OpenTelemetry is a new open-source project from Google that allows developers to collect telemetry data from their applications without having to write any code.
It’s a great way to get insight into how users interact with your app, and it also helps you identify areas where your application needs improvement.
This article will explain how to use OpenTelemetry visualization data. You will learn everything you need to know to start collecting telemetry data from your web apps and turning it into actionable insights through visualizations.
Types of Data in OpenTelemetry
OpenTelemetry metrics and data include the following:
- User interactions
- Application performance
- Network traffic
- System information
- Environment variables
User interactions refer to data related to user actions on your website or mobile app. These include clicks, taps, swipes, scrolls, form submissions, etc.
This data can be collected using different methods such as click tracking, scroll tracking, mouse movement tracking, etc.
This type of data relates to the performance of your application.
For example, if you are running an e-commerce site, then you might want to track the time taken for each page load, the number of requests made per second, average response times, etc.
These types of data help you understand what parts of your application take longer than others and why.
This type of data relates to network activity. For example, if your application uses HTTP/2 protocol, you might want data about the number of bytes sent over the wire, latency, throughput, etc.
This type of data is related to system information. For example, if there is a problem with your server, then you may want to see which part of the server has been affected by the issue.
These are environment variables that have been set by the user. For example, if the user has changed the value of a variable called “USER_NAME”, then you would want to know this.
Logs contain all the messages generated by your application. They can be written to files, databases, etc.
Metrics are measurements that relate to your application, for example, CPU usage, memory usage, disk space used, etc.
Events are events that occur within your application. Examples of events include when a user logs in, a user registers, a user purchases something, etc.
You can use these metrics to understand user behavior, diagnose problems, and improve your app.
For instance, you might want to see if there are spikes in network traffic during peak hours or if certain pages on your site have unusually high load times.
Or maybe you’d like to see which parts of your app are most popular and what user experience people get when they visit those pages.
Best Ways to Visualize Different Types of Data
This section will discuss some of the most common data visualization methods.
1) Bar charts
One of the most straightforward data visualization methods is using bar charts. They show numeric values on a horizontal axis (x-axis) and display them on a vertical axis (y-axis).
2) Line graphs
Line graphs are similar to bar charts, except instead of showing bars, they show lines. Each line represents a single value on the x-axis. OpenTelemetry data is shown on line graphs to make it easier to compare multiple values.
3) Pie charts
Pie charts are another way to represent data visually. Instead of displaying numerical values on the y-axis, pie charts show percentages as slices out of a circle.
Pie charts are helpful for visualizing OpenTelemetry data when the data has been aggregated into categories.
For example, if you’re tracking the total number of events that occur on your site, then you could use a pie chart to show the breakdown of those events.
4) Scatter plots
Scatter plots are similar to line graphs, except that they don’t show a single value on the y-axis; instead, they show multiple points on the graph. The benefit of scatter plots for OpenTelemetry data is that they allow you to identify trends within the data easily.
Histograms are useful for comparing the distribution of data across groups or ranges. They are often used to compare how many users visited a page during a specific period of time. This makes them a great help in visualizing OpenTelemetry data.
6) Interactive dashboards
Another common way that OpenTelelemetry data can be visualized is through interactive dashboards. These dashboards provide a graphical interface where you can quickly view and interact with large amounts of data.
Why use different visualizations to understand OpenTelemetry data?
The main reason why you would want to use different types of visualization techniques is that they all offer unique benefits. For example, a line graph works well for displaying temporal relationships, whereas a bar chart is great for highlighting size disparities between two data sets.
Furthermore, there are situations where only a specific visualization method would do the trick. For instance, while pie charts are great for showing percentages, they’re not as helpful when showing absolute values.
Generally speaking, you should pick the optimal visualization style based on the type of information you need to comprehend.
There are several OpenTelemetry data visualization tools available online. Some of these tools include:
This tool provides an easy way to create and share dashboards using OpenTelemetry data. This allows you to quickly analyze and visualize metrics from your application. You can also export your dashboard to PDF, HTML, PNG, SVG, JPG, GIF, and EPS files.
Tableau Public is an easy-to-use tool that lets you create custom dashboards using data from any source. The dashboard also allows for immediate data download for further offline inspection.
Google Analytics provides a variety of reports that can be modified to fit your needs. This tool is free and offers insights into your site or app performance.
Kibana is a web application that allows you to search, visualize, and aggregate data stored in Elasticsearch. It’s designed specifically for analyzing logs and other structured data.
