Scraping BooksToScrape (P2 OC D-A Python) : Utiliser les bases de Python pour l'analyse de marché
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Jun 16, 2022 - Python
Scraping BooksToScrape (P2 OC D-A Python) : Utiliser les bases de Python pour l'analyse de marché
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Collect, combine, and clean data from Wikipedia and Kaggle for export into an SQL database.
Extract, Transform and Load data using Python, Pandas, pgAdmin and jupyter notebook
Developed an image classification model for Scones Unlimited to identify delivery vehicles (bicycles vs. motorcycles) to enhance routing and loading bay assignments, thereby optimizing operational efficiency.
🗂️ ETL Process completed in python3 using SQL Sever, MySQL, and PostgreSQl.
Extraction of data from different Database sources, Transformation (unification and cleaning) of extracted data and laoding into the data warehouse
Project for exploration of extract, transform, load process using Python, mongoDB and Flask. Data sets included cryptocurrency pricing and COVID case counts.
Use PySpark to perform the ETL process on a dataset retrieved from an AWS RDS instance.
This project repository provides a headless module to enrich location data in a database table using the Google Maps Geocode API.
This project is a comprehensive data engineering solution that extracts HR data from a GitHub repository, performs data transformations using Azure services, and creates an interactive HR dashboard using Power BI. The goal is to enable HR professionals and decision-makers to gain insights from the HR data for better workforce management.
Climate Data Analytics Project
This repository hosts a collection of Python scripts designed to work with ETL jobs.
A simple, reusable, templates based ETL (Extract, Transform and Load) library and framework written in Python
High Volume Data Analysis with Big Data, AWS, PySpark and pgAdmin
Đồ án thực hành môn HTTT phục vụ Trí tuệ Kinh doanh, HCMUS K19 | Project for Information Systems for Business Intelligence course
A Case Study of Extract, Transform, Load. Documentaion includes sources of data, types of data wrangling performed (data cleaning, joining, filtering, and aggregating) and the schemata used in the final production database. Technologies used include Pandas, PostgreSQL, Jupyter Notebook.
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