators communicate openly. Taming feral devops is a journey you can start by gathering feedback on the status quo, iterating on onboarding and training and holistically examining on-call health.Whether you’re an individual contributor, manager or leader you will find ideas to experiment with to “domesticate” devops in your organization
SRECon23 Cognitive Apprenticeship in Action_ Alert Triage Hour of Power
Cognitive apprenticeship is the philosophy that it is more effective to learn in context and real-world situations compared to following a tutorial in a sandboxed environment. In a nutshell it is “learning-through-guided-experience” and shines when teaching problem-solving processes experts use to handle complex tasks like say…investigating an alert by spelunking in production observability and monitoring data. Learn how Alert Triage Hour of Power became a can’t miss meeting of camaraderie and system surprises!
Pushing Observability Uphill - The Single “Pain” of Glass
For years tech companies have chased the fabled “single pane of glass” , the one observability tool to understand your system from north to south and east to west. Leafing through promo materials promising instant insights and seamless turnkey integrations you’d think increasing system observability is as easy as assembling a Lego set.
In my experience chasing the “single pane of glass” translates to “pain in the ass”. Survey data supports this revealing the majority of engineers cite tool sprawl as a minor or non-existent problem despite relying on several tools. As alluring as the siren call of “single pane of glass” is, let's be practical and examine how to best observe systems across a myriad of tooling.
From the telemetry buffet of metrics, events, traces and logs learn when to reach for which type and ways to bridge the gaps with links and enhance with context to free yourself from the fool’s errand of a “single pane of glass”.
Power Up with Podman
Curious about containers? There’s a new generation of containers on the scene, Podman! Supporting secure, rootless containers for Kubernetes microservices, it was designed and built with the cloud in mind. Benefitting from the lessons learned out in the open from Docker, this next generation of containers will quickly become a trusted daily driver in your dev workflow.
Covering what you need to know as an end-user from the UI to the backend, sharing a real world use case leveraging Podman for open source observability workshops https://o11y-workshops.gitlab.io. Paige will share how Podman and the adorable seal mascots Caitlín, Maighréad and Róisín have transformed her local development!
Intro to Instrumentation
This document provides an introduction and overview of instrumentation using OpenTelemetry. It begins with defining key concepts of observability and distributed tracing. It then discusses OpenTelemetry's SDKs, exporters and collectors for instrumenting code and exporting telemetry data. The document demonstrates both automatic and manual instrumentation techniques in OpenTelemetry and shows how to visualize trace data in Jaeger. It concludes by mentioning OpenTelemetry community resources for further learning.
From Cardinal(ity) Sins to Cost-Efficient Metrics Aggregation
Are you collecting just about every metric under the sun and the kitchen sink too? Understanding the cost of collecting metrics and the usefulness of those metrics is the only way to scale in a cloud native world. You can’t get away with just collecting everything as you grow. How can you make decisions about what to collect, what to drop, what to aggregate while still being able to alert, triage, remediate?
Gain immediate insights into high cost data (DPPS), when to drop time series data, and how to determine when the value of that data is at its lowest.
99.9% of Your Traces are Trash
Distributed tracing is still finding its footing in many organizations today, one challenge to overcome is the data volume - keeping 100% of your traces is expensive and unnecessary. Enter sampling - head vs tail how do you decide? Let’s review the tradeoffs associated with different types of sampling strategies and how they can be mixed and matched.
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Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)
Observability Concepts EVERY Developer Should Know (DevOpsDays Seattle)
Power Up with Podman - Kubernetes Community Day LA
Power Up with Podman - Kubernetes Community Day LA
99.99% of Your Traces Are (Probably) Trash (SRECon NA 2024).pdf
99.99% of Your Traces Are (Probably) Trash (SRECon NA 2024).pdf
OTel Orientation: How to Train Teams (OTel in Practice)
OTel Orientation: How to Train Teams (OTel in Practice)
SRECon23 Cognitive Apprenticeship in Action_ Alert Triage Hour of Power
SRECon23 Cognitive Apprenticeship in Action_ Alert Triage Hour of Power
Pushing Observability Uphill - The Single “Pain” of Glass
Pushing Observability Uphill - The Single “Pain” of Glass
From Cardinal(ity) Sins to Cost-Efficient Metrics Aggregation
From Cardinal(ity) Sins to Cost-Efficient Metrics Aggregation
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Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdf
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
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How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
dbms calicut university B. sc Cs 4th sem.pdf
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Finale of the Year: Apply for Next One!
Presentation for the event called "Finale of the Year: Apply for Next One!" organized by GDSC PJATK
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdf
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Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
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Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
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Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
Generating privacy-protected synthetic data using Secludy and Milvus
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Best 20 SEO Techniques To Improve Website Visibility In SERP
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Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
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Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
3rd Wave Observability: Open or Bust
- 1. 3rd Wave Observability: Open or Bust Paige Cruz Developer Advocate November 2023
- 2. “ Navigating today’s IT infrastructure is like exploring underground caves. You wander around in the dark, crawling systems, sifting through lots of data to try and understand what’s going on. - splunk.com circa XXXX ”
- 3. “ Navigating today’s IT infrastructure is like exploring underground caves. You wander around in the dark, crawling systems, sifting through lots of data to try and understand what’s going on. - splunk.com circa 2005 ”
- 7. 7 80s - early 00s Wave 1 Network + Device Monitoring mid 00s - late 10s Wave 2 Application Performance Monitoring late 10s - now Wave 3 Observability 7
- 12. The 80s
- 14. The 90s
- 18. 18 80s - early 00s Wave 1 Network + Device Monitoring mid 00s - late 10s Wave 2 Application Performance Monitoring 18
- 19. The 00s
- 23. 23 80s - early 00s Wave 1 Network + Device Monitoring mid 00s - late 10s Wave 2 Application Performance Monitoring late 10s - now Wave 3 Observability 23
- 25. Monitoring
- 26. Observability
- 29. Evolving past APM… Proprietary Data Formats Data Volume Interdependence Ephemerality 29 29
- 30. “Why is ______?”
- 31. 31
- 32. 32
- 35. If you rely on one or two experts to tell you if the system is working fine, where to get health data about the infrastructure, how to interpret the charts and traces on your dashboards…. You have a “Katie” 35
- 36. If you rely on one or two experts to tell you if the system is working fine, where to get health data about the infrastructure, how to interpret the charts and traces on your dashboards…. You have a “Katie” 36 Teams own more services than ever in cloud native environments. Today you have a responsibility to understand your “service neighborhood” - dependencies both in and outside of your organization. You build it, you run it, you observe it
- 37. If you rely on one or two experts to tell you if the system is working fine, where to get health data about the infrastructure, how to interpret the charts and traces on your dashboards…. You have a “Katie” 37 Teams own more services than ever in cloud native environments. Today you have a responsibility to understand your “service neighborhood” - dependencies both in and outside of your organization. You build it, you run it, you observe it Being able to tie the work you do to impact on customers, users, or key business goals is critical for growing your career. Observability data can and should drive conversations about where to invest engineering time. Develop yourself