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@ -17,16 +17,25 @@ Three conceptual steps are how most data pipelines are designed and structured:
## Tools & Code Samples
* [Data science resources](https://github.com/davidyakobovitch/data_science_resources).
* [AWS Data pipeline samples](https://github.com/aws-samples/data-pipeline-samples/tree/master/samples).
#### Airflow
* [Incubator Airflow data pipelining](https://github.com/apache/incubator-airflow)
* [Awesome Airflow Resources](https://github.com/jghoman/awesome-apache-airflow).
* [Airflow in Kubernetes](https://github.com/rolanddb/airflow-on-kubernetes).
* [Lorte data pipelining](https://github.com/instacart/lore).
* [Astronomer: Airflow as a Service](https://github.com/astronomer/astronomer).
* [AWS Data pipeline samples](https://github.com/aws-samples/data-pipeline-samples/tree/master/samples).
#### Lorte
* [Lorte data pipelining](https://github.com/instacart/lore).
## MOOCs
#### General Pipelines
* [Coursera's Big Data Pipeline course](https://www.coursera.org/lecture/big-data-integration-processing/big-data-processing-pipelines-c4Wyd).
#### Airflow
* [Udemy's Airflow for Beginners](https://www.udemy.com/airflow-basic-for-beginners/).