WeAreDevelopers LIVE Oct 30, 2024

Observability with OpenTelemetry & Elastic

Iulia Feroli

Are you still debugging complex Python microservices with native print statements? Discover how to achieve vendor-agnostic observability by combining OpenTelemetry's standardized signals with Elastic's powerful dashboards.

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#1 about 2 min

The increasing complexity of modern application observability

As applications and generative AI solutions become more complex, standardized monitoring processes are required to diagnose unexpected failures.

#2 about 6 min

Limitations of native debugging and logging in Python

Relying on print statements or the built-in logging module creates manual overhead and lacks compatibility across robust software systems.

#3 about 2 min

Defining OpenTelemetry as a standardized observability framework

OpenTelemetry provides a rapidly growing open-source toolkit to standardize data collection across all vendor ecosystems and programming languages.

#4 about 4 min

Core concepts of application instrumentation and telemetry

Instrumenting application code enables the continuous emission of telemetry signals that detail system behavior without inspecting internal mechanisms.

#5 about 5 min

Differentiating between metrics, logs, and distributed traces

Telemetry data breaks down into numeric metrics over time, discrete event logs, and sequence-based distributed traces for mapping request paths.

#6 about 3 min

Preventing vendor lock-in with an independent observability layer

Implementing an open standard separates data collection from specific cloud providers or analysis tools to ensure architectural flexibility.

#7 about 3 min

Implementing manual and automatic instrumentation in Python

Python developers can embed specific library calls manually or use an automated toolkit to seamlessly track common frameworks without altering core code.

#8 about 4 min

Integrating Elastic dashboards with OpenTelemetry data schemas

Routing standardized telemetry into Elastic's unified schema provides powerful search capabilities and comprehensive dashboards for advanced debugging.

#9 about 4 min

Live dashboard demonstration of tracing errors and system metrics

Running a containerized Python demo highlights how observability platforms surface latency bottlenecks, crash logs, and complex request transactions automatically.

#10 about 2 min

Future trends for monitoring complex software and language models

Embedding open-source observability practices simplifies long-term maintenance and model optimization for emerging machine learning applications.

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Upcoming sessions on this topic

Open session

World Congress 2026 North America

September 24, 2026 · 13:30–15:30

Stage 14

From Signal to Action: Empowering Your AI SRE with OpenTelemetry Data

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Open session

World Congress 2026 North America

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Outdoor Stage

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Open session

World Congress 2026 North America

September 24, 2026 · 14:10–14:40

Stage 3

Real-Time Data Platforms at Trillion-Event Scale

Diptamay Sanyal

Principal Engineer | Data, AI & Cybersecurity Platforms

Diptamay Sanyal
Open session

World Congress 2026 North America

September 24, 2026 · 11:40–12:10

Stage 5

Your Agents Need Observability Before They Need Better Models

Julia Furst Morgado

Principal Developer Relations Engineer at Dash0

Julia Furst Morgado
Open session

World Congress 2026 North America

September 25, 2026 · 13:30–14:00

Stage 2

Bluesky's Open Source Moderation Tools: LLM-based Event Detection in Python

Alex Garnett

Developer Relations, Bluesky

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Open session

World Congress 2026 North America

September 24, 2026 · 11:40–12:10

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Taming Rogue Agents: Observability-Driven Evaluation for Production Reliability

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Anjana Umapathy
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