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@ -14,25 +14,28 @@ Three conceptual steps are how most data pipelines are designed and structured:
* **Load**: processed data is transported to a final destination.
## Tutorials
## Tools & Code Samples
* [Data science resources](https://github.com/davidyakobovitch/data_science_resources).
* [Lorte data pipelining](https://github.com/instacart/lore).
* [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).
* [Data pipeline samples](https://github.com/aws-samples/data-pipeline-samples/tree/master/samples).
* [Awesome Scalability](https://github.com/binhnguyennus/awesome-scalability).
* [AWS Data pipeline samples](https://github.com/aws-samples/data-pipeline-samples/tree/master/samples).
## MOOCs
* [Coursera's Big Data Pipeline course](https://www.coursera.org/lecture/big-data-integration-processing/big-data-processing-pipelines-c4Wyd).
* [Udemy's Airflow for Beginners](https://www.udemy.com/airflow-basic-for-beginners/).
## Talks
* [Industrial Machine Learning Talk](https://www.youtube.com/watch?v=3JYDT8lap5U).
## Tutorials & Articles
#### 2019
* [How to Code Neat Machine Learning Pipelines](https://www.neuraxio.com/en/blog/neuraxle/2019/10/26/neat-machine-learning-pipelines.html).
## Enterprise Solutions
@ -57,6 +60,9 @@ Three conceptual steps are how most data pipelines are designed and structured:
* [Databrick data pipeline](https://databricks.com/blog/2017/03/31/delivering-personalized-shopping-experience-apache-spark-databricks.html).
## Talks
* [Industrial Machine Learning Talk](https://www.youtube.com/watch?v=3JYDT8lap5U).