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The goal of this article is showing a real world use case for ELT batch pipeline, with Cloud Storage, BigQuery, Apache Airflow and Cloud Composer : The Extract part is managed in Cloud Storage The Load part is managed from Cloud Storage to BigQuery The Transform part is managed by a BigQuery SQL query Everything is orchestrated by Airflow

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tosun-si/teams-league-airflow-elt

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teams-league-airflow-elt

This project shows a real world use case with ELT pipeline using Cloud Storage, BigQuery, Airflow and Cloud Composer

The article on this topic :

https://medium.com/@mazlum.tosun/elt-batch-pipeline-with-cloud-storage-bigquery-orchestrated-by-airflow-composer-8bbfc80bf171

The video in English :

https://youtu.be/XT-xdEtN0dA

The video in French :

https://youtu.be/gPJDj97rK-I

Deploy the Airflow DAG in Composer with Cloud Build from the local machine

gcloud builds submit \
    --project=$PROJECT_ID \
    --region=$LOCATION \
    --config deploy-dag.yaml \
    --substitutions _FEATURE_NAME="team_league_elt",_COMPOSER_ENVIRONMENT="dev-composer-env",_CONFIG_FOLDER_NAME="config",_ENV="dev" \
    --verbosity="debug" .

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The goal of this article is showing a real world use case for ELT batch pipeline, with Cloud Storage, BigQuery, Apache Airflow and Cloud Composer : The Extract part is managed in Cloud Storage The Load part is managed from Cloud Storage to BigQuery The Transform part is managed by a BigQuery SQL query Everything is orchestrated by Airflow

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