A Python API for Asynchronously Loading Data into Snowflake DB -

Analytics engineering capstone from DataExpert. I used python, Snowpark, Snowflake, dbt, airflow, machine learning (LightGBM), and Power BI. Ingested 700k rows of raw flight arrival data and paired with a weather api to make delay predictions with ML.

This package has been created to simplify the creation of Snowpark for Python sessions and Snowpipe Ingest Manager objects. This package heavily leverages the main `snowflake-snowpark-python[pandas]` and `snowflake-ingest` packages from Snowflake.

A production‑grade, modular data pipeline that automates the ingestion, transformation, and loading of retailer data into Snowflake using Python, Airflow, Terraform, and Docker. Ideal for data engineers, DevOps engineers, and DataOps workflows.

Data pipeline for Auto Ingestion in Snowflake using Snowpipe. Core Components: Terraform IaC (AWS + Snowflake), Python Flask WebApp (Data Source) and AWS S3 (Staging Area).

Car Rental Data Batch Ingestion using Python, PySpark, GCP Dataproc, Airflow, Snowflake.

This project implements a batch data ingestion and transformation pipeline for car rental data using Python, PySpark, Airflow, GCP Dataproc, and Snowflake.

This project implements a batch data ingestion and transformation pipeline for car rental data using Python, PySpark, Airflow, GCP Dataproc, and Snowflake.

An end-to-end streaming data pipeline that orchestrates data ingestion, processing, and storage using faust python library, kafka, python and snowflake. Components are dockerized for easy setup and management

End-to-end data analytics project using Python, Snowflake, and SQL. The workflow includes splitting a large JSON file for efficient ingestion, uploading data to AWS S3, loading it into Snowflake, and performing data analysis with SQL.

⚽ SnowGoal : Pipeline Data Engineering 100% Snowflake Native. Analyse de 11 compétitions internationales (Top 5, Championship, Brasileirão, UCL...). Architecture Medallion, Ingestion Snowpark (Python), et Dashboard Streamlit.

A comprehensive cloud-based stock price prediction application leveraging AWS, Snowflake, Python, and Streamlit. This project automates the collection of historical stock prices using AWS Lambda, stores the data in an S3 bucket, and integrates with Snowflake using Snowpipe for real-time data ingestion.

A robust cloud-based pipeline designed to process structured and unstructured data. This project automates the ingestion, transformation, and spatial analysis of Airbnb datasets using Python, AWS (S3/Athena), and Snowflake, optimizing large-scale geospatial queries.

An end-to-end real-time pipeline on AWS & Snowflake with a Python producer + Kinesis Data Firehose ingesting OpenAlex into RAW/VARIANT, CURATED views, Streamlit UI, RBAC & dynamic masking, with Terraform IaC and GitHub Actions CI/CD.

Automated Python ETL pipeline that fetches NSE stock data using yfinance, processes it in parallel, and loads it into Snowflake via Parquet with PUT and COPY INTO. Includes logging for execution tracking, ensuring scalable, efficient financial data ingestion.

Built an end-to-end data analytics platform integrating REST API ingestion, Snowflake warehousing, and Power BI dashboards, enabling faster access to insights from 35K+ real-world fitness records and eliminating manual data aggregation through automated Python and SQL ETL pipelines.

Built an automated daily pipeline to fetch Spotify’s “Top 100 Global” playlist data using Python, store raw files in AWS S3, and transform them into structured Albums, Artists, and Songs tables; leveraged Snowpipe for near real-time ingestion into Snowflake.

Resort Radar is a data engineering project that ingests Reddit data on snowboarding resorts via the PRAW API, applies sentiment analysis, and visualizes results in Tableau—all powered by an end-to-end ETL pipeline using Python, Airflow, and Snowflake.

A beginner-friendly, end-to-end ETL pipeline project demonstrating data ingestion from AWS S3 into Snowflake using Python. This project showcases cloud resource setup, secure credential management, and automation skills — perfect for trainees and freshers looking to prove their practical data engineering abilities.

• Simulated a sales data warehouse with Airflow-based ingestion and transformation. • Created data models and documentation using DBT and visualized insights using BI tools. • Wrote SQL queries for metrics like total sales, product growth, and top regions. • Tech Stack: Apache Airflow, Snowflake, DBT, Python, SQL, Excel, BI Tools

Sales ML Pipeline is an end-to-end analytics project that integrates Snowflake cloud storage with Python-based Machine Learning to predict sales revenue.The project initially explored real-time streaming using Docker and Kafka to simulate live sales ingestion, providing hands-on experience with data engineering concepts.

A modern end-to-end data pipeline that extracts raw data using Python, loads it into Snowflake, transforms it with dbt, orchestrates all workflows using Apache Airflow, and visualizes insights in Metabase. This project demonstrates the full modern data stack — from ingestion to analytics — with clean, scalable, and production-ready practices.

An automated script that cleans and imports a CSV into snowflake

Python scripts for Azure Blob Storage data ingestion into Snowflake. Includes a manual version and an HTTP request version for Azure Functions.

Python package to ease data ingestion from snowflake to streamlit

Production-style ELT pipeline with Python ingestion, Snowflake, dbt transformations, and Airflow orchestration

A minimal Snowflake ETL pipeline using Python and Pandas to ingest synthetic customer data.

Batch ELT pipeline for US bank financial data: Python ingestion → AWS S3 → Snowflake → dbt transformations.

End-to-end streaming pipeline built with Kafka, Python and Snowflake for data validation and ingestion.

End-to-end data pipeline using Python and Snowflake to ingest, transform, and analyze retail sales data.