What is telemetry data?

Telemetry data provides essential insights into how your systems are performing. It is crucial for proactive problem-solving and making informed decisions. To achieve this, though, you can't just rely on raw data — you need to have access to real-time insights. This is what telemetry data gives you.

Telemetry is the process of collecting, transmitting, and analyzing data from remote sources such as servers, applications, and monitoring devices. This process can be broken down into the following steps:

1. Data collection: Sensors, software, and other monitoring tools gather data such as user behavior, performance metrics, and environmental conditions.
2. Data Transmission: Sending the collected data to a central system.
3. Analysis and monitoring: Once the data has been received, the data is then processed, analyzed, and displayed to provide insights into the monitored systems status, performance, and behavior.

In order to achieve this, you have to have the right telemetry data. This is all the raw data that will include things like logs, metrics, events, and any other traces that are created by systems, applications, or devices. Without this raw data, the analysis and monitoring won't be as useful or accurate.

Logs

Logs are lists of text records, logging discrete events that happen within a system or application. Each record will be timestamped, and provide detailed information for debugging and analysis. They're essential for debugging and troubleshooting, compliance reporting, understanding user behavior, and various types of auditing.

There are different types of logs, including application logs, system logs, network logs, and access logs. All of which are valuable telemetry data that can be used to paint a precise picture of what's happened, when it happened, and who (or what) was involved.

Traces

A trace is essentially the breadcrumb trail a request leaves as it moves through a system. It's a record of the path a request takes, showing the flow and precise timing across multiple services. This includes things like distributed application traces and end-to-end request traces in microservice architectures.

As telemetry data, these traces provide valuable data to help with performance optimization, understanding system dependencies, root cause analysis, and identifying bottlenecks.

Metrics

Metrics are the numerical measurements of a system, software, or application captured at runtime. These metrics make it possible to carry out quantitative analysis of performance over time, and include things like CPU usage, request rate, error rate, response time, and memory utilization.

In telemetry, these metrics play a big part in generating visibility for things like system health, performance, and behavior. This visibility can provide insights for a variety of tools, from performance monitoring and anomaly detection to things like capacity planning and SLA compliance monitoring.

Events

Events are records of notable occurrences or state changes within your system. They capture significant moments or changes that impact the system or business, and differ from logs by often including additional contextual information. These events will include things like user actions like logins and purchases, system state changes, and business specific events.

Because of the extra contextual data, events are vital for business intelligence, system health monitoring, compliance auditing, and triggering automations. It's also great for user behavior analysis, because you can see exactly what they did, and when.

Step 1: Define your goals

The first step in setting up your telemetry data is deciding exactly what you're trying to achieve. You need to identify specific objectives for collecting your telemetry data, such as improving system performance, enhancing your user experience, or maybe identifying security threats. A good way to do this is by aligning your telemetry goals with your existing business objectives.

Then you need to determine what key performance indicators (KPIs) and metrics you'll need to track these. For example, if your goal is to improve your system performance, you might want to track things like response times, error rates, and resource utilization. Another important consideration when deciding your telemetry goals is privacy and compliance. You should review relevant regulations (CCPA, GDPR, etc) and define guidelines for data collection that will meet these regulations and respect user privacy.

Step 2: Configure your tools

The next step is to select and configure the appropriate tools to meet your goals. You should choose telemetry collection tools that both help you do this, and integrate easily with your existing tech stack. This might include things like application performance monitoring tools (APM), log aggregators, or custom SDKs.

You need to implement collection mechanisms, integrating logging frameworks or monitoring agents into your applications, OpenTelemetry could be a good vendor-neutral option for this. This will likely involve adding code instrumentation, or configuring existing systems to receive the data you need.

To configure your collection parameters you'll need to set up data collection specifics like sampling rates, types of events to capture, and level of detail for your logs. Then configure data filtering to filter out sensitive information and anonymization to comply with privacy regulations.

Step 3: Send your data

Next you'll need to actually send your telemetry data. To start you'll need to implement transmission protocols for sending your data. This could be HTTPS (or HTTP) for web apps, MQTT for IoT devices, or even specialized protocols such as OpenTelemetry (OTLP). This includes making sure the transfers are secure with encryption and authentication mechanisms.

To handle network interruptions and ensure reliability, you should set up local data buffering and retry logic. You will also need to balance the volume and frequency of your data transmission to minimize the potential impact of application performance and network bandwidth.

