Create YOUR OWN End-to-End ELT Pipeline with Apache Airflow Python S3 and Amazon Redshift!
Learn how to build a comprehensive end-to-end ELT (Extract, Load, Transform) pipeline using Apache Airflow, Python, Amazon S3, and Amazon Redshift!
This video tutorial will take you through the process of creating a robust ELT pipeline that can extract data from various sources, transform it into a usable format, and load it into a target system for analysis and reporting.
With Apache Airflow as the workflow management system and Python as the programming language, you'll be able to automate and schedule your ELT pipeline to run efficiently.
Additionally, you'll learn how to store and manage your data in Amazon S3, providing a scalable, load it to Amazon Redshift and secure solution for your data warehousing needs.
By the end of this video, you'll have a fully functional ELT pipeline that can help you make data-driven decisions and improve your organization's analytics capabilities.
Resources Mentioned:
Docker Videos - https://www.youtube.com/playlist?list=PLDtaSS0Aqlqo3kLIRlxgXBzK-VdHIDPm0
Airflow Videos - https://www.youtube.com/playlist?list=PLDtaSS0AqlqrAAgiH9xQNOrhhFXuh6wlO
AWS Tutorials - https://www.youtube.com/playlist?list=PLDtaSS0AqlqphYZyhuT9fVZBvw8eRpM98
DBT Tutorials - https://www.youtube.com/playlist?list=PLDtaSS0AqlqqaTvsH-o_hZfQ1LKYAouxN
GitHub Source Code:
https://github.com/kalekem/data_engineering/blob/main/airflow/dags/redshift_pipeline.py
#DataEngineering #Python #Airflow #AmazonS3 #AWS #Redshift #DataPipeline #ETL #CloudComputing #AWSRedshift #S3toRedshift #AirflowDAG #PythonAutomation #DataProcessing #DataExtraction #CloudDataPipeline #BigData
Learn how to build a comprehensive end-to-end ELT (Extract, Load, Transform) pipeline using Apache Airflow, Python, Amazon S3, and Amazon Redshift!
This video tutorial will take you through the process of creating a robust ELT pipeline that can extract data from various sources, transform it into a usable format, and load it into a target system for analysis and reporting.
With Apache Airflow as the workflow management system and Python as the programming language, you’ll be able to automate and schedule your ELT pipeline to run efficiently.
Additionally, you’ll learn how to store and manage your data in Amazon S3, providing a scalable, load it to Amazon Redshift and secure solution for your data warehousing needs.
By the end of this video, you’ll have a fully functional ELT pipeline that can help you make data-driven decisions and improve your organization’s analytics capabilities.
Resources Mentioned:
Docker Videos – https://www.youtube.com/playlist?list=PLDtaSS0Aqlqo3kLIRlxgXBzK-VdHIDPm0
Airflow Videos – https://www.youtube.com/playlist?list=PLDtaSS0AqlqrAAgiH9xQNOrhhFXuh6wlO
AWS Tutorials – https://www.youtube.com/playlist?list=PLDtaSS0AqlqphYZyhuT9fVZBvw8eRpM98
DBT Tutorials – https://www.youtube.com/playlist?list=PLDtaSS0AqlqqaTvsH-o_hZfQ1LKYAouxN
GitHub Source Code:
https://github.com/kalekem/data_engineering/blob/main/airflow/dags/redshift_pipeline.py
#DataEngineering #Python #Airflow #AmazonS3 #AWS #Redshift #DataPipeline #ETL #CloudComputing #AWSRedshift #S3toRedshift #AirflowDAG #PythonAutomation #DataProcessing #DataExtraction #CloudDataPipeline #BigData
