# What is OpenTelemetry? A Comprehensive Guide
In today's world of distributed systems and microservices, understanding how
your applications behave in production is more challenging—and more
critical—than ever.
OpenTelemetry (OTel) has emerged as the industry's answer to this challenge by
offering a unified, open-source framework for collecting and managing
observability data.
It takes a vendor-neutral approach solves a fundamental problem in modern
observability: the fragmentation of telemetry collection methods. Instead of
maintaining different instrumentation code for each monitoring tool, you can
instrument once with OpenTelemetry and send your data anywhere.
In this comprehensive guide, we'll explore how OpenTelemetry works, its key
components, and how it can transform your observability strategy.
Whether you're just starting your observability journey or looking to modernize
your existing approach, this article will help you understand why OpenTelemetry
is becoming the backbone of modern observability.
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## What is telemetry data?
Telemetry data is the foundation of [observability](https://betterstack.com/community/guides/observability/what-is-opentelemetry/). It
consists of detailed measurements and records automatically collected from your
applications, infrastructure, and services that help you understand their
behavior, performance, and health.
The most common types of telemetry data include:
- **Metrics**: These include request rates and latencies, error counts, resource
utilization like CPU and memory usage, and business metrics such as active
users or transaction volumes. These are often plotted on dashboards and used
for alerting.
- **Traces**: A detailed record of a request's journey through your distributed
system. Traces show the exact path and timing of service calls, reveal
dependencies between services, highlight performance bottlenecks, and provide
context about each step in a request's lifecycle.
- **Logs**: Time-stamped records of discrete events that provide rich context
about system behavior. This encompasses application logs showing user actions
and system responses, error logs with stack traces, security and audit logs
tracking access patterns, and infrastructure logs from servers and network
devices.
- **Events**: Structured records of significant occurrences in your system.
Events capture the complete context of important transactions, state changes,
business processes, and deployment or configuration changes.
- **Profiles**: Deep analysis of resource consumption and code execution
patterns. Profiling data reveals CPU usage across function calls, memory
allocation patterns, lock contention issues, and I/O or network usage hotspots
to help optimize application performance.
The importance of telemetry has grown exponentially with the rise of
cloud-native architectures. Modern applications often span multiple services,
cloud providers, and data centers, making traditional monitoring approaches
insufficient.
By collecting and correlating these different types of telemetry data, you can
build a complete picture of your system's behavior and quickly respond to issues
when they arise.
## What is OpenTelemetry?
OpenTelemetry represents a significant milestone in observability tooling. It is
an open-source, vendor-neutral framework that standardizes how we collect and
handle telemetry data across distributed systems.
[It was born in 2019](https://www.cncf.io/blog/2019/05/21/a-brief-history-of-opentelemetry-so-far/)
through the merger of [OpenTracing](https://opentracing.io/) and
[OpenCensus](https://opencensus.io/)
Each project had unique strengths but also limitations that hindered broader
adoption. By combining their best features under the Cloud Native Computing
Foundation (CNCF), OpenTelemetry provides a unified, standardized framework for
collecting all kinds of observability signals, addressing the shortcomings of
its predecessors.
Today, OpenTelemetry stands as the
[second most active CNCF project](https://all.devstats.cncf.io/d/1/activity-repository-groups?orgId=1)
after Kubernetes, reflecting its critical role in modern observability.
### Key problems OpenTelemetry solves
OpenTelemetry addresses several critical challenges in modern observability.
Before its emergence, teams struggled with fragmented tooling and multiple
proprietary agents, leading to vendor lock-in and high maintenance overhead.
Different monitoring tools required different instrumentation methods, and data
consistency suffered as tools used varying formats and semantics.
To solve these challenges, **OpenTelemetry operates as a vendor-neutral,
language-agnostic platform** that maintains backwards compatibility with
existing implementations.
It's designed for high performance with minimal overhead in production
environments, while remaining extensible enough to accommodate specific use
cases.
It excels at collecting telemetry data from various sources, establishing
standardized formats and semantics, processing and enriching this data, and
ensuring reliable export to analysis tools.
Rather than trying to do everything, it deliberately leaves data analysis and
visualization to specialized tools, concentrating instead on being the most
efficient and reliable telemetry collection and processing pipeline possible.
By maintaining this focused scope while adhering to its core principles,
OpenTelemetry has emerged as the de facto standard for instrumenting
cloud-native applications.
This approach reduces implementation complexity, lowers maintenance costs, and
provides organizations with future-proof instrumentation that enables seamless
backend switching and comprehensive observability practices.
