PacificRetail Snowflake Data Engineering Project

👤 My Role

As a Data Engineer, I designed and implemented this end-to-end Snowflake solution for PacificRetail.
My responsibilities included:

• Architecting the Bronze–Silver–Gold layered data pipeline
• Implementing data ingestion from Azure Data Lake Storage (ADLS) into Snowflake
• Developing incremental processing with Snowflake Tasks & Streams
• Building data quality checks & standardization logic
• Designing business-ready data models in the Gold layer for analytics and ML use cases

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📌 Project Overview

PacificRetail is a fast-growing e-commerce company operating in 15 countries with:

• 5M+ active customers
• 100K+ product catalog
• Real-time transaction logs

The company faced challenges with data silos, processing delays, poor scalability, and limited analytics capabilities.
This project showcases how Snowflake’s cloud-native architecture can overcome these bottlenecks and enable near real-time reporting, advanced analytics, and ML readiness.

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🎯 Business Goals

• Reduce reporting latency from 24h → 1h
• Achieve 99% accuracy in cross-channel sales reporting
• Support 5x growth in data volumes with no performance degradation
• Provide self-service BI to business teams
• Build a foundation for AI/ML use cases (e.g., churn prediction, demand forecasting)

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🏗️ Solution Architecture

The solution uses a multi-layer Snowflake architecture with ADLS as staging:

1. Data Sources

   • CRM system → CSV (daily export)
   • Inventory management → JSON (hourly updates)
   • E-commerce platform → Parquet (real-time logs)

2. Azure Data Lake Storage (ADLS)

   • Central landing zone for all raw data
   • Cost-effective, scalable, supports multiple file formats

3. Snowflake Data Warehouse

   • Bronze Schema → Raw ingestion (append-only)
   • Silver Schema → Clean & standardized data with incremental merges
   • Gold Schema → Business-ready aggregates and models for BI/ML

4. Analytics Layer

   • Power BI / Tableau / Excel connected to Snowflake Gold layer

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🔄 Data Flow

1. Data lands in ADLS from source systems
2. Snowflake COPY INTO loads data → Bronze tables
3. Snowflake Tasks + Streams apply transformations → Silver tables
4. Standardization & data quality checks applied
5. Aggregates, views, and marts created → Gold schema
6. BI/Analytics tools query the Gold layer
   

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🛠️ Key Snowflake Features Implemented

• External Stages → Connect Snowflake with ADLS
• COPY Command → Load CSV, JSON, Parquet into Bronze tables
• Streams + Tasks → Automate incremental processing & transformations
• MERGE Statements → Handle updates in Silver schema
• Time Travel → Historical data recovery and audit
• Zero-Copy Cloning → Rapidly provision Dev/Test environments

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💻 Example SQL Snippets

1. Create External Stage (ADLS → Snowflake)

create or replace stage adls_stage
url='azure://<storage_account>/<container>'
credentials=(azure_sas_token='<token>');

2. Load Data into Bronze Layer

copy into bronze.customer
from @adls_stage/customer/
file_format = (type = csv field_optionally_enclosed_by='"')
on_error = 'continue';

3. Incremental Merge into Silver Layer

merge into silver.customer as target
using (
  select customer_id, name, email, country, customer_type
  from bronze.customer
) as source
on target.customer_id = source.customer_id
when matched then update set
    name = source.name,
    email = source.email,
    country = source.country,
    customer_type = source.customer_type
when not matched then insert (customer_id, name, email, country, customer_type)
values (source.customer_id, source.name, source.email, source.country, source.customer_type);

4. Create Business Aggregates in Gold Layer

create or replace table gold.sales_summary as
select 
    c.country,
    p.category,
    date_trunc('day', o.transaction_date) as sales_date,
    count(distinct o.transaction_id) as total_orders,
    sum(o.total_amount) as total_revenue,
    avg(o.total_amount) as avg_order_value
from silver.orders o
join silver.customer c
    on o.customer_id = c.customer_id
join silver.product p
    on o.product_id = p.product_id
group by c.country, p.category, date_trunc('day', o.transaction_date);

✅ Business Outcomes

• ⏱️ Reporting latency reduced from 24h → 1h
• 📊 99% accuracy in cross-channel reporting
• ⚡ 5x scalability with no performance degradation
• 👩‍💻 Self-service analytics enabled for business teams
• 🤖 ML-ready foundation for churn prediction, demand forecasting & personalization

🚀 Key Learnings

• Importance of layered architecture (Bronze/Silver/Gold) for flexibility
• Using Streams + Tasks to automate near real-time processing
• How Snowflake’s separation of storage & compute improves scalability
• Designing pipelines that balance cost efficiency with performance