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Data lakes vs warehouses

Intermediate⏱ 14 min read📅 Updated: 2026-02-17

Introduction

Data collect pannita — ipo enga store pannanum? 🤔

Idhu oru simple question maari theriyum, but wrong choice pannina lakhs of rupees waste aagum, performance drop aagum, AI models proper ah train aaga maatengum.

Two main options irukku: Data Lake 🏊‍♂️ and Data Warehouse 🏢. Plus, modern world la Lakehouse 🏠 nu oru new concept varudhu.

Indha article la moonu concepts um clear ah purinjidum — with real examples and when to use what! 🚀

What is a Data Warehouse?

Data Warehouse = Organized, clean, structured data store — specifically designed for analytics and reporting.

Analogy: Neatly organized library 📚

Books properly cataloged
Each book has ISBN, category, shelf number
Librarian maintain pannum
Easy to find what you need

Characteristics:

Structured data only (tables, rows, columns)
Schema-on-write — data load pannumbodhe schema define pannanum
Optimized for reads — fast queries
Clean data — ETL process vazhiyaa cleaned data mattum varum
Expensive storage — but fast query performance

Popular Data Warehouses:

Tool	Provider	Best For
BigQuery	Google	Serverless analytics
Snowflake	Independent	Multi-cloud
Redshift	AWS	AWS ecosystem
Synapse	Azure	Microsoft ecosystem
Teradata	On-premise	Enterprise legacy

What is a Data Lake?

Data Lake = Massive storage that accepts any type of data — structured, semi-structured, unstructured — in raw format.

Analogy: Oru periya lake 🏊‍♂️

Any water source varudhu (river, rain, drain)
Water filtered ah illa, raw ah store aagudhu
Need pannum bodhu filter panni use pannuvaanga
Massive volume handle pannudhu

Characteristics:

Any data type — CSV, JSON, images, videos, logs
Schema-on-read — read pannumbodhu dhaan schema apply pannum
Raw data — as-is from source
Cheap storage — object storage (S3, GCS) use pannum
Flexible — future use cases ku ready

Popular Data Lake Storage:

Tool	Type	Cost
AWS S3	Object store	~$0.023/GB/month
Google Cloud Storage	Object store	~$0.020/GB/month
Azure Data Lake Storage	Object store	~$0.018/GB/month
HDFS	On-premise	Hardware cost
MinIO	Self-hosted	Free (open source)

Head-to-Head Comparison

Side-by-side comparison paapom:

Feature	Data Lake 🏊‍♂️	Data Warehouse 🏢
Data Type	Any (raw)	Structured only
Schema	Schema-on-read	Schema-on-write
Users	Data Scientists, ML Engineers	Business Analysts, BI
Processing	ELT	ETL
Cost	💰 Low storage	💰💰💰 High compute
Query Speed	🐢 Slow (without optimization)	🚀 Fast (optimized)
Flexibility	✅ Very flexible	❌ Rigid schema
Data Quality	⚠️ Varies	✅ High (curated)
Scale	Petabytes+	Terabytes
Best For	AI/ML, exploration	Reports, dashboards

Key insight: It's not Lake vs Warehouse — most companies use both! Lake la raw data store pannuvaanga, Warehouse la clean data analytics ku use pannuvaanga. 🎯

Schema-on-Write vs Schema-on-Read

✅ Example

Idhu important concept — example la paapom:

Schema-on-Write (Warehouse) 📝

code

Nee book library la add pannanum:
1. First — book details fill pannunga (title, author, ISBN, category)
2. Rules follow pannunga (ISBN must be 13 digits, category must exist)
3. Apparam dhaan shelf la vekka mudiyum

Data load pannumbodhe structure enforce aagum. Wrong format data reject aaidum.

Schema-on-Read (Lake) 📖

code

Nee box la books throw pannunga:
1. Any book, any format — just throw it in
2. Read pannumbodhu dhaan — "oh this is fiction, this is science"
3. Same book different ways la read pannalam

Data raw ah store pannum. Read pannumbodhu dhaan structure apply pannum.

AI ku: Schema-on-read better — raw data la hidden patterns irukkum, schema enforce pannina lose aaidum! 🤖

The Lakehouse: Best of Both Worlds 🏠

2020s la oru new concept popularize aaichu — Data Lakehouse!

Lakehouse = Data Lake + Data Warehouse features combined

Why Lakehouse?

Lake la data quality issues irukku
Warehouse la flexibility issues irukku
Rendu maintain panradhu expensive
Why not combine?

