TELEMETRY
Darshika Srivastava
Associate Project Manager @ HuQuo | MBA,Amity Business School
WHAT IS TELEMETRY?
Telemetry is the automated process of measuring and wirelessly transmitting data from remote sources. It involves using sensors to collect electrical data (such as voltage and current) and physical data (such as temperature and pressure). This data is sent to remote locations for monitoring and analysis, enabling effective management and control of systems.
Why it matters for IT monitoring
Modern, dynamic distributed systems require comprehensive monitoring to understand software behavior in various situations. Customers face challenges tracking the software’s performance in the field and responding to various modifications. To keep up with digital advancements, it’s essential to have a simple way to collect data from various products and share it for analysis. This need led to the creation of telemetry and OpenTelemetry.
In this article, we will explore what telemetry is, how it works, and its significance for application monitoring. We will also discuss its benefits to customers and its impact on software development.
Key takeaways
Telemetry is the automated process of collecting and transmitting data from remote sources for monitoring and analysis.
It is crucial for IT monitoring, allowing developers to track software performance and identify issues.
The telemetry process involves specifying metrics, data transmission, data processing, and data analysis.
Telemetry data includes environmental, performance, and operational data, providing insights into system conditions and performance.
What does telemetry mean for application monitoring?
Telemetry is one of the latest ways to track software progress. It helps developers stay aware of the software’s performance and notifies them if a problem occurs with the application.
Software development is a great way to learn which features end-users prefer and which require modification. This system also notifies the developers if there is a problem with the application, like a bug. It gives the developers enough time and data to rectify the problem and introduce an even better version in the market. It is an important step in ensuring the end-user gets the best application version.
How does telemetry work?
Telemetry for application monitoring is a four-step process:
Step 1: Specifying the metrics
This step involves planning how the telemetry will occur. Metrics are specified for the developer’s process to track the performance of their application. The developers need to create a plan of action for what should happen if the given hypothesis is validated. This helps prioritize the work.
Then, the developer needs to specify the lifetime of the telemetric run. It depends on the cost and returns on the investment of the run. Once you have specified the life, you will give the formula the telemetrics would calculate. The developer needs to mention the necessary data points here.
Step 2: Data transmission
Telemetry requires tracking the user’s activity on your application. That makes it important for the developer to stay aware of privacy laws and regulations. For that purpose, the best action is to get the end user to sign up for telemetry data analysis.
You must also ensure data transmission and safety to make the process safe and secure. It should not be able to easily fall into the hands of hackers or any third-party users. Sometimes, when the device is connected to a metered network, it may incur charges. That is why waiting until the device is connected to an unmetered connection is better.
Step 3: Data processing
Once the data is transmitted and stored, it must be processed before it can be used. For that purpose, various mathematical formulas are run to make data digestible for the analysis. The method adopted for the data processing may depend upon the type of metrics the developer sets.
For metrics involving a formula, the step mainly applies the data to simplify the results. If required, the results are merged with other data sets. For exploratory metrics, a data mining approach is adopted. In this method, computer algorithms are run to find the hidden pattern in the data.
Step 4: Data analysis
The final step of the telemetric process is to perform the analysis of the data. Once the data has been collected and filtered into their respective data sets, the data is analyzed to see the performance of the software. In this step, all the issues and bugs with the application are reported and rectified by the developer.
Once the developer implements the action plan, the identified problem is removed from the application. It helps improve the application’s user experience, making it better and easier for the user.
What is telemetry data?
Telemetry data refers to the automatic collection and transmission of logs, metrics, events, and traces from remote sources using sensors and protocols. This data is sent to centralized systems for monitoring and analysis, enabling effective system management and control.
Telemetry data comes in various forms, each offering valuable insights for effective system monitoring and management. Here are the main types:
Environmental Data: Includes information about the physical conditions around a system or device, such as temperature, humidity, and air pressure. Monitoring environmental data is crucial for maintaining optimal operating conditions and preventing damage from adverse environmental factors. For example, tracking temperature and humidity in data centers helps ensure equipment runs safely and efficiently, extending its lifespan.
Performance Data: Focuses on how well a system is working. This data type includes CPU usage, memory utilization, network latency, and throughput metrics. By monitoring performance data, organizations can identify slowdowns, optimize resource use, and ensure systems run smoothly. For instance, monitoring CPU and memory usage in real time can prevent server overloads and avoid system crashes.
Operational Data: Relates to a system’s daily functioning and health. This category includes metrics like uptime, error rates, transaction volumes, and system alerts. Operational data is essential for ensuring systems are reliable and available. By monitoring these metrics, businesses can quickly spot and fix issues before they become major problems, ensuring continuous service. For example, tracking uptime and error rates helps IT teams maintain high service levels and meet their Service Level Agreements (SLAs).
Benefits of telemetry
Telemetry has become so ingrained with DevOps teams because it helps reduce their work. Once an application is introduced in the market, it gets scattered across various parts of the world. At that point, it becomes nearly impossible to keep track of all the applications and ensure they work optimally.
Telemetry gives you the tools to keep track of performance from all outlets. All of the important information regarding the application and its performance is reported to you at your place, no matter how remote the end user’s location is. This makes telemetry an ideal method for improving the application.
Here are a few benefits that telemetry provides the developers:
Reports the best and worst features
The telemetric analysis helps the developer learn which software features are the best. Through the analysis, developers will learn which software feature is frequently preferred by users in a region. It also helps the developer learn which features must be included in the next version to ensure the application is even more popular.
