ETL using Python in Jupyter Notebook, loading CSV, cleaning data, and saving to SQL Database.

Jupyter Notebooks with different purposes: Social Network WebScrapping, ETL, Selenium WebDriver for Web Testing, Automation using Python, Data Wrangling, Data Transformation, Data Cleaning, Stock Market Analysis, APIs, Machine learning Algorithms, etc...

Jupyter Notebook showing how to process Telecom datasets using PySpark (SparkSQL and DataFrames) and plotting the results using Matplotlib.

Orchestration of data science and earth observation models in Apache Airflow, scale-up with Celery Executor, experiment with jupyter notebook using a docker containers composition

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.

A starter repository for your next AWS Glue project. This comes with complete IaC, a CD pipeline and a reusable common SDK. Set up jupyter notebook for AWS Glue locally

Various Data Analytics Projects based On Statistics in form of Notebook.

ETL with Jupyter Notebooks, Pandas, and Azure Cosmos DB

👾 my old deep learning notebooks (e.g., tensorflow examples, caffee, deep art, numpy)

This is a repository to hold the files and notebooks produced throughout my Udacity's Nanodegree Data Engineering program.

This repo contain all the notebooks and the code of the Data Science Mentorship Program offered by Campusx youtube channel.

Common ETL patterns and utilities for PySpark. Notebooks tested on Databricks Community edition

Data science encompasses a wide range of areas, topics, and sub-domains such as Big Data, Machine & Deep learning (ETL, TensorFlow, Keras), Data Mining/Visualization (EDA), BI, Predictive Analytics, Statistical Analytics, etc.

Using data extracted from Kaggle on the top restaurants from 2020, this project utilized Python scripting in Jupyter Notebook to transform and clean the data and finally, load the cleaned data frames into a PostgreSQL database.

python etl demo by jupyter notebook

My AI labs Jupyter notebooks repo

SEC Finance Data Engineering - ETL process for SEC Finance data of S&P 500 companies. Jupyter Notebooks to run ETL work flows. The final dataset is hosted in MongoDB Atlas(cloud). The API is written using Python with PyMongo and Flask libraries. The dashboards with charts are hosted in MongoDB Atlas.

Jupyter Notebook ETL from AWS S3 bucket

Sample notebooks on Azure Databricks for ETL