Step 4: Store your data

You then need to choose the most appropriate storage solution to receive your telemetry data. You can configure different storage for different data types, or it can be much simpler to take a distributed storage approach using Elasticsearch and its cross-cluster search and replication to create a data mesh.

Next, you'll need to implement retention policies, defining how long different types of data should be stored. To decide this, consider both the analytical and regulatory requirements that apply to that data. Decisions around storage of your telemetry data will often require balancing the need for analytical performance versus the cost of storing it.

Another consideration is partitioning and indexing — you want to organize your storage to optimize queries performance using things like time-based partitioning and indexing. Backup and disaster recovery should also be in place to protect against data loss and make sure continuous access to telemetry data.

Step 5: Analyze your telemetry data

Finally, you need to be able to analyze and visualize your telemetry data. This includes developing dashboards and visualizations to represent the key metrics and trends. You also need to implement alerting systems so you're notified when metrics exceed predefined thresholds or when it detects anomalies.

To get the most out of your telemetry data, set up advanced analytics such as statistical analysis and machine learning to better identify patterns and predict future trends. This works especially well when you combine insights from different data types to get a comprehensive view of system activity and user behavior.

Here are a few of the key benefits you get from building effective telemetry processes using your data:

• Real-time visibility: By tracking and analyzing critical metrics and logs, you get instant insights into what's going well, and where you can make improvements.
• Proactive issue detection and resolution: Your telemetry data makes it possible to identify anomalies and patterns that could indicate potential problems, before they escalate.
• Improved operational efficiency: You're able to streamline your workflows and increase productivity by implementing automated monitoring and data collection.
• Faster troubleshooting: You detailed telemetry data means you can quickly identify the root cause of an issue, reducing mean time to resolution (MTTR)
• Enhanced decision-making: By having all the data you need available, you can make data-driven decisions and informed strategic choices about resource allocation, product development, and UX improvements.

In order to get all the benefits from your telemetry data, it's crucial to use a unified data platform to consolidate your telemetry data. This way you can bring together your data from various sources and reduce swivel chairing.

This consolidation not only gives you the above benefits, but also improves scalability and cost efficiency, while ensuring consistency across all your data processing. That's also why open standards like OpenTelementry Semantic Conventions (Otel SemConv) are so important. They make sure you have interoperability between your different tools and platforms.

As with any technology, using telemetry data comes with both benefits and challenges. The way to overcome these challenges is to understand them and make a plan on how to overcome them.

• Data security and privacy: The safety and privacy of your user and customer data is vital. Not just because it’s good practice, but also because it can have huge reputational and regulatory repercussions. To combat this, you need to implement robust security measures like encryption, access controls, and comprehensive logging. You also need to protect sensitive information, and maintain tight control over your data access.
• Legacy software integration: You need to include support for a range of data formats and sources by implementing flexible data ingestion. This makes it possible to incorporate telemetry data from both modern and legacy systems into a unified platform.
• Cost-effective storage: Tiered storage options and Information Lifecycle Management (ILM) policies mean you can optimize your storage costs while ensuring data accessibility. Hot‑warm‑cold architecture can also help you balance performance and cost-effectiveness.
• Open standards support: To ensure interoperability, use open standards like OpenTelemetry as early as possible. This allows you to future-proof your telemetry infrastructure and easily integrate with other tools and platforms.

Earlier in the guide we touched on OpenTelemetry, the open source observability framework that unifies data collection and standardizes telemetry data formats. It also gives you a vendor-neutral approach to telemetry that avoids lock-in, and is very scalable for high-volume data handling.

This fits very closely with our commitments to open standards at Elastic, which is why we've contributed our open source specification Elastic Common Schema (ECS) to OpenTelemetry.

We also include seamless ingestion of OTel-formatted data into Elasticsearch as part of our broader observability function. This means you can use the advanced Elasticsearch analytics and search with OTel data, and future-proof your observability platform.

This combination of Elastic and OpenTelemetry addresses a lot of the key challenges you're likely to face:

• Consolidating heterogeneous data sources
• Ensuring data security and privacy
• Managing legacy software integration
• Optimizing storage costs
• Handling diverse data types
• Supporting open standards for interoperability

• OpenTelemetry guide
• APM and OpenTelemetry: The key to unlocking insights from your logs
• OpenTelemetry integration
• Data building blocks for an observability solution