Retail ETL Pipeline Series
Retail ETL Pipeline Series — Google Cloud & BigQuery
Welcome to the DET LENG Retail ETL Engineering Series.
This series documents the complete lifecycle of designing and implementing a production-style ETL / ELT pipeline using Google Cloud, BigQuery, SQL, and modern data engineering practices.
Instead of focusing only on theoretical concepts, this project demonstrates practical engineering workflows used in real analytics environments — from raw ingestion and staging to curated analytical layers, incremental loading, partitioning, validation, and production-style pipeline architecture.
All tutorials are written with a practical learning approach including:
Real engineering workflows
SQL execution examples
BigQuery screenshots
Architecture explanations
Validation processes
Incremental loading strategies
Failure recovery concepts
Professional portfolio presentation techniques
📘 ETL Engineering Series
1️⃣ ETL Pipeline Execution Overview
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2️⃣ Online Retail Dataset Overview
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3️⃣ Dataset Understanding & Initial Inspection
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4️⃣ Cost-Effective BigQuery Usage
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5️⃣ Raw Data Preparation Layer
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6️⃣ ETL Pipeline Architecture
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7️⃣ BigQuery ETL Architecture Design
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8️⃣ Transformation Layer Logic
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9️⃣ Data Engineering Learning Notes
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🔟 Staging Layer — Clean Retail Data
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1️⃣1️⃣ Staging Layer Validation Process
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1️⃣2️⃣ Curated Retail Orders Table
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1️⃣3️⃣ Curated Layer Validation
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1️⃣4️⃣ BigQuery Partitioning Explained
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1️⃣5️⃣ Incremental Loading using Watermark
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1️⃣6️⃣ Watermark vs Merge Strategy
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1️⃣7️⃣ Incremental ETL Execution
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1️⃣8️⃣ Duplicate Handling in Incremental Loads
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1️⃣9️⃣ Pipeline Failure Scenarios & Recovery
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2️⃣0️⃣ Curated Retail Orders Final Layer
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2️⃣1️⃣ How to Explain This Project Professionally
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2️⃣2️⃣ Retail ETL Portfolio Summary
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2️⃣3️⃣ Marketing Data Engineering Skills
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2️⃣4️⃣ Data Engineering Learning Journey / Reflection
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🚀 What Makes This Series Different?
This is not just a theoretical tutorial collection.
DET LENG focuses on practical implementation, engineering clarity, production-style workflows, and real-world learning approaches used by modern data engineers.
The goal is to help aspiring engineers understand not only what happens inside ETL systems — but why modern engineering decisions are made.
Modern Data Engineering is not only about writing SQL queries.
It is about building scalable systems, understanding data flow architecture, validating transformations, handling failures, optimizing cost, and designing production-ready workflows that organizations can trust.
DET LENG focuses on practical engineering skills that bridge learning and real-world implementation.
