Focusing on building industry-leading ETL engines.

Regular practice on Data Science, Machien Learning, Deep Learning, Solving ML Project problem, Analytical Issue. Regular boost up my knowledge. The goal is to help learner with learning resource on Data Science filed.

python ETL framework

For this project I am creating an ETL (Extract, Transform, and Load) pipeline using Python, RegEx, and SQL Database. The goal is to retrieve data from different sources, clean and transform it into a useful format and finally load the data into an SQL database where the data is ready for further analysis. The result is an established automated p…

Implementation of an ETL process for real-time sentiment analysis of tweets with Docker, Apache Kafka, Spark Streaming, MongoDB and Delta Lake

3NF-normalize Yelp data on S3 with Spark and load it into Redshift - automate the whole thing with Apache Airflow

Extract transform load CLI tool for extracting small and middle data volume from sources (databases, csv files, xls files, gspreadsheets) to target (databases, csv files, xls files, gspreadsheets) in free combination.

PHP ETL library: pipeline of extractors, transformers, and loaders (CSV/JSON/DB, etc.) run via a fluent API.

Sugar candy for data scientist. Easy manipulation in time-series data analytics works.

This project repository provides a headless module to enrich location data in a database table using the Google Maps Geocode API.

Scraping BooksToScrape (P2 OC D-A Python) : Utiliser les bases de Python pour l'analyse de marché

This is a sentimental analysis project that aims to provide a better insight on customers' satisfaction based on comments gathered (scrapped) from social media using google's Bert classification model.

a data warehouse for an online course shop

Dynamic website scraper and email notifier.

Udacity nd027 Data Modeling with Postgres

Extractor of Ethereum data to Dgraph format, utilities to analyse the indexed data.

We examine two data sets relate with the music Industry. We Extract, transform and load the data sets in order to create a data base and identify insides and trends about the music Industry.

I made various data normalization operations with python scripts. Target data in CSV format

An ETL process for a fictitious streaming service, Amazing Prime, was developed in Jupyter Notebook. The code was then refactored into a Python script to automate the ETL process.

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.