# Instrumentation A software engineering technique that adds instructions or bytecode to programs to collect data for tools such as monitors, profilers, coverage analyzers, and event loggers. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Digital Experience Monitoring with Grafana Cloud: Session Replay, synthetic checks, and faster investigations DevFeed: [Digital Experience Monitoring with Grafana Cloud: Session Replay, synthetic checks, and faster investigations]() Original publisher: [Read original article]() Author: Bukola Ayodele Published: 2026-09-15T01:35:30.954353Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [digital experience monitoring](), [Grafana Cloud](), [Frontend observability](), [synthetic monitoring](), [real user monitoring](), [session replay](), [Instrumentation](), [web applications](), [JavaScript](), [Core Web Vitals](), [Traces]() Tags: [analytics](), [core-web-vitals](), [digital-experience-monitoring](), [frontend-observability](), [grafana](), [grafana-cloud](), [grafana-cloud-frontend-observability](), [instrumentation](), [javascript](), [logs](), [metrics](), [monitoring](), [observability](), [real-user-monitoring](), [session-replay](), [synthetic](), [synthetic-monitoring](), [traces](), [web-applications]() ### AI overview This article explains how Digital Experience Monitoring in Grafana Cloud combines Frontend Observability, Synthetic Monitoring, real user monitoring, and session replay. It shows how these capabilities help engineering teams detect issues proactively, understand their scope and root causes, correlate frontend signals with backend traces, and resolve production problems faster. ### Source excerpt When something breaks in production, the questions that matter most are also the toughest to answer from metrics alone: who was affected, what did they actually see, and is this worth waking someone up for? Answering those questions requires a fuller picture of the issue and its impact on your users. That's where Digital Experience Monitoring (DEM) in Grafana Cloud comes in. By combining Frontend Observability and Synthetic Monitoring, DEM connects real user experiences with proactive testing, helping engineering teams understand the scope of an issue, investigate its cause, and resolve it faster, all within Grafana Cloud. In this blog post, we'll walk through some of the latest DEM updates in Grafana Cloud, and how to get started. You can also learn more by watching the video below. First, what is Digital Experience Monitoring? Digital Experience Monitoring in Grafana Cloud gives you a complete picture of how users experience your web applications, from real user data to proactive synthetic checks. DEM helps your team achieve: Real user visibility: know how users truly experience your web application, not just what your backend metrics suggest. Proactive detection: catch problems before your users do, using automated checks against your critical user journeys. End-to-end correlation: connect a frontend signal to the backend trace behind it. Faster resolution: cut your mean time to recovery from hours to minutes. Session Replay: see exactly what your users saw Session Replay in Grafana Cloud Frontend Observability lets you visually replay what a user saw and did inside your web application. Your team can watch exactly what users experienced and correlate it with real user monitoring signals like Core Web Vitals, user actions, and traces, which makes it a powerful tool for investigating bugs and running root cause analysis. Session Replay is powered by Faro, Grafana's open source JavaScript instrumentation library for collecting real user monitoring data. Let's walk ## Faster Starts, Less JavaScript Overhead DevFeed: [Faster Starts, Less JavaScript Overhead]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [CodeName One]() Topics: [JavaScript](), [Compiler](), [Instrumentation]() Tags: [benchmarks](), [compiler](), [instrumentation](), [javascript](), [profiling]() ### AI overview The article describes startup-performance work in Codename One. It identifies launch delays caused by repeated screen-scale queries, theme scans, unnecessary synchronous dispatches, premature GC-park handshakes, and JavaScript suspension preparation. The fixes publish screen state atomically, avoid waiting when operations can proceed immediately, index theme keys, and improve instrumentation so profiling reveals hidden stalls. ### Source excerpt Codename One removes native startup waits, repeated style scans, and unnecessary JavaScript suspension. Profiles and compiler benchmarks expose costs that bundle size and frame rates miss. ## How to measure and improve instrumentation quality for better full-stack observability DevFeed: [How to measure and improve