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.
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Updated
Dec 7, 2022 - Python
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.
FastAPI ML API with CI/CD and AWS Deployment
ETL pipeline to analyze notebook listings on Mercado Livre using Scrapy, Pandas, SQLite, and Streamlit.
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.
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
A Python-based ETL pipeline notebook demonstrating how to extract, transform, and load data using pandas and SQLite.
Extract, Transform, and Load (ETL) to create pipeline on movie datasets using PostgreSQL, Python, Pandas, and Jupyter Notebook
Production-ready data pipeline with Jupyter notebook, SQLite database modeling (star schema), and automated ETL workflows for customer churn analysis and segmentation.
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.
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.
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