Perform the Extract, Transform and Load (ETL) process to create a data pipeline on movie datasets using Python, Pandas, Jupyter Notebook and PostgreSQL.

Used Sql, Pandas, and Jupyter Notebook to model, engineer and analyze employee data. Database created with PgAdmin4.

Practice examples using Google Colab Notebooks: working with big data

Extract, Transform, and Load (ETL) to create pipeline on movie datasets using PostgreSQL, Python, Pandas, and Jupyter Notebook

Big Data Analysis on an Amazon Vine Review Dataset using pgAdmin, PostgresSQL, Python, Pandas, Pyspark, AWS, Google Colab Notebook.

Created a data pipeline from movie datasets using Python, Pandas, Jupyter Notebook and PostgreSQL. Implemented (ETL) - Extract, Transform, Load - to complete

Big Data using PySpark, AWS, pgAdmin, postgreSQL, and Google Colab Notebooks to analyze if there any bias towards paid Amazon Reviews.

Perform the Extract, Transform and Load (ETL) process to create a data pipeline on movie datasets using Python, Pandas, Jupyter Notebook and PostgreSQL.

Perform the Extract, Transform and Load (ETL) process to create a data pipeline on movie datasets using Python, Pandas, Jupyter Notebook and PostgreSQL.

Performed the Extract, Transform and Load (ETL) process to create a data pipeline on movie datasets using Python, Pandas, Jupyter Notebook and PostgreSQL.

Perform the Extract, Transform and Load (ETL) process to create a data pipeline on movie datasets using Python, Pandas, Jupyter Notebook and PostgreSQL.

Used Pandas to extract movie data from Kaggle and web scraping, clean data on Jupyter notebook, and load data on PostrgeSQL and PgAdmin.

Performed the Extract, Transform and Load (ETL) process to create a data pipeline on movie datasets using Python, Pandas, Jupyter Notebook and PostgreSQL.

Analysis of the reviews are utilize Google Colaboratory Notebook to perform ETL, connect to an AWS RDS instance and load the transformed data into pgAdmin.

Extracted movie data from Kaggle and Wikipedia, transformed the data to be usable for a hackathon competition using Pandas and Jupyter Notebook, and loaded the data to SQL in PGAdmin 4 and merged the datasets.