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.

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

dtflw is a Python framework for building modular data pipelines based on Databricks dbutils.notebook API.

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.

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

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.

This is a Proof of Concept for an ETL (Extract Transform Load) set up, which can be run all within a Docker Container leveraging a PySpark-Notebook for the environment.

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.

This project extracts, transforms, and loads airline data into a MySQL database for further analysis in Tableau. The ETL pipeline is built using Python (pandas, SQLAlchemy) and runs inside a Jupyter Notebook.