Build a Real– Time Fraud Detection Pipeline in Snowflake

What you'll learn

Build a near real-time fraud detection pipeline using native Snowflake capabilities.
Use Snowflake Streams to implement Change Data Capture (CDC) and process incremental data.
Create a raw transaction layer and simulate incoming transaction data.
Build a fraud alerts layer to store suspicious transactions and fraud detection results.
Use Snowpark Python to implement fraud detection and scoring logic inside Snowflake.
Create and execute Snowpark stored procedures.
Automate data processing using Snowflake Tasks.
Build an end-to-end pipeline that processes new transactions automatically.
Test and validate an automated incremental data pipeline.
Monitor pipeline execution using Snowflake Task History and Query History.
Understand how Streams, Tasks, and Snowpark work together in real-world data engineering architectures.

Requirements

Basic understanding of SQL, including SELECT statements and working with tables.
Access to a Snowflake account or Snowflake trial environment.
Basic familiarity with the Snowflake interface and worksheets is helpful.
Basic understanding of Python is helpful but not required.
No prior experience with Snowflake Streams, Tasks, or Snowpark is required.
An interest in data engineering, real-time data processing, or automated data pipelines.

Description

Build a near real-time fraud detection pipeline in Snowflake through a practical, end-to-end hands-on project.
In this course, you will work with a realistic fintech and digital payments use case where transactions arrive continuously and suspicious activity needs to be identified quickly. Instead of focusing only on individual Snowflake features, you will build a complete automated data pipeline step by step.
You will start by creating a raw transaction layer and simulating incoming payment transactions. You will then use Snowflake Streams to track incremental changes so that the pipeline processes only new records instead of repeatedly scanning the entire dataset.
Next, you will build a fraud detection process using Snowpark Python. You will create a stored procedure that reads new transactions, applies fraud detection rules, calculates fraud scores, and stores suspicious transactions in a fraud alerts table.
You will then automate the pipeline using Snowflake Tasks. The Task will run the fraud detection process automatically, allowing you to experience how incremental and automated data pipelines can be built using native Snowflake capabilities.
By the end of this course, you will understand how the following Snowflake technologies work together
- Snowflake Streams for Change Data Capture (CDC)
- Snowpark Python for in-platform data processing
- Stored Procedures for implementing processing logic
- Snowflake Tasks for pipeline automation
- Incremental data processing
- Fraud scoring and alert generation
- Task History and Query History for monitoring pipeline execution
The architecture you will build follows this flow
Incoming Transactions
↓
Raw Transaction Layer
↓
Snowflake Stream for Change Data Capture
↓
Snowpark Python Fraud Scoring
↓
Automated Snowflake Task
↓
Fraud Alerts
↓
Monitoring and Execution History
This course is designed as a real-world, project-based learning experience. You will not simply learn what Streams, Tasks, and Snowpark are—you will use them together to build a complete working pipeline.
Although this course uses fraud detection as the business scenario, the architecture and concepts can also be applied to many other use cases, including monitoring, alerting, log processing, and incremental data pipelines.
If you already have basic familiarity with Snowflake and SQL and want to gain hands-on experience building a practical automated data pipeline, this course is for you.
By the end of the course, you will have a working example of how to design and build an incremental, automated, near real-time data pipeline using native Snowflake capabilities.

Who this course is for

Data Engineers who want hands-on experience building automated data pipelines in Snowflake.
Snowflake developers who want to learn Streams, Tasks, and Snowpark through a practical project.
Data professionals who want to understand Change Data Capture and incremental data processing.
Analytics Engineers who want to build automated processing workflows using native Snowflake capabilities.
Developers with basic SQL knowledge who want practical experience with Snowpark Python.
Anyone who already understands the basics of Snowflake and wants to build a complete end-to-end data engineering project.

Homepage

https://www.udemy.com/course/real-time-fraud-detection-pipeline-in-snowflake/

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