DataOps Foundations: Monitoring And Observability For ETL

This course contains the use of artificial intelligence.

Stop fighting data pipeline fires and start building reliable, self-healing data systems. Modern data platforms require more than basic status alerts—they require deep observability, proactive monitoring, and automated feedback loops.

Course Curriculum

• Data Engineers seeking to build bulletproof ETL/ELT pipelines and lower pipeline downtime

• DataOps Engineers and Platform Architects managing critical enterprise data infrastructure

• Analytics Engineers looking to incorporate software engineering reliability practices into dbt and SQL workflows

• Technical Leaders wanting to establish operational excellence and clear SLAs across data teams

• Master the fundamental pillars of DataOps telemetry: Freshness, Quality, Volume, Schema, and Lineage

• Differentiate clearly between basic pipeline monitoring and full system observability

• Implement OpenTelemetry distributed tracing across complex PySpark, Airflow, and dbt pipelines

• Build automated circuit breakers to quarantine bad data before it pollutes data warehouses

• Architect self-healing feedback loops that recover from transient platform errors automatically

• Enforce schema-drift management and automated data quality assertions in real time

• Leverage column-level data lineage to reduce Mean Time to Resolution (MTTR) from hours to minutes

• Apply enterprise observability strategies to mission-critical industrial ETL workloads

• Basic understanding of data engineering concepts and ETL/ELT pipelines

• Familiarity with Python and basic SQL queries

• Conceptual understanding of orchestration tools like Apache Airflow or dbt is helpful, but not required