If you’re building modern software systems or working in data engineering, you’ve likely heard of databases, data warehouses, and data lakes. But what are they really? When should you use each? And how do large platforms like Shopee leverage them to build scalable, intelligent infrastructure?
Let’s break it down — with real-world examples.
🔍 1. What Are They?
📘 Database — The Operational Workhorse
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Purpose: Store structured data for day-to-day transactions.
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Data Type: Structured only.
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Example: MySQL, PostgreSQL, MongoDB.
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Use Case: Real-time application usage.
🧪 At Shopee:
Every time you:
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Browse a product,
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Add to cart,
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Place an order,
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Chat with a seller…
👉 That data is handled by relational databases like MySQL or PostgreSQL.
🏢 Data Warehouse — The Analytical Powerhouse
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Purpose: Analyze structured data for business intelligence (BI) and reporting.
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Data Type: Structured (after cleaning and transforming).
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Example: Amazon Redshift, Google BigQuery, Snowflake.
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Use Case: Business analytics, trend reports, dashboards.
📊 At Shopee:
The marketing team might want to analyze:
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Monthly active users,
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Conversion rates from campaigns,
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Regional performance of certain product categories.
👉 These insights come from the data warehouse, which aggregates clean, consistent data from multiple sources.
🌊 Data Lake — The Raw Data Ocean
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Purpose: Store vast amounts of raw, unstructured, and semi-structured data.
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Data Type: Structured, semi-structured (JSON, CSV), unstructured (images, logs).
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Example: Amazon S3 with AWS Lake Formation, Azure Data Lake, Hadoop HDFS.
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Use Case: Machine learning, real-time analytics, data archiving.
🧠 At Shopee:
The AI team needs:
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User behavior logs,
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Clickstream data,
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Image data from product uploads,
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Reviews in raw text.
👉 This raw, diverse data is stored in the data lake before being processed for ML model training or detailed user behavior analytics.
⚖️ 2. Comparison Table
|
Feature |
Database |
Data Warehouse |
Data Lake |
|---|---|---|---|
|
Data Type |
Structured |
Structured |
Structured, Semi-, Unstructured |
|
Use Case |
Operational/Transactional |
Analytics/BI |
ML, Big Data, Archive |
|
Example Tech |
MySQL, PostgreSQL, MongoDB |
Redshift, BigQuery, Snowflake |
S3, HDFS, Azure Data Lake |
|
Query Speed |
Fast (for small datasets) |
Fast (for analytical queries) |
Slow (unless optimized) |
|
Schema |
Strict schema |
Schema-on-write |
Schema-on-read |
|
Cost |
Medium |
Higher |
Cheaper per GB, but requires setup |
|
Shopee Use Case |
Checkout, Chat, Orders |
Sales Reports, BI Dashboards |
ML logs, Behavior tracking |
🧭 3. When Should You Use Each?
✅ Use Database when:
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You need fast reads/writes.
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You’re handling real-time operations.
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Data is highly structured and transactional.
🎯 Shopee Example: Order placement, inventory update, or chat system.
✅ Use Data Warehouse when:
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You need clean, structured data for analysis.
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You’re building dashboards and BI reports.
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You want fast query performance on large datasets.
🎯 Shopee Example: Understanding customer lifetime value, campaign ROIs, and logistics efficiency.
✅ Use Data Lake when:
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You’re collecting data in various formats.
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You’re storing raw data for later use.
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You’re doing AI/ML, NLP, or image classification.
🎯 Shopee Example: Training a recommendation engine based on clickstream, past purchases, and customer reviews.
🚀 Final Thoughts
A system like Shopee doesn’t rely on one data technology — it combines all three:
• 🏗️ Database: powers daily operations.
• 📊 Data Warehouse: fuels decision-making.
• 🧠 Data Lake: feeds advanced models and innovation.
Each has its place, and choosing the right one depends on your goals — performance, scalability, flexibility, or cost.
🧩 Think of it like this:
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Database = Fast food kitchen (orders go in and out quickly)
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Warehouse = Chef’s recipe book (analyze what people ordered and why)
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Data Lake = All ingredients, raw and ready to create anything
📌 TL;DR
|
Task |
Best Fit |
|---|---|
|
Real-time shopping cart |
Database |
|
Monthly revenue report |
Data Warehouse |
|
Training fraud detection model |
Data Lake |
💡 So next time you’re architecting a system, ask yourself:
What kind of data am I handling? What do I want to do with it?
And let Shopee be your guide to combining all three for a powerhouse architecture.
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