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Food Truck Data Pipeline on Snowflake

Members: Truong Dang, Snowflake Solution Engineers

This project demonstrates an end-to-end data pipeline built for a food truck brand (Tasty Bytes) using the Snowflake Data Cloud. It automates data ingestion, transformation, analytics, and monitoring: turning raw data into actionable business insights for better decision-making.

What It Does

The pipeline is divided into three key parts:

  1. Load & Transform: Ingest data from shared Snowflake Tasty Bytes documents into Snowflake Stages and transforms it into tables using SQL and Streams.
  2. Python Powered Analytics: Use Snowpark for Python to run advanced data analysis and build predictive models directly inside Snowflake without data movement.
  3. Scheduling & Monitoring: Automate workflows using Tasks and Stored Procedures, integrates Notification Services, and enables performance tracking for reliable and auditable data operations.

Technologies

  • Snowflake
    • Data Cloud
    • Streams
    • Tasks & Procedures
    • Notification Integrations
  • Python
    • snowflake.snowpark
    • snowflake.connector
    • pandas
    • modin
    • requests
  • SQL
  • JSON

About

Build an end-to-end data pipeline to transform, analyze, monitor changes, and schedule tasks for the Tasty Bytes food truck dataset using Snowflake

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