Low-code Python library to safely use notebooks in production: schedule workflows, generate assets, trigger webhooks, send notifications, build pipelines, manage secrets (Cloud-only)
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Updated
Feb 14, 2025 - Python
Low-code Python library to safely use notebooks in production: schedule workflows, generate assets, trigger webhooks, send notifications, build pipelines, manage secrets (Cloud-only)
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...
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
ETL with Jupyter Notebooks, Pandas, and Azure Cosmos DB
👾 my old deep learning notebooks (e.g., tensorflow examples, caffee, deep art, numpy)
Various Data Analytics Projects based On Statistics in form of Notebook.
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.
Sample notebooks on Azure Databricks for ETL
Jupyter Notebook ETL from AWS S3 bucket
./ PySpark OpenFoodFacts ETL: Extracts, transforms, and loads nutrition data for balanced weekly menus with SQLite3 integration.
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