Data Pipelines & ETL

Build and orchestrate reliable batch and streaming pipelines with Airflow, dbt and Kafka.

Introduction to Data Engineering

A four-hour intermediate DataCamp course that walks through databases, parallel processing, Airflow scheduling, and building a working ETL pipeline.

Airflow Python SQL

Introduction to Apache Airflow in Python

Data Pipelines & ETL

Hands-on Apache Airflow 3 course covering Dags, scheduling, sensors, monitoring, branching, and a sales ETL pipeline project in about 4 hours.

Airflow Python

Data Engineering on AWS - The complete training

Data Pipelines & ETL

A 19-hour Udemy course mapping the four AWS Certified Data Engineer Associate (DEA-C01) exam domains, from ingestion to security and governance.

AWS Python

Intermediate Data Importing in R

Data Pipelines & ETL

Master diverse data importing techniques in R, from databases to web sources, and enhance your data preparation skills.

Pipelines are where data engineering is judged, because everything downstream fails when they do. Courses here cover ETL and ELT design, incremental loading and change data capture, orchestration and scheduling with Apache Airflow, Dagster or Prefect, transformation and testing in dbt, and real-time streaming with Kafka, Flink or Spark Streaming. The better courses spend time on reliability rather than only the happy path: idempotency, retries and backfills, schema evolution, data quality testing, and lineage so you can trace a wrong number back to its source. Practical for anyone maintaining pipelines other people depend on.