Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training Datadog APM - Optimizing Application Performance training are: Mastery of Datadog fundamentals, experience with microservices and application instrumentation (Java, Python, or Node.js). A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Datadog APM - Optimizing Application Performance training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Training Datadog APM - Optimizing Application Pe... | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training Datadog APM - Optimizing Application Performance training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training Datadog APM - Optimizing Application Performance training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training Datadog APM - Optimizing Application Performance training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Master Datadog APM to analyze distributed traces in production

Configure advanced instrumentation for professional applications

Develop custom dashboards and alerts for the enterprise

Optimize application performance through real-time insights

Implement Datadog APM integrations with CI/CD and hybrid clouds

Acquire certifying skills in professional APM monitoring

Training program

Module 1Datadog APM: Advanced Configuration and Initial Instrumentation (agents, tracers, SDK)

In-depth installation and configuration of the Datadog APM agent in Kubernetes and Docker environments, automatic and manual instrumentation of critical applications using official libraries for Java, Python, and Go, initial trace testing on a concrete enterprise case, practical exercises to generate realistic data, validation of basic metrics, and debugging common pitfalls for smooth and high-performance deployment.

Module 2Datadog APM: Distributed Traces and Request Context (service maps, custom spans)

Exploration of end-to-end traces in microservices architectures, creation of custom spans to capture specific business metadata, building interactive service maps to visualize dependencies, hands-on workshops on a production-simulated red thread project, analysis of inter-service latencies with log-trace correlation, and optimization of sampling to handle high volumes without losing critical information.

Module 3Datadog APM: Bottleneck Detection and Diagnosis (flame graphs, CPU/memory profilers)

Use of integrated flame graphs and profilers to identify code hotspots in real time, analysis exercises on real failing applications, correlation with infrastructure metrics for holistic diagnostics, establishment of performance baselines and automated alert thresholds, practical cases on reducing response times by 40% through Datadog APM-guided refactoring, and documentation of insights for development teams.

Module 4Datadog APM: Dashboards, Alerts, and SLO Monitoring (advanced queries, notebooks)

Design of dynamic multi-view dashboards for application KPI tracking, definition of intelligent alerts based on anomalies and SLOs, use of collaborative notebooks for in-depth investigations, integration with Slack and PagerDuty for maximum responsiveness, workshops on real incident scenarios, customization of APM queries for business-specific metrics, and export/sharing of reports for team reviews.

Module 5Datadog APM: Advanced Integrations and Production Optimization (CI/CD, security, costs)

Integration of Datadog APM into GitHub Actions and Jenkins pipelines for observability as code, configuration of security and compliance rules in traces, optimization of retention and ingestion costs at scale, final exercises on the red thread project with staging deployment, code review and best practices for enterprise scalability, certification preparation, and post-training action plan for immediate ROI.