## Components of OpenTelemetry
The OpenTelemetry framework comprises of
[several components](https://opentelemetry.io/docs/concepts/components/) that
work together to capture and process telemetry data, which are outlined below:
### Specification and standards
At its heart lies the
[OpenTelemetry specification](https://opentelemetry.io/docs/specs/otel/), which
defines how the framework should be implemented across different environments.
This specification encompasses three crucial elements:
The **API specification** establishes the fundamental interfaces for creating
and handling telemetry data consistently across languages. The **SDK
specification** implements these interfaces, handling essential tasks like
sampling and data export. The **Data specification** defines the [OpenTelemetry
Protocol (OTLP)](https://betterstack.com/community/guides/observability/otlp/), ensuring standardized data transmission throughout the
ecosystem.
Supporting these specifications are **[semantic
conventions](https://betterstack.com/community/guides/observability/opentelemetry-semantic-conventions/)** which provide consistent
naming and structure for telemetry data. For instance, HTTP status codes,
database systems, and error types all follow standardized naming patterns,
making data correlation and analysis more straightforward.
### The data pipeline
[The OpenTelemetry Collector](https://betterstack.com/community/guides/observability/opentelemetry-collector/) serves as the central
nervous system for telemetry data. As a standalone service, it receives,
processes, and routes telemetry data between your applications and analysis
tools. Its general mode of operation is as follows:
First, it receives data through various receivers that support multiple formats,
from OTLP to Prometheus and Jaeger. Next, processors handle tasks like
filtering, enrichment, and transformations. Finally, exporters send the
processed data to your chosen analysis platforms.
The Collector comes in two major flavors: a streamlined
[core version](https://github.com/open-telemetry/opentelemetry-collector-releases/blob/main/distributions/otelcol)
(`otelcol`) for basic OTLP handling, and a
[contrib version](https://github.com/open-telemetry/opentelemetry-collector-releases/blob/main/distributions/otelcol-contrib)
(`otelcol-contrib`) packed with the whole kitchen sink.
For production use, its recommended that you [build a custom
flavour](https://betterstack.com/community/guides/observability/custom-opentelemetry-collector/) that includes only the components you
need.
Two key technologies enhance the Collector's capabilities. The
[Open Agent Management Protocol (OpAMP)](https://opentelemetry.io/docs/specs/opamp/)
enables centralized management of collector instances across large deployments,
while the [OpenTelemetry Transformation Language (OTTL)](https://betterstack.com/community/guides/observability/ottl/) provides powerful
data transformation capabilities within the collection pipeline.
### Protocol and data exchange
[The OpenTelemetry Protocol](https://betterstack.com/community/guides/observability/otlp/) provides the universal language that all
OpenTelemetry components use to communicate. It is a complete vendor-neutral
system for encoding, transporting, and delivering telemetry data between
different components within the observability pipeline.
While OTLP is OpenTelemetry's native protocol, the framework maintains broad
compatibility through its collector components, which can ingest data from
familiar formats like Zipkin, Prometheus, and Jaeger.
This flexibility allows you to gradually adopt OpenTelemetry without throwing
out your existing observability tools and workflows entirely.
### Learning through practice
[The OpenTelemetry Demo Application](https://betterstack.com/community/guides/observability/opentelemetry-demo/) brings these components
together in a practical context. It is a microservice-based shopping application
that demonstrates real-world instrumentation and observability practices, making
it an invaluable learning tool for anyone interested in adopting or integrating
with OpenTelemetry.
## The current state of OpenTelemetry
OpenTelemetry is a collaborative effort spanning multiple working groups, each
focused on different aspects of the framework. From telemetry signals, to
language-specific implementations, each component evolves at its own pace
through distinct maturity stages:
Components in OpenTelemetry progress through several stages:
- **Stable (GA)**: Production-ready with guaranteed long-term support and
backward compatibility.
- **Experimental (Alpha/Beta)**: Suitable for evaluation and proof-of-concept.
testing
- **Draft**: Early development stage.
- **Deprecated/Unmaintained**: End-of-life stages.
OpenTelemetry provides official support for major programming languages through
dedicated APIs and SDKs:
- Java
- JavaScript
- Python
- Go
- .NET
- C++
- Ruby
- PHP
- Erlang/Elixir
- Rust
- Swift
Beyond these officially supported languages,
[the OpenTelemetry Registry](https://opentelemetry.io/ecosystem/registry/) hosts
numerous community-developed SDKs and instrumentation libraries.
When evaluating OpenTelemetry for your project, consider these key factors:
1. **Technology stack**: Identify your programming languages and frameworks to
determine which client libraries and instrumentation agents you'll need.