Lakehouse features:

✅ Raw data store (like Lake)
✅ ACID transactions (like Warehouse)
✅ Schema enforcement optional
✅ Fast SQL queries
✅ ML workloads support
✅ Cheap storage (object store based)

Popular Lakehouse Technologies:

Technology	Creator	Key Feature
Delta Lake	Databricks	ACID on Spark
Apache Iceberg	Netflix	Open table format
Apache Hudi	Uber	Incremental processing

Medallion Architecture in Lakehouse:

🥉 Bronze — Raw data (as-is)
🥈 Silver — Cleaned, validated
🥇 Gold — Business-ready, aggregated

Idhu modern standard — most new projects Lakehouse architecture adopt pannuraanga! 🚀

Modern Data Architecture

🏗️ Architecture Diagram

┌─────────────────────────────────────────────────┐
│         MODERN LAKEHOUSE ARCHITECTURE            │
├─────────────────────────────────────────────────┤
│                                                   │
│  SOURCES              LAKEHOUSE          CONSUMERS│
│  ┌──────────┐   ┌──────────────────┐  ┌────────┐│
│  │ Databases│──▶│ 🥉 BRONZE (Raw)  │  │BI Tools││
│  │ APIs     │──▶│      ↓           │─▶│Tableau ││
│  │ Files    │──▶│ 🥈 SILVER (Clean)│  │Looker  ││
│  │ Streams  │──▶│      ↓           │  ├────────┤│
│  │ IoT      │──▶│ 🥇 GOLD (Ready) │  │ AI/ML  ││
│  └──────────┘   │                  │─▶│Models  ││
│                 │  Delta Lake /    │  │Feature ││
│                 │  Iceberg / Hudi  │  │Store   ││
│                 └──────────────────┘  └────────┘│
│                         │                        │
│                 ┌───────▼────────┐                │
│                 │  GOVERNANCE    │                │
│                 │ Catalog│Access │                │
│                 │ Lineage│Quality│                │
│                 └────────────────┘                │
└─────────────────────────────────────────────────┘

When to Use What?

Decision framework:

Use Data Warehouse when:

📊 Business reporting is primary use case
📋 Data is mostly structured (tables)
👔 Business users directly query pannum
⚡ Fast query performance critical
💰 Budget irukku for premium tools

Use Data Lake when:

🤖 AI/ML workloads primary
🎥 Unstructured data irukku (images, text, video)
🔬 Data exploration & experimentation venum
💾 Massive data volume (petabytes)
💰 Cost-sensitive (cheap storage venum)

Use Lakehouse when:

🎯 Both analytics AND AI workloads
🆕 New project — greenfield start panringa
🔄 Don't want to maintain two systems
⚡ Need ACID transactions on data lake
📈 Growing company — future-proof venum

Quick Decision:

code

Only BI/Reports? ──▶ Warehouse
Only AI/ML? ──────▶ Data Lake
Both? ────────────▶ Lakehouse
Not sure? ────────▶ Lakehouse (safe choice)

Data Swamp Alert!

⚠️ Warning

⚠️ Data Lake → Data Swamp is a REAL problem!

Without governance, your beautiful lake becomes a swamp 🐊:

- Nobody knows what data is where

- Duplicate data everywhere

- No documentation

- Stale/outdated data mixed with fresh data

- No access controls — anyone dumps anything

- Query performance terrible

How to prevent:

1. 📝 Data Catalog — Apache Atlas, AWS Glue Catalog use pannunga

2. 🏷️ Metadata — Every file ku owner, date, description irukanum

3. 📂 Folder Structure — bronze/silver/gold organize pannunga

4. 🔒 Access Control — Who can read/write what

5. 🧹 Cleanup Jobs — Old/duplicate data regular ah clean pannunga

6. 📊 Data Quality — Automated checks run pannunga

"A data lake without governance is just an expensive hard drive" 💸

Cost Comparison

Real-world cost comparison (approximate):

Storing 10 TB of data per month:

Solution	Storage Cost	Query Cost	Total/Month
S3 (Lake)	$230	$5-50 (Athena)	~$280
BigQuery (WH)	$200	$50-500	~$500
Snowflake (WH)	$230	$100-1000+	~$800
Databricks (LH)	$230	$50-300	~$400

Key insights:

💰 Storage — Lake wins (object storage is cheapest)
⚡ Query speed — Warehouse wins (optimized engines)
🎯 Total cost — Depends on query volume!

