In today's data-intensive landscape, organizations depend on real-time and batch data pipelines for critical decision-making. When these pipelines fail or degrade, the consequences can include data loss, latency, poor insights, and compliance issues. The Monitoring and Debugging Data Pipelines training course is designed to equip data engineers, DevOps professionals, and analytics teams with the skills and tools required to ensure robust pipeline observability, proactive issue detection, and efficient troubleshooting. This hands-on course explores pipeline monitoring strategies across modern data stacks, focusing on tools like Airflow, Prefect, Spark, Kafka, and cloud-native observability platforms. Participants will master logging, tracing, metrics collection, anomaly detection, and root cause analysis to build resilient, self-healing pipelines that deliver trustworthy data.

Duration: 10 Days

Target Audience

• Data Engineers
• DevOps Engineers
• ETL Developers
• Platform Reliability Engineers
• Cloud Infrastructure Engineers
• Analytics Engineers
• SRE and Observability Teams
• Pipeline QA/Test Engineers

Course Objectives

• Understand the importance of observability in data pipelines
• Design pipelines with built-in monitoring and alerting capabilities
• Implement logging and metric collection across data workflows
• Detect anomalies and performance bottlenecks in pipelines
• Apply debugging techniques for failed or degraded pipelines
• Monitor batch and streaming data systems
• Set up dashboards and visual observability with modern tools
• Utilize cloud-native and open-source monitoring platforms
• Automate issue detection and recovery mechanisms
• Ensure end-to-end data reliability and uptime
• Improve communication between data, infra, and business teams

Course Modules

Module 1: Introduction to Pipeline Observability

• Overview of pipeline monitoring and debugging
• Key challenges in pipeline reliability
• Observability vs monitoring vs alerting
• The 3 pillars of observability: logs, metrics, traces
• Understanding pipeline SLAs and SLOs

Module 2: Logging Strategies for Data Pipelines

• Designing structured logs in ETL/ELT flows
• Integrating logging libraries in Python/Spark/Scala
• Logging with Airflow and Prefect
• Centralized log management tools (ELK, Fluentd, Loki)
• Logging best practices for debugging

Module 3: Metrics and KPIs for Pipeline Health

• Identifying key metrics: latency, throughput, error rate
• Custom metrics from Apache Spark, Kafka, Flink
• Prometheus and Grafana for metric visualization
• Instrumenting custom data workflows
• Using metrics for anomaly detection

Module 4: Monitoring Batch Pipelines

• Airflow DAG monitoring and sensors
• Failure alerts, retries, and SLA miss handlers
• Capturing task-level and DAG-level performance
• Detecting delayed or stuck jobs
• Monitoring data quality in batch jobs

Module 5: Monitoring Stream Processing Pipelines

• Kafka monitoring with Cruise Control and Confluent Control Center
• Flink and Spark Structured Streaming observability
• Lag monitoring and backpressure detection
• Streaming checkpoint and state health tracking
• Setting up alerts on consumer group performance

Module 6: Distributed Tracing in Data Pipelines

• Understanding the concept of distributed tracing
• Implementing OpenTelemetry in data stacks
• Tracing pipelines across multiple tools
• Visualizing traces to find performance bottlenecks
• Debugging cross-service failures

Module 7: Alerting and Notifications

• Designing meaningful and actionable alerts
• Avoiding alert fatigue and false positives
• Setting thresholds based on baseline behaviors
• Using tools like PagerDuty, Slack, Opsgenie
• Alert routing and escalation policies

Module 8: Root Cause Analysis Techniques

• Debugging failed DAGs or tasks in Airflow
• Analyzing logs and metrics to isolate issues
• Performing post-incident analysis
• Investigating resource constraints and data volume spikes
• Reducing MTTR (Mean Time to Resolution)

Module 9: Monitoring Tools and Platforms

• ELK Stack for centralized observability
• Prometheus + Grafana for custom metrics
• Datadog, New Relic, Splunk, and Sentry
• Open-source options: Loki, Jaeger, OpenTelemetry
• Integrating tools into CI/CD

Module 10: Data Quality and Validation Monitoring

• Adding data validation checks in pipelines
• Schema evolution tracking with tools like Great Expectations
• Detecting nulls, duplicates, outliers, and drift
• Monitoring freshness and completeness
• Visualizing data quality metrics

Module 11: Pipeline Testing and Debugging Best Practices

• Writing unit and integration tests for pipelines
• Data mocking and test data generation
• Reproducing pipeline failures locally
• Debugging slow or memory-intensive flows
• Version control and rollback strategies

Module 12: Handling Failures and Retry Mechanisms

• Configuring automatic retries and fallbacks
• Circuit breakers and dead-letter queues
• Using state recovery and checkpoints
• Designing idempotent tasks
• Monitoring failed job trends and patterns

Module 13: Monitoring in Cloud-Native Environments

• Cloud-native observability in AWS, GCP, Azure
• Using CloudWatch, Stackdriver, Azure Monitor
• Kubernetes-based pipeline monitoring
• Monitoring cloud storage (S3, GCS, Blob) activity
• IAM issues and error tracing

Module 14: Cost Monitoring and Performance Optimization

• Monitoring pipeline cost with cloud cost dashboards
• Identifying inefficient queries or processes
• Using profiling tools to reduce resource usage
• Alerting on budget thresholds
• Rightsizing infrastructure for pipelines

Module 15: Final Project: Build and Monitor a Data Pipeline

• Design a robust batch or stream data pipeline
• Implement observability best practices
• Set up logging, alerting, and dashboards
• Simulate failures and perform root cause analysis
• Present monitoring outcomes and improvements

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.