2. **Signal requirements**: Determine which telemetry signals you need and their
sources (applications, infrastructure, third-party services) to note which
receivers you'll need in your collector pipeline.
3. **Observability backend**: Your chosen analysis tools (like
[Jaeger](https://betterstack.com/community/guides/observability/jaeger-guide/), [Prometheus](https://betterstack.com/community/guides/monitoring/prometheus/), or
[Better Stack](https://betterstack.com/telemetry) will guide your collector
exporter configuration.
Ensure to always check the current stability status of required components in
the [official documentation](https://opentelemetry.io/status/) before
implementation.
## OpenTelemetry signals and stability explained
OpenTelemetry's framework is built around three core telemetry signals -
[traces, metrics, and logs](https://betterstack.com/community/guides/observability/logging-metrics-tracing/) - with
[continuous profiling](https://github.com/open-telemetry/opentelemetry-proto-profile/pull/3)
in early development as the fourth.
Each signal serves a distinct purpose in understanding system behavior, and
their implementations have reached different levels of maturity across languages
and platforms.
In OpenTelemetry, **stability indicates a component's maturity and
production-readiness**. A stable component offers a well-defined API, schema,
and behavior that organizations can rely on without fear of disruptive changes.
However, stability can vary across specifications, semantic conventions,
protocols, and language implementations.
### Current signal status
| Language | Traces | Metrics | Logs |
| ------------- | ------ | ----------- | ----------- |
| C++ | Stable | Stable | Stable |
| C#/.NET | Stable | Stable | Stable |
| Erlang/Elixir | Stable | Development | Development |
| Go | Stable | Stable | Beta |
| Java | Stable | Stable | Stable |
| JavaScript | Stable | Stable | Development |
| PHP | Stable | Stable | Stable |
| Python | Stable | Stable | Development |
| Ruby | Stable | Development | Development |
| Rust | Beta | Beta | Beta |
| Swift | Stable | Development | Development |
[Distributed Tracing](https://betterstack.com/community/guides/observability/distributed-tracing/) was the first signal to reach General
Availability (GA) in September 2021. Tracing is now production-ready across most
supported languages, with comprehensive APIs for tracking request flows through
distributed systems. Only the Rust implementation remains in beta, while all
other languages offer stable tracing support.
[Metrics](https://betterstack.com/community/guides/observability/opentelemetry-metrics/) achieved general availability in 2021,
signifying that its API, SDK, and Protocol specifications are production-ready
for various programming languages. That said, development for full SDK stability
is still ongoing across the board.
[Logs reached stability in late 2023](https://github.com/open-telemetry/opentelemetry-specification/pull/3376),
completing OpenTelemetry's core observability trifecta. This milestone enables
seamless integration of logging with traces and metrics, though language support
is still evolving. Java, C++, .NET, and PHP lead with stable implementations,
while other languages are progressing through experimental and alpha stages.
For the most current stability information, always consult the
[OpenTelemetry documentation](https://opentelemetry.io/status/), as the project
maintains detailed status tracking for each component and language
implementation.
## Criticisms of OpenTelemetry
While OpenTelemetry has emerged as the de facto standard for observability
instrumentation, it's important to also understand its limitations and
challenges.
Like any ambitious open-source project, it has faced several criticisms that you
should consider before choosing to adopt it for your observability strategy.
Let's look at a few of this criticisms below:
### Too many moving parts
One of the primary criticisms of OpenTelemetry is its broad scope and
complexity. What began as as a simple idea around standardized observability
instrumentation has grown into a vast ecosystem with numerous concepts,
configurations, and dependencies.
The sheer number of SDKs, exporters, processors, and pipelines that need to be
configured can make it difficult to implement and scale efficiently, and it's
sure to require significant effort to maintain on an ongoing basis.
### Lack of stability
Despite being promoted as a standard, OpenTelemetry still lacks a cohesive,
stable implementation across different languages and frameworks. Features are
often in different stages of development or missing entirely in some SDKs.
This inconsistency creates frustration. Developers who expect a seamless
experience across languages are met with partially implemented APIs, evolving
specifications, and breaking changes between versions. The promise of
standardization feels hollow when you constantly have to work around
compatibility issues.
### Enterprise influence
Large tech companies significantly influence the project's direction, sometimes
prioritizing features that align with their commercial interests. This can lead
to feature bloat and complexity that may not serve the broader community's
needs.
### Documentation Gaps
While extensive, OpenTelemetry's documentation often lacks practical guidance
for common scenarios and troubleshooting. Critical information is scattered
across multiple repositories and websites, making it difficult to find
authoritative answers.
