Part 3: Building Tracing Pipeline with Alloy and Exporting to Tempo

Video Description

📌 Welcome to the Grafana Alloy Course! Grafana Alloy is a powerful, vendor-neutral tool built on OpenTelemetry (OTel) that collects metrics, traces, and logs. In this video, we focus on tracing pipelines — walking you through the complete process of ingesting, processing, and exporting trace data with Alloy and Tempo. --- 📋 What This Video Covers: Intro to tracing pipelines with Grafana Alloy Understanding traces, spans & instrumentation Manual instrumentation for a Python app Auto-instrumentation in Kubernetes with OTel Operator Deploying Tempo, Grafana & Alloy for trace collection Configuring OTLP receiver & exporter Viewing traces in Grafana Enhancing traces with processors: span, attributes, k8s attributes, filter, transform, batch Building service graphs from trace data --- ⏰ Timestamps for Easy Navigation: 00:00:00 Intro to Tracing Pipelines with Grafana Alloy 00:01:00 What Are Traces, Spans and Instrumentation? A Quick Intro 00:04:53 Understanding the Tracing Pipeline Architecture: Receivers, Processors, and Exporters 00:07:11 Tracing a Python Demo Shop App with Manual Instrumentation 00:15:11 Auto-Instrumenting a Python Demo Shop App in Kubernetes with OpenTelemetry Operator 00:23:14 Deploying Tempo, Grafana & Alloy on Kubernetes to Implement the Tracing Pipeline 00:36:24 Receiver.OTLP – Configuring the OpenTelemetry Receiver for Trace Ingestion 00:40:02 Exporter.OTLP – Sending Traces from OpenTelemetry to Tempo 00:42:01 Viewing Traces in Grafana: Basic Tracing Pipeline Without Processors 00:44:43 Processor.Span – Modifying and Enriching Trace Spans 00:53:14 Processor.Attributes – Adding and Updating Span Attributes 00:59:17 Span vs Resource Attributes – Understanding the Difference 01:00:52 Processor.K8sAttributes – Adding Kubernetes Metadata to Traces 01:07:53 Processor.Filter – Filtering Spans Before Export 01:12:26 Processor.Transform – Transforming Trace Data for Custom Needs 01:23:19 Processor.Batch – Optimizing Trace Export with Batching 01:29:40 Connector.ServiceGraph – Building Service Graphs from Trace Data --- 🎥 These are the other parts of the Grafana Alloy playlist, covering collecting logs and metrics with Alloy: https://www.youtube.com/watch?v=tMSZ_DVq5pw https://www.youtube.com/watch?v=kDQ-egkmNvE --- 📚 Learn More from the Official Documentation: For more in-depth details and advanced configuration options, make sure to check out the Grafana Alloy official documentation: 👉 https://grafana.com/docs/alloy/latest/ --- 🔗 Code and Resources: All the code explained in this video is available on the CodeOpsByMo GitHub organization: 👉 https://github.com/codeopsbymo The repository for this course can be found here: 👉 https://github.com/codeopsbymo/grafan... --- 📢 Why Follow This Course? This episode is your complete, hands-on guide to building a tracing pipeline with Grafana Alloy and Tempo. You’ll not only understand the theory behind traces, spans, and processors — but also see it in action with real deployments on Kubernetes. By following along, you’ll gain the skills to: Instrument applications (manual & auto) for distributed tracing. Deploy and configure a tracing stack. Enrich and transform trace data for deeper insights. Build service graphs to visualize relationships in your system. 📌 Subscribe to CodeOpsByMo for more hands-on DevOps tutorials. 👍 Like the video if you found it helpful, and let us know your thoughts in the comments!