instrumentation quality for better full-stack observability]() Original publisher: [Read original article]() Author: Arpit kumar Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Instrumentation](), [observability](), [Grafana Cloud](), [telemetry]() Tags: [analytics](), [grafana](), [grafana-cloud](), [how-to](), [instrumentation](), [knowledge-graph](), [logs](), [metrics](), [monitoring](), [observability](), [traces]() ### AI overview This article explains how to measure and improve instrumentation quality for full-stack observability. It introduces Grafana Cloud's continuously updated instrumentation quality report, which evaluates each service using automated checks for telemetry completeness, correctness, signal coverage, naming, Kubernetes correlation, and connections across the service graph. ### Source excerpt Modern engineering teams instrument everything, with metrics, logs, traces, and profiles flowing from hundreds of services at once. But full-stack observability isn't really about collecting more telemetry; it's about having a single, unified picture of how your services connect to every layer beneath them, including their dependencies, the pods and nodes they run on, and the logs, traces, and profiles that explain their behavior. But there's often a quiet problem hiding underneath all that data: not all instrumentation is created equal, and every gap silently breaks one of those connections. One service, for example, might emit metrics but no logs, so when you pivot from "this is erroring" to "show me why," you hit a dead end. Another might have logs but an invalid service.name or a missing k8s.pod.name that breaks correlation, dropping it out of the graph and away from its pods and nodes. A third service might look perfectly healthy right up until an incident, when you discover its traces were never wired up and the trail goes cold exactly when you need it most. To fix this, Grafana Cloud's Knowledge Graph now includes an instrumentation quality report: an automated, continuously updated assessment of how well each of your services is instrumented--and, in effect, how they plug into the full-stack picture. In this post, we'll walk through how to read the instrumentation quality report, how the scoring works, and how to use it to systematically raise the observability bar, keeping every layer of your stack joined up across every service you run. What is instrumentation quality? Instrumentation quality is a measure of how complete and correct the telemetry for a given service is, judged against a set of automated checks. Each service is evaluated by a server-computed set of quality checks: small, focused rules that validate one specific thing about a service's telemetry. A few examples: Does the service emit logs? Are service graph metrics present? Is the service nam ## How to add Sentry monitoring to a Webflow Cloud app DevFeed: [How to add Sentry monitoring to a Webflow Cloud app]() Original publisher: [Read original article]() Author: Ismail Ajagbe Published: 2026-09-12T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [error tracking](), [Next.js](), [Workers](), [session replay](), [debugging](), [Traces](), [configuration](), [Instrumentation](), [Command-line interface]() Tags: [cloudflare-workers](), [debugging](), [error-tracking](), [guides](), [how-to](), [instrumentation](), [monitoring](), [next-js](), [session-replay](), [traces]() ### AI overview This tutorial explains how to manually add Sentry monitoring to a Webflow Cloud app running Next.js. It covers SDK installation, initialization files, environment variables, a Cloudflare-specific tunnel fix, verification, alerts, and common silent failure modes. The setup enables error grouping, stack traces, performance traces, and session replay. ### Source excerpt Learn how to add Sentry monitoring to a Webflow Cloud Next.js app. Get error grouping, stack traces, and session replay working. ## Wrapture: a Python package for monkey patching, testing, and observability DevFeed: [Wrapture: a Python package for monkey patching, testing, and observability]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-11T13:51:32Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [Python](), [Testing](), [observability](), [Instrumentation](), [tracing](), [OpenTelemetry](), [Flask](), [jupyterlab]() Tags: [code](), [graham-dumpleton](), [graham-dumpleton-6](), [how-to](), [instrumentation](), [monkey-patching](), [monkey-patching-10](), [observability](), [observability-10](), [open-source](), [open-source-320](), [opentelemetry](), [python](), [python-1-283](), [testing](), [testing-95](), [tracing]() ### AI overview This article reviews wrapture, an alpha Python package by Graham Dumpleton for monkey patching, unit testing, call