### Operational overhead
Managing OpenTelemetry at scale presents significant challenges. Configuration
management, capacity planning, and troubleshooting become increasingly complex
as deployments grow, requiring dedicated expertise and resources.
---
While many of the criticisms of OpenTelemetry are valid, they should be viewed
in the context of what the project is trying to achieve.
The project's extensibility and broad scope stems from its fundamental mission:
creating a universal standard for observability that works across diverse
technology stacks and organizational needs.
Features that may seem unnecessary for some organizations are often essential
for others, and this flexibility is what allows teams across different technical
environments to adopt a common observability framework.
Ultimately, the advantages of having a single, widely-adopted standard for
observability outweigh the challenges of its complexity. A common foundation
enables innovation in observability tooling and practices that would be
impossible in a fragmented ecosystem.
While OpenTelemetry may not be perfect, it is successfully delivering on its
core promise: providing a standard way to instrument, collect, and export
telemetry data across the industry.
## How to adopt OpenTelemetry in five steps
The journey to effective observability through OpenTelemetry requires careful
planning and a structured approach.
While the framework is powerful and flexible, successful adoption depends on
understanding your current environment and implementing changes methodically.
In essence, you need to:
- Define clear observability objectives.
- Assess your current technology landscape and component stability.
- Plan your migration path from existing solutions.
- Design a robust collection pipeline.
- Implement changes incrementally.
Let's explore how each of these steps in more detail.
### 1. Define your observability goals
Establish clear objectives that align with your business needs. This ensures
you're not collecting data simply because you can, but because it serves a
well-defined purpose. Identify which service interactions need tracing, what
metrics indicate system health, and which logs are essential for operations.
These decisions will influence your instrumentation strategy and help optimize
storage costs. Consider compliance requirements and [Service Level Objectives
(SLOs)](https://betterstack.com/community/guides/incident-management/sla-vs-slo-vs-sli/) that your observability solution needs to support.
### 2. Assessing your current telemetry
Begin your OpenTelemetry journey with a thorough evaluation of your technology
stack, including languages, frameworks, and existing monitoring tools.
Take inventory of your current telemetry sources, whether they're applications,
infrastructure components, or external services.
This is necessary because OpenTelemetry's stability varies by language and
signal type. You can also checkout the [OpenTelemetry Demo](https://betterstack.com/community/guides/observability/opentelemetry-demo/)
to see how the various components of OpenTelemetry comes together in a near
real-world scenario.
### 3. Evaluate migration paths
Map your transition strategy based on your current tooling. If you're using
OpenCensus or OpenTracing, you can leverage OpenTelemetry's backward
compatibility to migrate gradually.
For vendor-specific implementations, assess the effort required for
re-instrumentation against the benefits of a vendor-neutral solution. This
evaluation helps you understand the resource requirements and potential risks of
migration, allowing you to plan accordingly.
### 4. Design your collection pipeline
Your telemetry pipeline design should account for both current needs and future
scale. Consider factors like data volume, retention requirements, and
performance impact when choosing between different collector deployment patterns
(agent, gateway, or both).
Select receivers based on your data sources and exporters that match your
analysis tools. This architecture planning ensures your observability solution
can grow with your system without becoming a bottleneck.
### 5. Implementation strategy
Start with automatic instrumentation where available, as it provides immediate
visibility with minimal effort. Then gradually expand to custom instrumentation
for business-critical paths where deeper insights are needed.
Validate that the collected data answers important questions about system
behavior and performance. Set clear success criteria for each phase of the
rollout to ensure your implementation delivers value incrementally.
This phased approach helps manage risks while building expertise in your team
and proving the value of OpenTelemetry to stakeholders. Each step builds on the
previous one, creating a solid foundation for comprehensive observability.
## Final thoughts
OpenTelemetry represents a significant shift in how we approach observability.
By providing a standardized, vendor-neutral framework for telemetry collection,
it offers a future-proof foundation for your observability needs.
While the journey to implementing OpenTelemetry may seem daunting, the benefits
of standardized instrumentation and vendor independence make it a compelling
choice for modern observability strategies.
Remember that successful adoption is a gradual process. Start small, focus on
your immediate observability needs, and expand your implementation as your
expertise grows.
For more insights into OpenTelemetry's capabilities or to contribute to the
project, explore the [official website](https://opentelemetry.io/) and
[GitHub Repository](https://github.com/open-telemetry). You can also see our
[observability](https://betterstack.com/community/guides/observability/) guides for more in-depth articles on various
OpenTelemetry components.
Thanks for reading!