Automate ETL Pipelines

Overview

Keeping location data up to date is critical, but manually updating databases every night does not scale. In this beginner-to-intermediate course (Python and SQL basics required), you will learn how to build, automate, and monitor a real-world geospatial ETL pipeline from start to finish. Through three progressive lessons, working through the practical scenario of nightly address updates, you will convert raw CSV data into spatial records using PostGIS, schedule reliable pipelines with Airflow, and analyze failures using logs and monitoring tools. Through short videos, hands-on learning, Coach-guided reflections, and scenario-based decision making, you will move beyond theory into applied data engineering. You are required to have basic Python skills, familiarity with CSV files, and introductory knowledge of databases and SQL. By the end of the course, you will have the confidence and skills to build production-ready geospatial pipelines that run automatically, recover from failures, and alert you when intervention is needed.

Syllabus

From CSV to Spatial Data

In this module, you will discover how raw CSV address data is transformed into spatially enabled database records. Starting from first principles, you will build confidence with ETL thinking and apply it by loading geospatial data into PostGIS. This module focuses on doing, not memorizing.

Automating Nightly Runs with Airflow

In this module, you will move from manual scripts to automated pipelines. You will learn how Airflow schedules, runs, and retries ETL jobs so your address updates arrive every night without manual intervention.

Monitoring, Logging, and Failure Handling

In this module, you will learn how to detect and respond to pipeline failures. You will add logging and monitoring, so issues are visible before downstream users are impacted.