recording, live and zero-code tracing, timing analysis, and OpenTelemetry export. It highlights tutorials and JupyterLab workshops covering the package and related instrumentation. ### Source excerpt Graham Dumpleton's new monkey patching package wrapture is shaping up to be an indispensable tool for Python developers. I'm not sure why I've seen so little buzz about it! Graham has been posting new tutorials for it almost daily since the initial release on August 31st. Here's everything he's published so far: Introducing wrapture - a new monkey patching library that serves both testing and observability (think New Relic style tracing) at the same time. Unit testing with wrapture - how to use it for the same kinds of thing as unittest.mock. Recording calls with wrapture - recording method calls as timelines and processing and displaying them as trees. Phased behaviour in wrapture - arranging patched methods to change behavior across multiple calls. Beyond callables in wrapture - monkey patching attributes, dictionaries, generators. Live tracing with wrapture - tracing a live application to see exactly how it works. Zero-code tracing with wrapture - configuring tracing in a separate TOML file without modifying Python code at all. Tracing Flask with wrapture - using the separate wrapture-instrumenation package to instrument a Flask application. That package also provides instrumentation for aiohttp.client, aiohttp.web, django, fastapi, flask, grpc, http.client, httpx, jinja2, requests, sqlalchemy, sqlite3, starlette, urllib.request, urllib3, uvicorn, werkzeug.serving, wsgiref.simple_server, xmlrpc.client, xmlrpc.server. Finding slow code with wrapture - wrapture's tools for recording timing information, both individually and aggregated across multiple calls. OpenTelemetry export in wrapture - exporting traces to OpenTelemetry. Graham also has a set of interactive workshops for wrapture, implemented as JupyterLab notebooks. Wrapture is still alpha software but it's already very usable - especially given you can configure and try it out with a TOML file without modifying any Python code at all. This feels like one of those Swiss Army Knife packages that, once mastered ## What does DX do? Key features, use cases, and alternatives DevFeed: [What does DX do? Key features, use cases, and alternatives]() Original publisher: [Read original article]() Author: John Crowley Published: 2026-09-09T15:53:52Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [code productivity](), [developer-productivity](), [Developer experience](), [atlassian](), [Git](), [Instrumentation](), [sdlc](), [Artificial Intelligence]() Tags: [agentic](), [ai](), [atlassian](), [developer-experience](), [developer-productivity](), [engineering](), [git](), [instrumentation](), [metrics](), [sdlc](), [survey]() ### AI overview This article explains what DX does as an engineering intelligence platform. DX measures developer productivity, developer experience, delivery data, and AI's impact on codebases using developer surveys and data from Git and Jira. It describes DX's DevEx Cloud, DX Data Cloud, DX AI, and Fabric offerings, along with their research-backed metrics, use cases, limitations, and alternatives. ### Source excerpt What DX does: its engineering intelligence features, survey-based metrics, common use cases, where it stops, and the alternatives ## Coordinate product launches with Datadog DevFeed: [Coordinate product launches with Datadog]() Original publisher: [Read original article]() Author: Milene Darnis; Adam Virani Published: 2026-09-08T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [Instrumentation](), [experiments]() Tags: [ai](), [bits-ai](), [digital-experience-monitoring](), [experimentation](), [experiments](), [feature-flags](), [instrumentation](), [launch](), [product-analytics](), [session-replay]() ### AI overview A tutorial on using Datadog Product Analytics Launches to plan product releases, define measurement questions, create tracking plans, and identify missing events and properties before rollout. ### Source excerpt Learn how to turn a product brief and feature flag into a connected launch workflow for instrumentation, experimentation, QA, and reporting. ## Open by design: How Elastic supports MOSA, the DoD's Modular Open Systems Approach DevFeed: [Open by design: How Elastic supports MOSA, the DoD's Modular Open Systems Approach]() Original publisher: [Read original article]() Author: Woody Walton Published: 2026-09-04T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [Architecture & Design](), [data](), [Instrumentation](), [telemetry]() Tags: [acquisition](), [analytics](), [data](), [defense-government-open-source-standards](), [design](), [elasticsearch-platform](), [government-public-sector](), [instrumentation](), [integration](), [open](), [opentelemetry](), [scalability](), [systems]() ### AI