This is a dimensional data warehouse that seeks to provide insights into the raw data that FEMA provides publicly for its Individual and Housing Program. I used Jupyter Notebook, Python (Pandas, NumPy, Pyodbc), and SQL to perform ETL on the dataset, loading the warehouse based on the schema I designed. I created visualizations using Tableau from…

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.

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

Repository containing the notebooks used on classes and projects done from the Udacity Data Engineer Nanodegree.

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.

Data Modeling With Postgres for Udacity's Data Engineering Program. Using Python in Jupyter Notebook.

Data Modeling With Apache Cassandra for Udacity's Data Engineering Program. Using Python in Jupyter Notebook.

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

An ETL project in Jupyter notebook that filters and analyzes app reviews from the play store using NLP

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

Google Colaboratory Notebook files to design ETL pipeline of Amazon music reviews and connection to AWS PostgreSQL database and analysis of the ratio of five star reviews as it relates to participation in the Vine program.

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

In this project ETL and Analysis is performed on Amazon Sales Data in notebook and Tableau. The raw data consisted of 5 files which was transformed into one Excel file.

A lightweight helper utility which allows developers to do interactive pipeline development by having a unified source code for both DLT run and Non-DLT interactive notebook run.

Crowd-Quest: ETL Journey for Crowdfunding Data is a repository showcasing the ETL (Extract, Transform, Load) process. It involves extracting data from Excel files, transforming it into CSV format, designing an ERD and database schema, and loading the data into PostgreSQL. Tools used: Jupyter Notebook, VSCode, PostgreSQL, Quick DBD, Excel.

Jupyter Notebook demonstrating ETL (Extract, Transform, Load) pipeline for bank market capitalization data.

This project focuses on cleaning traffic volume data using Python, Jupyter Notebook, Pandas, and NumPy. The goal is to preprocess the raw data and convert it into a clean CSV/JSON format for further analysis and visualization.