Tracking Real-Time Customer Journey using Snowpipe at GlobalMart

Learning Objectives

Configure notification integrations for enabling auto-ingestion pipelines between Azure and Snowflake

Implement Snowpipe for automatic data loading with event-driven triggers and continuous ingestion capabilities

Create and manage external stages for accessing streaming Parquet files from cloud storage containers

Design staging tables with VARIANT data types to handle flexible schemas and semi-structured streaming data

Analyze schema variations across event types to understand data completeness and structure differences in streaming data

Extract and transform semi-structured data using FLATTEN operations and lateral joins for nested JSON structures

Handle timestamp conversions for epoch-based timestamps and real-time event processing

Overview

GlobalMart, a fast-growing e-commerce company, was losing customers to competitors who seemed to "read their minds" in real-time. While GlobalMart analyzed yesterday's customer behavior, their rivals were responding to what customers were doing right now — offering instant recommendations, preventing cart abandonment within minutes, and personalizing experiences based on live browsing patterns. The solution? Implementing streaming data analytics to capture and analyze every customer action as it happens.

In this project, you'll follow GlobalMart's transformation from batch-based historical analytics to real-time customer intelligence. You'll learn how to:

Build auto-ingesting data pipelines that process customer actions continuously
Set up Snowpipe for automatic data loading without manual intervention
Handle streaming data with flexible schemas using VARIANT data types
Analyze real-time customer behavior patterns and conversion funnels

This project isn't just about moving data faster — it's about fundamentally changing how businesses understand and respond to customer behavior. You'll discover how real-time data enables instant personalization, immediate cart abandonment prevention, and live competitive intelligence that drives revenue growth.

If you want to master streaming data engineering and learn how modern e-commerce companies achieve real-time customer insights, this project will teach you the skills that separate advanced data engineers from the rest.

Prerequisites

Experience with Snowflake data warehouse concepts including external stages, file formats, and storage integrations
Knowledge of cloud storage platforms such as Azure Data Lake Storage, AWS S3, or GCP Storage for staging streaming data files
Familiarity with VARIANT data types for handling flexible JSON/Parquet schemas and extracting nested data structures
Knowledge of timestamp handling and data type conversions for processing real-time event data