A Jupyter notebook documentation of an ETL (extract -> transform -> load) data pipeline

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

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

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

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

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.

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.

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.

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.

Databricks ETL Pipeline for retrieving and processing NI TestStand test results, featuring a well-documented notebook for ETL operations, Data Lake for storage, Spark SQL+Python for transformations, and Power BI as the final visualization of factory metrics.

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.

Sentiment Analysis project that focuses on classifying the interactions of customers with support agents from different brands on X (formerly Twitter). The project is developed starting from an ETL process through advanced NLP techniques and ML models for classification, written in Python leveraging Jupyter Notebooks.

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.

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