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#

etl-processes

Here are 8 public repositories matching this topic...

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A library for data warehouse and data integration pattern and architecture documentation.

  • UpdatedJul 6, 2025
source-watcher-core

This is a PHP project which combines ETL with different strategies to extract data from multiple databases, files, and services, transform it and load it into multiple destinations.

  • UpdatedOct 22, 2025
  • PHP

The project covers the complete data pipeline—from importing data from an RDS source to HDFS using Sqoop, processing data with Spark, to executing analytical queries on an AWS Redshift cluster.

  • UpdatedMar 10, 2025
  • Jupyter Notebook

This project is a specialized Library Management System (LMS) built using MYSQL as the backend database. The database schema is designed to ensure data integrity and consistency, with tables storing information about users, books, transactions, staff.

  • UpdatedFeb 13, 2024

Data analysis project combining Python and SQL to clean, query, and analyze jewelry sales data. Demonstrates skills in data preprocessing with Pandas, writing advanced SQL queries (joins, aggregations, subqueries), and optimizing database performance with indexes and views.

  • UpdatedJul 31, 2025
  • Python

The Global Heatwave Warning Systems Analysis Project was an initiative to develop an advanced warning system for heatwaves worldwide. It involved extracting and analyzing complex meteorological data to predict heatwave occurrences, thereby aiding in timely and effective response strategies for affected regions.

  • UpdatedOct 10, 2023
  • Jupyter Notebook

Parts-Unlimited (EV Expansion)is a key project aimed at enhancing the company's capabilities in the Electric Vehicle (EV) sector. It involved designing and implementing a data warehouse using advanced ETL processes to accommodate the dynamic data requirements of the EV expansion initiative.

  • UpdatedJan 2, 2024
  • Jupyter Notebook

Hands-On Introduction: Data Engineering Project provides practical experience in building data pipelines, managing large datasets, and integrating tools for efficient data processing. It focuses on hands-on skills development for real-world data engineering challenges.

  • UpdatedFeb 12, 2025
  • Shell

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