Extract Transform Load for Python 3.5+
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Updated
May 12, 2023 - Python
Extract Transform Load for Python 3.5+
A utility library for comparing and synchronizing different datasets.
PREVIEW - SQL databases in Bonobo, using sqlalchemy
This project demonstrates how to build and automate an ETL pipeline using DAGs in Airflow and load the transformed data to Bigquery. There are different tools that have been used in this project such as Astro, DBT, GCP, Airflow, Metabase.
PREVIEW - Run Bonobo data processing graphs in docker containers.
open-source ETL pipeline for HEX cryptocurrency data
Archive. See Datatractor Yard, below:
Lien de l'application
A bundle for zipline-reloaded to allow data for crypto assets to be ingested from Tardis
PRE-ALPHA - Write web crawlers using Bonobo
Extract, Transform, and Load script for fetching new data from the NYC Open Data Portal's vehicle collision data and loading into the NYC Crash Mapper table on CARTO.
Python module for extracting, transforming and loading data
This is an Extract, Transform, Load (ETL) project of unstructured Airline Billing and Settlement Plans (BSP) data
Archive of MaRDA Metadata Extractors Schema. See Datatractor Beam, below, for the current repository.
In this project, I've created an end-to-end ETL pipeline and subsequently developed a machine learning model to predict the price of Amazon products based on several product-related features.
This pipeline can be used to collect statistical information about all games, distributed through the Steam platform.
A group of python scripts that clean large data sets by removing duplicate data, putting data in correct formats, and removing redundant cells
Developed a Streamlit application for analyzing transactions and user data from the Pulse dataset. Explored data insights on states, years, quarters, districts, transaction types, and brands through EDA. Visualized trends and patterns using plots and charts to optimize decision-making in the Fintech industry.
Domain : Social Media | Extracting data using Youtube API and storing it on MongoDB then Transforming it to a relational databaselike MySQL. For getting various info about youtube channels.
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