Telemetric analysis also helps users learn about the applications’ features they do not prefer. This gives the developers a chance to improve those features so that they can also be used frequently, which will help improve the application’s rating.
Reports the customer’s preferences
Telemetric analysis data informs the developers of the users’ preferred application settings, such as which display types they prefer. This makes it easier for the developers to modify the application based on the data to match the users’ needs.
It also informs the developers which users commonly use screen configuration and display backgrounds. That way, the developers can modify the application to become more compatible with that kind of screen orientation and device settings. When users find these settings in the application by default, they would surely prefer to use the application.
Reports the user’s engagement with the application
User engagement is a key metric determining an application’s rating. Applications that can keep users engaged for a long time with their content are more successful in application rating. That is why telemetry analysis of the application is important.
Through it, the developer can learn how much time the user spends on their application. Then, they can work towards improving that time by introducing interactive and engine content. By knowing the users’ favorite features, the developers can keep them engaged with their applications for a long time.
Reports the crashes
Each time an application crashes, it is instantly reported to the developer through telemetric data transmission. This information also provides insights into the crash context, identifying which apps were running in the background at the time of the crash. Additionally, it tracks the frequency of application crashes on a device.
This information aids the developer in identifying and rectifying the causes of crashes. If the data reveals that another application is responsible, the developer will ensure that their application remains stable when used at the same time as others. That is why the crash context is really important and provided to the developer by the telemetric data transmission.
“Telemetry automates real-time tracking of software performance, empowering developers to fix issues before users even notice.”
Telemetry challenges and considerations
Data privacy and security concerns
Handling large volumes of telemetry data requires a focus on data privacy and security to protect sensitive information. Ensuring that telemetry data is securely collected, transmitted, and stored is vital to prevent unauthorized access and breaches. Implementing strong encryption methods and adhering to privacy laws and regulations, such as GDPR, are essential practices to safeguard data.
Handling large volumes of telemetry data
Managing large volumes of telemetry data involves efficient data processing and storage solutions. Scalable cloud storage, data compression techniques, and real-time data processing frameworks help handle the influx of telemetry data. These methods ensure that the data is readily available for analysis without overwhelming system resources.
Ensuring data accuracy and reliability
Implementing data validation and cleansing processes is crucial to maintaining the accuracy and reliability of telemetry data. Regularly calibrating sensors and using redundancy checks can help detect and correct errors in the data. Ensuring that the data collected reflects the true state of the monitored systems enables accurate analysis and decision-making.
Advanced uses of telemetry
Telemetry data isn’t just for monitoring systems in real-time; it also has advanced uses that can significantly enhance system management and decision-making. Here are two key areas:
Predictive analytics
Telemetry data can be used in predictive analytics to foresee system failures before they happen. Organizations can predict and address potential issues by analyzing historical data, patterns, and trends. This minimizes downtime and keeps systems running smoothly.
AI and machine learning
Integrating telemetry data with AI and machine learning can enhance automation and decision-making. AI algorithms can analyze vast amounts of telemetry data to identify anomalies, optimize performance, and make real-time adjustments. Machine learning models can continuously learn from the data, improving their predictions and actions over time, leading to smarter and more efficient systems.
Future trends in telemetry
The future of telemetry holds significant potential across various sectors, driven by advancements in technology and increasing demands for real-time data analysis and automation. Here are some key trends to watch:
Practical applications of telemetry and real-world examples
Telemetry data is widely used across various industries, providing essential insights for monitoring and improving systems. Here are some practical applications and real-world examples from different sectors:
IT and software
In the IT industry, telemetry data is crucial for monitoring system performance, detecting anomalies, and ensuring optimal operation. For instance, cloud service providers use telemetry to track server uptime, CPU usage, and network latency. Analyzing this data, they can predict failures and automatically scale resources to maintain service reliability and performance.
Healthcare
Telemetry in healthcare involves monitoring patient vital signs remotely. For example, wearable devices collect data such as heart rate, blood pressure, and oxygen levels and transmit this information to healthcare providers for real-time monitoring. This allows for timely intervention in the event of any abnormalities, improving patient outcomes and reducing hospital visits.
Automotive
Telemetry is used for various testing and operational purposes in the automotive industry. Automotive telemetry systems can measure torque, stress, and temperature on critical components like driveshafts and brakes. For example, Accumetrics’ systems provide real-time torque measurements from vehicle drivelines, helping engineers optimize performance and ensure safety during high-stress conditions such as racing or heavy-load transportation.
Aerospace
Telemetry plays a vital role in aerospace, monitoring and analyzing the performance of aircraft components. Aerospace companies use telemetry data to track parameters such as engine temperature, vibration, and pressure. This data helps predict failures and schedule maintenance proactively. A notable example is NASA’s telemetry monitoring of rotor blade dynamics on helicopter models, improving flight safety and performance.
“By analyzing telemetry data, developers continuously improve software, ensuring it adapts to meet user needs and expectations.”
Conclusion
Telemetry has revolutionized software development and monitoring by simplifying the process of gathering and analyzing data from remote sources. It provides essential insights into system performance, environmental conditions, and operational health, enabling IT teams to identify and resolve issues proactively.
Advancements in IoT, AI, edge computing, and enhanced security measures will further enhance the power of telemetry. These technologies will allow for more sophisticated data collection and analysis, predictive maintenance, and improved decision-making.
Telemetry is indispensable for maintaining optimal system performance and driving continuous improvement. It helps developers learn from initial versions and improve subsequent ones, ensuring applications effectively meet user needs. As telemetry continues to evolve, it will remain a cornerstone of efficient and reliable IT management, essential for application monitoring.