Perform the Extract, Transform and Load (ETL) process to create a data pipeline on movie datasets using Python, Pandas, Jupyter Notebook and PostgreSQL.
Common ETL patterns and utilities for PySpark. Notebooks tested on Databricks Community edition
This project aims to demonstrate the process of ETL (Extract, Transform & Load) using Python and SQL. It involves extracting data from multiple sources, cleaning and transforming the data using Jupyter Notebook with pandas, numpy, and datetime packages, and loading the cleaned data into a relational database using pgAdmin.
Perform the Extract, Transform and Load (ETL) process to create a data pipeline on movie datasets using Python, Pandas, Jupyter Notebook and PostgreSQL.
Created a data pipeline from movie datasets using Python, Pandas, Jupyter Notebook and PostgreSQL. Implemented (ETL) - Extract, Transform, Load - to complete
Performed the Extract, Transform and Load (ETL) process to create a data pipeline on movie datasets using Python, Pandas, Jupyter Notebook and PostgreSQL.
This project focuses on scraping data related to Japanese Whiskey from the Whiskey Exchange website; performing necessary transformations on the scraped data and then analyzing & visualizing it using Jupyter Notebook and Power BI.
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