Amazing Prime loves the dataset and wants to keep it updated on a daily basis. We need to create an automated pipeline that takes in new data, performs the appropriate transformations, and loads the data into existing tables. We create one function that takes in the three files—Wikipedia data, Kaggle metadata, and the MovieLens rating data—and performs the ETL process by adding the data to a PostgreSQL database.
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Amazing Prime loves the dataset and wants to keep it updated on a daily basis. We create one function that takes in the three files Wikipedia data, Kaggle metadata, the MovieLens rating data and creates an automated pipeline that takes in new data, performs the appropriate transformations, and loads the data into existing tables.
Rl16193/Movies-ETL
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Amazing Prime loves the dataset and wants to keep it updated on a daily basis. We create one function that takes in the three files Wikipedia data, Kaggle metadata, the MovieLens rating data and creates an automated pipeline that takes in new data, performs the appropriate transformations, and loads the data into existing tables.
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