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.

In this comprehensive course, you will transform from a reactive data engineer into a proactive DataOps professional. You will learn to design, implement, and manage enterprise-grade observability stacks that catch errors before they impact production dashboards, machine learning models, or business decision-makers.

Who This Course Is For

• 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

What You Will Learn

• 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

Requirements

• 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

Final Hands-on Project Description

You will design and deploy an end-to-end DataOps Observability Framework for a simulated enterprise industrial ETL pipeline. Using Python, OpenTelemetry, and structured logging, you will inject telemetry points into raw data streams, build a circuit-breaker pattern that quarantines anomalous sensor data, map end-to-end column lineage, and configure an automated self-healing feedback loop that alerts operators while safely continuing processing.