overview Elastic explains how its data and analytics platform can support the US Department of Defense Modular Open Systems Approach through modularity, open standards, and component removability. ### Source excerpt Major US defense acquisition programs are required to be designed around a Modular Open Systems Approach (MOSA). Here's what program offices need to know about how Elastic can support MOSA at the data and analytics layer. ## The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough DevFeed: [The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-02T17:15:57Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [CUDA](), [Optimization](), [GPU](), [toolchain](), [debug](), [Instrumentation](), [Algorithms]() Tags: [algorithms](), [benchmark](), [cuda](), [debug](), [developer-tools-techniques](), [gpu](), [how-to](), [image-processing](), [instrumentation](), [memory](), [optimization]() ### AI overview A step-by-step CUDA optimization walkthrough that improves an image-processing pipeline with debugging, benchmarking, optimized algorithms, GPU memory management, pinned transfers, streams, and asynchronous transfers. ### Source excerpt NVIDIA CUDA remains the foundation of GPU-accelerated computing, powering everything from scientific simulations to large-scale AI training. But writing... ## Making Rust observability reliable at scale with OpenTelemetry DevFeed: [Making Rust observability reliable at scale with OpenTelemetry]() Original publisher: [Read original article]() Author: Björn Antonsson; Paul Le Grand des Cloizeaux; Scott Gerring Published: 2026-09-01T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [tracing](), [telemetry](), [Traces](), [Instrumentation]() Tags: [apm](), [instrumentation](), [latency](), [observability](), [opentelemetry](), [production](), [rust](), [scale](), [traces](), [tracing]() ### AI overview Datadog describes building an opinionated Rust tracer on OpenTelemetry to improve trace propagation, sampling consistency, and trace quality in production services. ### Source excerpt Learn how Datadog improved Rust tracing by building an opinionated OpenTelemetry-based library to help ensure consistent sampling, propagation, and trace quality at scale. ## Debug live production code without redeploying with Datadog Live Debugger DevFeed: [Debug live production code without redeploying with Datadog Live Debugger]() Original publisher: [Read original article]() Author: Eric Metaj; Sarah Stonehill Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [debugging](), [Instrumentation](), [AI-assisted coding](), [Code]() Tags: [agents](), [ai](), [apm](), [bits-ai](), [code](), [debug](), [debugger](), [debugging](), [developer](), [ide](), [instrumentation](), [production](), [root-cause-analysis](), [tracing]() ### AI overview Datadog Live Debugger helps developers investigate production bugs without changing, restarting, or redeploying application code. It captures runtime details through logpoints, including variable values, method arguments, execution context, and request paths. Bits AI can analyze linked source code, place non-breaking logpoints, interpret collected data, and suggest fixes grounded in production behavior. ### Source excerpt Learn how Live Debugger helps you investigate production code and debug faster using Bits AI. ## How to reduce Rails boot time with require-profiler and Bootsnap DevFeed: [How to reduce Rails boot time with require-profiler and Bootsnap]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-08-19T00:00:00Z Content type: tutorial Language: en Sources: [Evil Martians]() Topics: [Rails](), [Optimization](), [Ruby](), [Instrumentation](), [Docker](), [Development]() Tags: [development](), [docker](), [dx](), [instrumentation](), [optimization](), [performance](), [rails](), [ruby]() ### AI overview This guide explains how to profile and reduce Rails application boot time using require-profiler, sampling profilers, and Bootsnap. It describes profiling results from real projects, including a 40% reduction for a 200-component monolith, and covers cache precompilation and verification in Docker-based production builds. ### Source excerpt Rails boot time is a DX metric in the AI age: meet require-profiler, learn to actually read sampling profilers, and see the pit stop that cut a 200-component monolith's boot by 40%. ## Observability best practices for Lambda durable functions DevFeed: [Observability best practices for Lambda durable functions]() Original publisher: [Read original article]() Author: D Surya Sai Published: 2026-08-14T12:49:49Z Content type: tutorial Language: en Sources: [AWS Compute Blog]() Topics: [AWS