Grafana is a powerful open-source analytics platform that helps you build beautiful interactive dashboards. It supports both SQL and NoSQL databases, such as MongoDB, MySQL, Cassandra, Redis, and more.
Graphite is a lightweight service that stores metrics and timestamps in a database. It’s designed to store large volumes of data efficiently.
Prometheus is a monitoring system that collects metrics from servers and applications. It’s built into Kubernetes, which means that you can monitor containerized applications running on Kubernetes clusters.
Visualizations Can Make Complex Problems Easy to Understand
One of the reasons why people like using visualizations is because they make complex problems easy to understand.
Information presented graphically is considerably more conducive to spotting trends and other patterns. This is particularly true when there is a large amount of data to process.
You may also construct an interactive dashboard to make your data easier to understand. If you’re having problems spotting trends in your data, using a dashboard can be helpful.
For example, you might create a dashboard listing the top ten pages on your website that receive the most traffic. Then, using filters, you may further focus your search by looking at the top five websites for each month.
This dashboard type can be used to examine past data and keep an eye on your website’s performance throughout the year.
When analyzing OpenTelemetry data, you’ll likely want to look at the same metrics over time. To accomplish this, you can create a dashboard that compares the data from two dates.
For example, suppose you wanted to know which pages were most popular on January 1, 2019, compared to January 1, 2018. You could create a dashboard that displays the top ten most popular sites on both days.
Then, you could filter the results to include only those pages with the highest traffic on either day.
You can also take things a step further by adding a chart that compares the number of visits per site between the two dates.
Visualizations speed up learning processes.
A lot of times, we learn best by seeing examples. When you’re learning something new, it helps if you can see what other people are doing before you start.
If you don’t already have a visual representation of the problem, then you may not be able to get started. However, once you’ve learned the basics, you can move on to more advanced concepts.
By having a visual representation of the concept, you can quickly grasp how the rest of the system works.
For example, if you’re working with a database and want to figure out how many rows are being inserted or deleted, you can easily create a table that lists the total number of records.
Similarly, you can create a graph that shows the number of updates performed during a specific period of time.
The key here is to keep the visuals simple enough that you can clearly see the pattern.
Visualizations help to clarify complex data sets.
Sometimes, you might have a large amount of data that doesn’t seem very useful. In these cases, you can use visualizations to help you better understand the data set.
For example, imagine you’re looking at a log file that contains all the errors that occurred while running a program. Without some sort of structure, it would be difficult to read the whole book.
In its place, you may create a visual that mixes different types of the same issue. This will make it easier to see recurring errors and understand their context. Remember that there is no one “right” way to display data.
Different types of visualizations work well depending on the context.
In general, you should try to choose a visualization that makes sense based on the information you’re trying to convey. This will help you focus on the relevant parts of the data set.
In addition, when you’re creating a visualization, you should consider how easy it is to interpret.
Varying your use of visualizations helps you compare data sets
When you’re comparing multiple data sets, it can be helpful to vary the type of visualization used. This will allow you to compare and contrast the various data sets and see their individual differences.
One use case is tracking an app’s performance over time for analysis. To visualize the average monthly response time, simply create a bar graph.
A line graph showing the evolution of the response time might be another option. Using this strategy, it is easy to see if the program has improved or deteriorated over time.
One thing to keep in mind is that you shouldn’t stick to just one method of visualizing your goals. Instead, experiment with several approaches until you find what works best for you.
It’s also good to remember that you need not stick to just one type of visualization. Instead, you can combine multiple techniques to create something unique.
For example, instead of showing the average response time for every month, you could show both the monthly averages and the standard deviation.
Visualizations are best kept simple.
It’s tempting to create a visualization with too many features. However, this will just confuse users and prevent them from understanding the underlying data.
If you’re unsure about what kind of visualization to use, start by thinking about the problem you’re trying to solve. Then, look up examples of how other people have solved the same problems.
Visualizations are crucial for processing, analyzing, and comprehending the complex data sets that arise in OpenTelemetry.
They allow us to identify patterns in our data and draw conclusions quickly. They provide a great starting point for more in-depth research.
You should check out TelemetryHub if you want an interactive, adaptive, and informative dashboard to help you better understand your observability and OpenTelemetry data.
TelemetryHub offers you a variety of concise and clear visual representations to help you quickly identify trends and gain actionable insights. Try TelemetryHub today and start your free trial.