15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
Session 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 pipelinesv
- 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
About the Course
This advanced training equips data engineers and technology professionals with the skills to design resilient, observable, and high-performing data pipelines that remain reliable under changing workloads and operational conditions. The course focuses on building robust data systems through effective observability, metrics, logging, tracing, monitoring, alerting, and systematic debugging.Participants will learn how to identify pipeline failures, diagnose data quality issues, detect performance bottlenecks, establish meaningful operational metrics, and implement proactive monitoring strategies. The training emphasizes practical approaches to improving pipeline reliability, availability, scalability, maintainability, and operational efficiency across modern data platforms.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Overview of pipeline monitoring and debugging
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Workshop 1.2:
Key challenges in pipeline reliability
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Workshop 1.3:
Observability vs monitoring vs alerting
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Workshop 1.4:
The 3 pillars of observability: logs, metrics, traces
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Workshop 1.5:
Understanding pipeline SLAs and SLOs
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Workshop 2.1:
Designing structured logs in ETL/ELT flows
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Workshop 2.2:
Integrating logging libraries in Python/Spark/Scala
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Workshop 2.3:
Logging with Airflow and Prefect
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Workshop 2.4:
Centralized log management tools (ELK, Fluentd, Loki)
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Workshop 2.5:
Logging best practices for debugging
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Workshop 3.1:
Identifying key metrics: latency, throughput, error rate
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Workshop 3.2:
Custom metrics from Apache Spark, Kafka, Flink
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Workshop 3.3:
Prometheus and Grafana for metric visualization
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Workshop 3.4:
Instrumenting custom data workflows
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Workshop 3.5:
Using metrics for anomaly detection
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Workshop 4.1:
Airflow DAG monitoring and sensors
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Workshop 4.2:
Failure alerts, retries, and SLA miss handlers
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Workshop 4.3:
Capturing task-level and DAG-level performance
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Workshop 4.4:
Detecting delayed or stuck jobs
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Workshop 4.5:
Monitoring data quality in batch jobs
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Workshop 5.1:
Kafka monitoring with Cruise Control and Confluent Control Center
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Workshop 5.2:
Flink and Spark Structured Streaming observability
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Workshop 5.3:
Lag monitoring and backpressure detection
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Workshop 5.4:
Streaming checkpoint and state health tracking
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Workshop 5.5:
Setting up alerts on consumer group performance
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Workshop 6.1:
Understanding the concept of distributed tracing
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Workshop 6.2:
Implementing OpenTelemetry in data stacks
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Workshop 6.3:
Tracing pipelines across multiple tools
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Workshop 6.4:
Visualizing traces to find performance bottlenecks
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Workshop 6.5:
Debugging cross-service failures
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Workshop 7.1:
Designing meaningful and actionable alerts
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Workshop 7.2:
Avoiding alert fatigue and false positives
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Workshop 7.3:
Setting thresholds based on baseline behaviors
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Workshop 7.4:
Using tools like PagerDuty, Slack, Opsgenie
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Workshop 7.5:
Alert routing and escalation policies
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Workshop 8.1:
Debugging failed DAGs or tasks in Airflow
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Workshop 8.2:
Analyzing logs and metrics to isolate issues
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Workshop 8.3:
Performing post-incident analysis
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Workshop 8.4:
Investigating resource constraints and data volume spikes
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Workshop 8.5:
Reducing MTTR (Mean Time to Resolution)
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Workshop 9.1:
ELK Stack for centralized observability
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Workshop 9.2:
Prometheus + Grafana for custom metrics
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Workshop 9.3:
Datadog, New Relic, Splunk, and Sentry
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Workshop 9.4:
Open-source options: Loki, Jaeger, OpenTelemetry
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Workshop 9.5:
Integrating tools into CI/CD
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Workshop 10.1:
Adding data validation checks in pipelines
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Workshop 10.2:
Schema evolution tracking with tools like Great Expectations
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Workshop 10.3:
Detecting nulls, duplicates, outliers, and drift
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Workshop 10.4:
Monitoring freshness and completeness
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Workshop 10.5:
Visualizing data quality metrics
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Workshop 11.1:
Writing unit and integration tests for pipelines
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Workshop 11.2:
Data mocking and test data generation
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Workshop 11.3:
Reproducing pipeline failures locally
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Workshop 11.4:
Debugging slow or memory-intensive flows
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Workshop 11.5:
Version control and rollback strategies
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Workshop 12.1:
Configuring automatic retries and fallbacks
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Workshop 12.2:
Circuit breakers and dead-letter queues
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Workshop 12.3:
Using state recovery and checkpoints
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Workshop 12.4:
Designing idempotent tasks
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Workshop 12.5:
Monitoring failed job trends and patterns
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Workshop 13.1:
Cloud-native observability in AWS, GCP, Azure
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Workshop 13.2:
Using CloudWatch, Stackdriver, Azure Monitor
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Workshop 13.3:
Kubernetes-based pipeline monitoring
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Workshop 13.4:
Monitoring cloud storage (S3, GCS, Blob) activity
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Workshop 13.5:
IAM issues and error tracing
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Workshop 14.1:
Monitoring pipeline cost with cloud cost dashboards
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Workshop 14.2:
Identifying inefficient queries or processes
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Workshop 14.3:
Using profiling tools to reduce resource usage
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Workshop 14.4:
Alerting on budget thresholds
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Workshop 14.5:
Rightsizing infrastructure for pipelines
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Workshop 15.1:
Design a robust batch or stream data pipeline
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Workshop 15.2:
Implement observability best practices
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Workshop 15.3:
Set up logging, alerting, and dashboards
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Workshop 15.4:
Simulate failures and perform root cause analysis
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Workshop 15.5:
Present monitoring outcomes and improvements
