Advanced RFM Analysis with DBT and Snowflake

This project dives into the advanced usage of dbt (data build tool) and Snowflake, a cloud data platform, to perform Recency, Frequency, Monetary (RFM) analysis on e-commerce data. By leveraging Snowpark, Snowflake's powerful library for data operations, we aim to enhance the capabilities of our data transformation processes.

Features

• RFM Analysis with DBT: A comprehensive model in dbt to transform data for RFM analysis.
• Snowflake and Snowpark Integration: A deep dive into Snowflake's Snowpark library, integrating its functionality into our dbt models.
• Data Management with SnowSQL: Step-by-step data ingestion procedure in Snowflake using SnowSQL commands

Getting Started

Prerequisites

• DBT: A command-line tool that enables data analysts and engineers to transform data in the warehouse more effectively.

  • Installation: Follow the official guide for installation instructions.

• Snowflake Account: Ensure you have an active account and the necessary permissions on Snowflake.

  • Sign up: Visit Snowflake's official website.

• SnowSQL: Snowflake's command-line client.

  • Installation: Visit SnowSQL's official documentation for installation steps.

Configuration & Deployment

1. Repository Setup:

   git clone [URL_OF_YOUR_GITHUB_REPOSITORY] dbt_snowflake_project
   cd dbt_snowflake_project

2. Snowflake Connection:

   • Set up your Snowflake connection credentials for dbt in ~/.dbt/profiles.yml.
   • Ensure the necessary permissions for data access.

3. Data Loading using SnowSQL:

   • Navigate to the directory containing the Snowflake script:

     snowsql -f snowflake_scripts/data_upload.sql

   • This script handles various Snowflake operations, such as setting up the working environment, staging CSV data, loading it to tables, and performing clean-up operations.

4. DBT Model Execution:

   dbt run --models rfm_analysis

Deeper Dive

• RFM Analysis:

  • Our dbt model extracts insights from the invoices and items tables.
  • The aim is to evaluate customers' value using the RFM methodology, which considers how recently a customer has purchased (Recency), how often they purchase (Frequency), and how much the customer spends (Monetary).

• Snowflake Script Operations:

  • Login and environment setup within Snowflake.
  • Data staging and ingestion from upload.csv.
  • Verification of data population within Snowflake tables.

Additional Documentation

• Snowpark Guide: Explore the capabilities of Snowflake's Snowpark and its usage in our models.