By completing this course, learners will:

• Install and configure Fluentd.

• Collect logs from servers, containers, and apps.

• Use plugins for data parsing, filtering, and routing.

• Deploy Fluentd in Kubernetes for centralized logging.

• Build log pipelines for Elasticsearch, Kafka, and cloud storage.

• Apply best practices for reliability and observability.

Course Syllabus

Module 1: Introduction to Fluentd

• What is Fluentd?

• Fluentd vs Logstash vs Fluent Bit

• Core components and data flow

Module 2: Installation & Setup

• Installing Fluentd on Linux, Windows, and containers

• Fluentd configuration structure

• Running Fluentd with Docker

Module 3: Plugins & Data Flow

• Input plugins for log collection

• Filter plugins for parsing and transformation

• Output plugins for routing to destinations

• Buffering and retry mechanisms

Module 4: Log Routing & Processing

• Sending logs to Elasticsearch and Kibana

• Forwarding to Kafka and other message queues

• Exporting to AWS S3, GCP, and Azure

• Metrics and monitoring with Prometheus

Module 5: Fluentd in Containers

• Fluentd with Docker logging driver

• Centralized logging in Kubernetes

• Fluentd DaemonSet configuration

• Integrating with EFK (Elasticsearch-Fluentd-Kibana) stack

Module 6: Advanced Features

• Structured logging (JSON, logfmt)

• High-throughput pipelines

• Handling large log volumes with buffering

• Security and compliance considerations

Module 7: Real-World Projects

• Centralized logging system for microservices

• Kubernetes observability stack with EFK

• Data pipeline with Fluentd → Kafka → Spark

• Cloud-native logging to AWS/GCP/Azure

Module 8: Best Practices & Future Trends

• Scaling Fluentd clusters

• Comparing Fluentd with Fluent Bit

• Observability trends in cloud-native systems

• Future of logging in AI-driven monitoring

Learners will receive a Certificate of Completion from Uplatz, validating their expertise in Fluentd and modern log pipelines. This certification demonstrates readiness for roles in DevOps, SRE, and data engineering.

Fluentd skills prepare learners for roles such as:

• DevOps Engineer (logging & monitoring)

• Site Reliability Engineer (SRE)

• Cloud Engineer (observability pipelines)

• Data Engineer (log analytics pipelines)

• Monitoring & Observability Specialist

Fluentd is widely adopted in cloud-native, microservices, and enterprise systems, making it a highly valuable skill in modern observability.

1. What is Fluentd?
An open-source data collector for unifying logs and event data across systems.

2. How does Fluentd differ from Fluent Bit?
Fluentd is heavier and feature-rich, while Fluent Bit is lightweight and optimized for edge and container environments.

3. What are Fluentd’s main components?
Input plugins, filters, output plugins, and buffers.

4. How does Fluentd ensure reliability?
Through buffering, retries, and persistent queues.

5. What destinations can Fluentd send data to?
Elasticsearch, Kafka, S3, cloud services, and monitoring tools.

6. How is Fluentd used in Kubernetes?
As a DaemonSet to collect logs from all pods and nodes into centralized pipelines.

7. What is the EFK stack?
Elasticsearch + Fluentd + Kibana, a popular logging and visualization stack.

8. What are the benefits of Fluentd?

• Unified logging across environments

• Large ecosystem of plugins

• High scalability and flexibility

• Strong Kubernetes and cloud support

9. What are challenges with Fluentd?

• Higher resource consumption vs Fluent Bit

• Complex configuration for large pipelines

• Requires monitoring to prevent log loss

10. Where is Fluentd being adopted?
In Kubernetes clusters, microservices, enterprise logging systems, and cloud-native observability stacks.