Monitoring Tooling
This digital credential validates your knowledge of monitoring MongoDB deployments. It recognizes your understanding of MongoDB's monitoring tools, ability to interpret key performance metrics such as CPU usage and query execution times, and ability to configure alerting mechanisms to capture critical events.
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Upon completion of the Monitoring Tooling skill and assessment, you will earn a Credly Badge that you are able to share with your network. |
Learning Objectives
Use monitoring tools for MongoDB
List, describe, and use some of MongoDB's built-in monitoring tools.
Use performance metrics
Interpret key hardware performance metrics such as CPU usage, memory consumption, and query execution times.
Configure alerting mechanisms for alerting best practices
Set up alerts based on performance thresholds to capture critical events and proactively manage database health.
Who is this Course Good for?
This skill is designed for developers and operations engineers who are responsible for keeping MongoDB deployments healthy, responsive, and predictable in production. If you work on applications where database performance and uptime directly affect user experience, this Monitoring Tooling Skill Badge will help you build a structured approach to MongoDB monitoring. It is especially valuable if you are building a proactive monitoring strategy and want to understand which metrics truly matter, how to interpret them, and how to connect them to concrete performance outcomes. Whether you manage MongoDB Atlas clusters or self-hosted environments, this course will help you use MongoDB monitoring tools to gain real-time visibility into database behavior, detect issues before they escalate, and support data-driven performance tuning.
What to Expect in this Course
In this skill badge, you will learn why monitoring is a critical part of running MongoDB in production and how it supports both application reliability and query performance. The skill begins by introducing key MongoDB metrics that provide a real-time pulse on what is happening in your deployment. You explore how tracking the right metrics helps you keep query response times as performant as possible and why a good monitoring strategy is essential for maintaining a positive user experience. From there, you examine the core elements of a robust monitoring plan: which metrics to track, how to configure alerts for critical events, and how to define clear response workflows when something goes wrong.
You then dive into MongoDB Atlas monitoring capabilities, learning how to use the Atlas Metrics and Real-Time panels to monitor query-specific metrics alongside server resources such as CPU, memory, and I/O that influence MongoDB performance. You see how these views help you correlate slow queries with underlying resource constraints and identify trends before they impact users. The skill then introduces Atlas Query Insights, showing you how to investigate underperforming queries, understand their patterns, and prioritize which ones to optimize.
To broaden your toolkit beyond the application level, you step back and look at MongoDB from the cluster perspective. You learn how to use command-line tools like mongostat and mongotop to monitor activity across databases and collections, giving you additional context on throughput, lock behavior, and read/write patterns. You also get hands-on with higher-level MongoDB commands such as db.serverStatus(), db.stats(), and db.collStats(), which surface detailed information about server operations, database health, and collection-level statistics. These tools and commands together provide a comprehensive view of how your MongoDB deployment is performing under real workloads.
Finally, the skill focuses on turning observations into action. You learn how to configure alerts in MongoDB Atlas so critical events trigger timely notifications, and you explore proactive strategies such as smart indexing to optimize query performance from the start. You also look at how to identify and handle outlier queries whose behavior does not match typical patterns but can still degrade overall performance. Hands-on labs give you the opportunity to apply monitoring concepts to realistic scenarios so you can confidently use MongoDB monitoring tooling to support performance, reliability, and operational excellence.
Summary of the Course
- Understand why monitoring is essential for MongoDB performance, stability, and user experience.
- Identify key MongoDB metrics to track for query performance and system health.
- Use MongoDB Atlas Metrics and Real-Time panels to monitor query behavior and server resource utilization.
- Investigate slow or underperforming queries using Atlas Query Insights.
- Monitor MongoDB clusters with command-line tools like mongostat and mongotop to view activity across databases and collections.
- Retrieve detailed operational, database, and collection statistics using db.serverStatus(), db.stats(), and db.collStats().
- Configure alerts and logging in MongoDB Atlas to capture critical events and support timely incident response.
- Apply proactive strategies such as smart indexing and outlier detection to optimize MongoDB performance and prevent issues before they impact users.
Sequoyha Pelletier | Senior Technologist, Education
Sequoyha Pelletier is a Senior Technologist at MongoDB, bringing over 15 years of experience in technical curriculum development and delivery. Before joining MongoDB, he worked in the Worldwide Support team for DataStax, eventually leading the curriculum team for new hire onboarding.
Sequoyha is extremely passionate about providing quality education for free to those in need and enjoys pushing the boundaries of what is considered "normal" practices with delivering educational content.
Sarah Evans | Senior Curriculum Engineer
Sarah is a Senior Curriculum Engineer on the Curriculum team at MongoDB. Prior to MongoDB, she taught and developed curricula for developer bootcamps. Sarah has a MAT degree from Columbia University Teachers College and studied Software Engineering at Flatiron School in Chicago, IL.
Manuel Fontan Garcia | Senior Technologist, Education
Manuel is a Senior Technologist on the Curriculum team at MongoDB. Previously he was a Senior Technical Services Engineer in the Core team at MongoDB. In between Manuel worked as a database reliability engineer at Slack for a little over 2 years and then for Cognite until he re-joined MongoDB. With over 15 years experience in software development and distributed systems, he is naturally curious and holds a Telecommunications Engineering MSc from Vigo University (Spain) and a Free and Open Source Software MSc from Rey Juan Carlos University (Spain).
Daniel Curran | Senior Software Engineer
Daniel is a Senior Software Engineer at MongoDB. Before joining MongoDB, he worked as an Instructional Designer and Content Developer specialising in technical content for a host of international clients. Daniel's goal is to remove obstacles so learners can feel confident on their journey to become masters of MongoDB.
John McCambridge | Curriculum Engineer
John is a Curriculum Engineer on the University team at MongoDB. Before his work as a Curriculum Engineer, he was an instructor and teaching assistant for coding boot camps at UT (Austin), and UCLA. Additionally, he worked as a QA engineer for a startup called Coder and spent five years at Apple Inc. John is a passionate software engineer and educator who enjoys taking complex topics and making them digestible for the community.
In this skill, you'll learn to identify key metrics that give us a real time pulse on what's happening.
One benefit of understanding key metrics is that it helps to keep query response times as performant as possible.
And to tie it all together, we'll outline the core elements of a good monitoring strategy, what metrics to track, how to set up alerts for when things go sideways, and what our response plan should look like. From there, we'll learn how to use the MongoDB Atlas metrics and real time panel to actively monitor query specific metrics, as well as our server resources that have an impact on our query performance.
Next, we'll explore how to use Atlas query insights to dig into our underperforming queries.
Then we'll take a step back from the application level metrics and look at things from the cluster level by using command line tools like Mongo Step and mongotop to monitor MongoDB.
These tools provide a different perspective, showing us the activity across all databases and collections.
After that, we'll get familiar with higher level MongoDB commands like server status, stats, and call stats.
These commands give us a wealth of information about server operations, database health, and collection specifics.
Next, we'll put it all together by learning how to set up alerts in Atlas.
When you're finished, you'll be ready to put your new skills to the test. To earn your badge, simply complete all the related content and then take the short test at the end. After passing the test, you'll receive an official Qredly badge via the email you provided.
Be sure to share your badge on LinkedIn to show off your new skills.
By completing this skill badge, you'll have a working understanding of monitoring tools in MongoDB.
Let's get started.
