In this video we will cover how to perform Incremental data load with Python ETL pipeline. The incremental data load approach in ETL (Extract, Transform and Load) is the ideal design pattern. In this process, we identify and process new and modified rows since the last ETL run. Incremental data load is efficient in the sense that we only process a subset of rows and it utilizes less resources.
We will focus on Source Change Detection technique today.


Link to medium article on this topic: [ Ссылка ]

Change Data Capture approach video: [ Ссылка ]...

Insert on Conflict: [ Ссылка ]

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Videos in this series:
Build ETL pipeline: [ Ссылка ]
Automate ETL Pipeline: [ Ссылка ]
ETL Load Reference Data: [ Ссылка ]
ETL Incremental Data Load (Destination Change Comparison): [ Ссылка ]

SQL Database setup videos:
SQL Server setup: [ Ссылка ]
PostgreSQL setup: [ Ссылка ]

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#ETL #Python #IncrementalDataLoad

Topics covered in this video:
0:00 - Introduction to ETL Incremental load approach
0:41 - Source Setup
1:04 - Target Setup
2:33 Implement Incremental load approach with Python
5:17 - Test Pipeline with target data check

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