Iulia Feroli
Observability with OpenTelemetry & Elastic
#1about 1 minute
The growing need for observability in complex applications
Modern applications with many moving parts, like those in the GenAI space, require robust monitoring to diagnose and fix issues effectively.
#2about 4 minutes
Moving beyond print statements for Python monitoring
While print() statements are a common starting point for debugging, Python's native logging module offers a more structured, albeit limited, approach.
#3about 5 minutes
Introducing OpenTelemetry as a universal standard
OpenTelemetry provides a vendor-agnostic, open-source framework for instrumenting applications to emit telemetry data for analysis.
#4about 7 minutes
Exploring the three main signals: traces, metrics, and logs
Observability is built on three core signal types: traces for request paths, metrics for numerical data, and logs for event records.
#5about 5 minutes
Using manual and automatic instrumentation in your code
You can add OpenTelemetry to your application by manually inserting code snippets or by using automatic instrumentation for common libraries and frameworks.
#6about 3 minutes
Combining OpenTelemetry data with the Elastic stack
Elastic natively supports the OpenTelemetry protocol and schema, allowing you to collect, store, and visualize telemetry data in a centralized platform.
#7about 3 minutes
Visualizing application performance with an Elastic dashboard
A live demonstration shows how an OpenTelemetry-instrumented application sends data to Elastic, revealing metrics like latency, throughput, errors, and logs.
#8about 2 minutes
Why observability is critical for Python and AI applications
Adopting observability standards like OpenTelemetry is crucial for Python developers to monitor, debug, and optimize increasingly complex AI and LLM-based systems.
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