Lambda](), [observability](), [Monitoring](), [log management](), [tracing](), [Instrumentation](), [debugging](), [Amazon API Gateway](), [stripe](), [Serverless]() Tags: [advanced-300](), [amazon-api-gateway](), [aws](), [aws-lambda](), [debugging](), [instrumentation](), [logging](), [metrics](), [monitoring](), [observability](), [payment](), [payment-processing](), [payments](), [serverless](), [technical-how-to](), [tracing]() ### AI overview This tutorial presents an observability pattern for AWS Lambda durable functions using a Stripe payment-processing workflow. It covers durable-function metrics, custom business metrics, alarms, structured logging, AWS X-Ray tracing, instrumentation across suspension boundaries, and end-to-end debugging of callback timeouts. ### Source excerpt Learn observability best practices for AWS Lambda durable functions, including CloudWatch metrics, custom alarms, structured logging, and X-Ray tracing for debugging callback timeouts end-to-end. ## ClickHouse joins the Open Secure AI Alliance DevFeed: [ClickHouse joins the Open Secure AI Alliance]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-07-30T19:44:40Z Content type: news Language: en Sources: [ClickHouse Blog]() Topics: [ai security](), [AI Agent](), [Securing AI](), [agent observability](), [clickhouse](), [Open Source](), [tracing](), [observability](), [Instrumentation](), [hugging face](), [Nvidia]() Tags: [agents](), [ai](), [ai-agent](), [ai-agents](), [ai-security](), [clickhouse](), [hugging-face](), [instrumentation](), [nvidia](), [observability](), [open-source](), [security](), [tracing]() ### AI overview ClickHouse joins the Open Secure AI Alliance with NVIDIA and other industry leaders to develop open tools for securing AI agents. The article highlights Langfuse for agent tracing, evaluations, guardrail monitoring, and open audit trails, including air-gapped deployments that store traces in ClickHouse. ### Source excerpt ClickHouse is joining the Open Secure AI Alliance alongside NVIDIA and other industry leaders to help build open tools that keep AI agents secure. ## The Gremlin app for Dynatrace: resilience testing and reliability scoring, built on the observability you already trust DevFeed: [The Gremlin app for Dynatrace: resilience testing and reliability scoring, built on the observability you already trust]() Original publisher: [Read original article]() Author: Ryan Detwiller Published: 2026-07-28T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [Resilience](), [dynatrace](), [observability](), [Instrumentation](), [distributed-systems]() Tags: [announcements](), [distributed-systems](), [dynatrace](), [engineering](), [gremlin](), [incident](), [instrumentation](), [metrics](), [observability](), [real-time](), [resilience](), [safety](), [systems](), [testing](), [uptime]() ### AI overview The Gremlin app for Dynatrace adds resilience testing and reliability scoring to Dynatrace workflows. Teams can run reliability tests, observe their impact in real time, and track service-level reliability scores using existing Dynatrace metrics, alerts, instrumentation, and health checks. ### Source excerpt With the Gremlin app for Dyantrace, you get resilience testing and reliability scoring built on the observability you already trust. ## Boost Developer Productivity: 8 Key Questions DevFeed: [Boost Developer Productivity: 8 Key Questions]() Original publisher: [Read original article]() Author: Mridhula Venkat Published: 2026-07-24T00:00:00Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [code productivity](), [dora metrics](), [Developer experience](), [Software Engineering](), [Instrumentation](), [pull-requests]() Tags: [developer-experience](), [developer-productivity](), [dora-metrics](), [efficiency](), [instrumentation](), [metrics](), [productivity](), [pull-requests](), [slack](), [software-engineering]() ### AI overview This article presents eight questions for engineering leaders to assess and improve developer productivity. It argues that story points and commit counts can obscure bottlenecks such as handoff delays, review queues, and context switching, and recommends considering team velocity, deployment frequency, DORA metrics, delivery quality, and business impact. ### Source excerpt Boost developer productivity with 8 essential questions covering metrics, workflows, and team efficiency. Learn how Harness SEI helps. | Blog ## Announcing v1 of OpenTelemetry Go Compile-Time Instrumentation DevFeed: [Announcing v1 of OpenTelemetry Go Compile-Time Instrumentation]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-07-23T06:20:13Z Content type: release Language: en Sources: [Blog on OpenTelemetry]() Topics: [Go Language](), [OpenTelemetry](), [Instrumentation](), [telemetry]() Tags: [go](), [instrumentation](), [opentelemetry](), [release](), [telemetry](), [zero-code]() ### AI overview OpenTelemetry announces the first stable release of Go Compile-Time Instrumentation. The project injects OpenTelemetry instrumentation during the Go build process, allowing traces and metrics for applications, dependencies, and supported standard-library packages without source-code changes or a separate runtime agent. ### Source excerpt If you write Java, Python, Node.js, or .NET, you have been able to add OpenTelemetry to an application without editing its code for years: attach an agent at startup and telemetry starts flowing. Go has been the exception. A Go program compiles to a single static binary with no runtime to hook into at startup, so Go developers have had to instrument by hand or reach for an out-of-process eBPF agent. ## What is observability platform engineering? DevFeed: [What is observability platform engineering?]() Original publisher: [Read original article]() Author: Luca Galante Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [observability](), [Platform Engineering](), [telemetry](), [Developer experience](), [Monitoring](), [OpenTelemetry](), [CI/CD](), [Instrumentation](), [Kubernetes](), [Databases]() Tags: [ci-cd](), [databases](), [developer](), [engineering](), [infrastructure](), [instrumentation](), [kubernetes](), [monitoring](), [observability](), [opentelemetry](), [platform-engineering](), [telemetry]() ### AI overview The article explains observability platform engineering as a platform-engineering discipline that makes observability a core capability. It presents a dual mandate: maintaining visibility into shared infrastructure while giving developers frictionless ways to monitor their applications. The approach uses OpenTelemetry, automation, auto-instrumentation, preconfigured dashboards, and correlated telemetry to support reliable self-service development. ### Source excerpt Observability platform engineering treats observability as a core platform capability, enabling operational visibility for infrastructure and seamless developer experience. ## OpenTelemetry: The platform engineer's path to standardized observability DevFeed: [OpenTelemetry: The platform engineer's path to standardized observability]() Original publisher: [Read original article]() Author: Sam Barlien Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [OpenTelemetry](), [observability](), [telemetry](), [Instrumentation](), [Traces](), [Kubernetes]() Tags: [cncf](), [incident](), [instrumentation](), [kubernetes](), [logs](), [metrics](), [observability](), [opentelemetry](), [platform](), [sdks](), [telemetry](), [traces](), [vendor-lock-in]() ### AI overview This article explains how OpenTelemetry standardizes observability across platform services by providing vendor-neutral APIs, SDKs, semantic conventions, and a Collector for routing and processing telemetry. It connects logs, metrics, and traces while allowing teams to choose analysis backends later, reducing fragmentation and vendor lock-in. ### Source excerpt Standardize observability across your platform with OpenTelemetry. Eliminate fragmented telemetry, reduce incident resolution time, and avoid vendor lock-in. ## 10 observability tools platform engineers should evaluate in 2026 DevFeed: [10 observability tools platform engineers should evaluate in 2026]() Original publisher: [Read original article]() Author: Sam Barlien Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [observability](), [Platform Engineering](), [OpenTelemetry](), [telemetry](), [distributed-systems](), [Kubernetes](), [Microservices](), [Instrumentation](), [Monitoring](), [CI/CD]() Tags: [ci-cd](), [developer](), [instrumentation](), [integration](), [kubernetes](), [metrics](), [microservices](), [observability](), [opentelemetry](), [operational](), [platform](), [platform-engineering](), [telemetry](), [traces]() ### AI overview This article evaluates observability tools for platform engineers in 2026. It focuses on the dual requirement of operational visibility and developer self-service, with evaluation criteria including native OpenTelemetry support, cost optimization, transparent pricing, and integration with internal developer platforms. It also discusses observability for Kubernetes, microservices, distributed systems, and shared infrastructure. ### Source excerpt Discover the 10 best observability tools for platform engineers in 2026. Learn how to meet the dual mandate of operational visibility and developer self-service by prioritizing OpenTelemetry support, transparent pricing, and seamless integration with your platform. ## Use OpenTelemetry-native observability with Datadog from ingestion to investigation DevFeed: [Use OpenTelemetry-native observability with Datadog from ingestion to investigation]() Original publisher: [Read original article]() Author: Shanel Huang Published: 2026-07-20T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [OpenTelemetry](), [observability](), [telemetry](), [Application Performance Management (APM)](), [infrastructure monitoring](), [Instrumentation](), [Traces](), [log management]() Tags: [apm](), [infrastructure-monitoring](), [instrumentation](), [kubernetes](), [logs](), [metrics](), [monitoring](), [observability](), [open-source](), [opentelemetry](), [sdks](), [serverless](), [telemetry](), [traces](), [tracing]() ### AI overview This article explains how Datadog supports OpenTelemetry-native observability from instrumentation through ingestion and investigation. It covers vendor-neutral OTLP ingestion through the OTel Collector's standard HTTP exporter and Datadog's direct OTLP intake for metrics, logs, and traces, including use cases such as managed services and serverless environments. ### Source excerpt Learn how you can use vendor-neutral telemetry while preserving Datadog's infrastructure and APM experiences. ## OpenTelemetry Has Graduated... Now what? DevFeed: [OpenTelemetry Has Graduated... Now what?]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-07-15T18:59:02Z Content type: opinion Language: en Sources: [Blog on OpenTelemetry]() Topics: [OpenTelemetry](), [telemetry](), [Open Source](), [vendor lock-in](), [Instrumentation](), [Traces]() Tags: [cncf](), [instrumentation](), [open-source](), [opentelemetry](), [telemetry](), [traces](), [tracing](), [vendor-lock-in]() ### AI overview This article discusses OpenTelemetry's CNCF graduated status, its history as a unified standard for traces, logs, and metrics, and the role of standardized APIs, language implementations, and the Collector in reducing vendor lock-in. ### Source excerpt In case you missed it: OpenTelemetry (OTel) has officially achieved CNCF graduated status! It now stands proudly alongside amazing open source projects such as Kubernetes and Prometheus, to name just a few. It's been a long journey, and we're very excited... But, now what? To understand where we're going, it's important to understand where we came from. History In the not-so-distant past, telemetry signals were not standardized. This meant telemetry formats differed from tool to tool, with each telemetry vendor creating and maintaining its own instrumentation libraries. Vendor lock-in was a huge problem: If you wanted to switch vendors, you had to strip out the previous vendor's libraries from your code and replace them with the new vendor's libraries. As a result, switching vendors was a nontrivial task. ## Logging Best Practices for Debugging Applications DevFeed: [Logging Best Practices for Debugging Applications]() Original publisher: [Read original article]() Author: Ben Coe Published: 2026-07-09T09:00:00Z Content type: tutorial Language: en Sources: [Sentry Blog]() Topics: [Logging](), [debugging](), [observability](), [audit](), [Instrumentation]() Tags: [audit](), [best-practices](), [debugging](), [how-to](), [instrumentation](), [learn](), [logging](), [observability](), [sentry]() ### AI overview This article presents practical logging best practices for applications. It recommends adding targeted log lines, using temporary instrumentation during investigations, recording runtime decisions and intermediate outcomes, and auditing access and mutating operations to make production software easier to debug and understand. ### Source excerpt Learn logging best practices: what to log, how to structure messages, and what to avoid. Practical tips with Sentry's structured logging. ## Cost Attribution in Discord's API DevFeed: [Cost Attribution in Discord's API]() Original publisher: [Read original article]() Author: Jim Benton Published: 2026-06-30T00:00:00Z Content type: article Language: en Sources: [Discord Blog]() Topics: [API](), [Discord](), [hosting](), [Kubernetes](), [observability](), [Instrumentation](), [Python](), [Deployment]() Tags: [api](), [cost](), [deployment](), [discord](), [hosting](), [instrumentation](), [kubernetes](), [observability](), [python](), [tooling]() ### AI overview Discord describes a method for attributing API hosting costs to individual features and endpoints. By extending application profiling tooling, the team estimates costs from the time worker processes spend executing feature-related code, without changing the existing Kubernetes deployment topology. ### Source excerpt Discord's API spans 1700+ endpoints across hundreds of Kubernetes deployments. The challenge: tracking per-feature hosting costs without restructuring. Jim Benton helps explain how Discord tackled the situation. [Next page]()