Cost optimization tips:

Partition data by date — query specific date range
Use columnar formats (Parquet, ORC) — 10x compression
Set data lifecycle policies — old data auto-archive
Use spot instances for batch processing
Monitor query costs — set budgets and alerts

Real-World Architecture: Spotify

✅ Example

Spotify oda data architecture paapom 🎵:

Data Lake (Google Cloud Storage):

- Raw event data — every play, skip, like, search

- 600+ billion events per day

- Stored as Avro/Parquet files

Data Warehouse (BigQuery):

- Cleaned, aggregated data

- Artist streaming counts

- Revenue calculations

- Business KPI dashboards

ML Feature Store:

- User taste profiles

- Song audio features

- Recommendation model inputs

Discover Weekly Pipeline:

1. 📥 Lake la irundhu user listening history

2. 🧹 Clean — duplicates, bots remove

3. 🤖 Collaborative filtering model run

4. 🎵 30 personalized songs select per user

5. 📤 Every Monday 600M+ users ku deliver

Lake + Warehouse + ML = Music magic! 🎶

Migration Strategy

Existing system la irundhu migrate pannanum na:

Warehouse → Lakehouse Migration:

📋 Audit current warehouse tables
🏊 Set up lakehouse (Delta Lake/Iceberg)
🥉 Bronze layer la raw data replicate
🥈 Silver layer la current transformations recreate
🥇 Gold layer la warehouse tables mirror
✅ Validate — results match aagudha?
🔄 Gradually switch consumers
🗑️ Decommission old warehouse

Timeline: 3-6 months for medium company

Common pitfalls:

❌ Big bang migration — risk romba jaasthi
❌ Skipping validation — data mismatch catch aagadhu
❌ Not training team — new tools, new skills venum
✅ Incremental migration — table by table move pannunga

Prompt: Design Storage Architecture

📋 Copy-Paste Prompt

You are a data architect designing storage for an Indian fintech startup.

Requirements:
- 50M transactions per day
- Need real-time fraud detection (AI model)
- Regulatory compliance (7 year data retention)
- Monthly business reports for investors
- Budget: ₹5 lakhs/month for data infrastructure

Design the storage architecture:
1. What combination of Lake/Warehouse/Lakehouse?
2. Which specific tools and why?
3. Data retention and archival strategy
4. Cost breakdown
5. Team size needed

Explain in Tanglish. Be practical and cost-conscious.

✅ Key Takeaways

Summary:

✅ Data Warehouse — Structured, clean, fast queries, BI-focused

✅ Data Lake — Any format, raw, cheap, AI/ML-focused

✅ Lakehouse — Best of both, modern standard

✅ Schema-on-Write (Warehouse) vs Schema-on-Read (Lake)

✅ Medallion Architecture — Bronze → Silver → Gold

✅ Data Swamp avoid pannunga — governance is must!

✅ Cost — Lake storage cheap, but Warehouse queries fast

✅ Modern trend — Everyone moving to Lakehouse

Next article: Preparing Data for AI — ML models ku data eppadi ready pannanum! 🎯

🏁 🎮 Mini Challenge

Challenge: Lakehouse vs Lake vs Warehouse practical experience

Hands-on comparison – architectures test pannu:

Setup: S3 Data Lake (5 min)

bash

# Bronze layer (raw data)
aws s3 sync ./raw s3://my-lake/bronze/
# Silver layer (cleaned)
aws s3 sync ./cleaned s3://my-lake/silver/

Setup: BigQuery Warehouse (5 min)

Create dataset: bq mk my_dataset
Load CSV: bq load my_dataset.table_name data.csv

Setup: Delta Lake (Lakehouse) (5 min)

python

import delta
df.write.format("delta").mode("overwrite").save("s3://my-lake/delta/table")

Test Queries (20 min):

Lake – S3 Select query (structured query on unstructured)
Warehouse – SQL (instant, optimized)
Lakehouse – Delta with SQL (best of both)

Compare:

Lake: Flexible, cheap, query slow
Warehouse: Fast queries, structured only, expensive
Lakehouse: Balanced, modern, recommended!

Learning: Architecture choice = cost vs speed trade-off! 💰⚡

💼 Interview Questions

Q1: Data Lake storage podhum, why warehouse separate maintain pannanum?

A: Lake = raw, flexible storage. Warehouse = optimized for analytics (indexed, aggregated, cleaned). Data scientists lake use pannum (raw data experiment), business analysts warehouse (fast reports). Both complement pannum – not replacement!

Q2: Data Swamp – lake eppadi swamp aagum?

A: Governance illa na – metadata tracking illa, organization illa, quality checks illa. Same data duplicate versions, nobody know who owner, stale data mix. Solution: Data catalog, lineage tracking, quality gates, access controls, retention policies implement pannu!

Q3: Schema-on-read vs schema-on-write – trade-offs?

A: Write (Warehouse): Enforce at load – safe, fast queries, but rigid. Read (Lake): Load as-is, apply at read – flexible, slow queries. Modern: Lakehouse hybrid – store raw (on-read), but optional schema enforcement (on-write). Best!

Q4: Cost analysis – lake vs warehouse real numbers?

A: Storage: Lake $0.02/GB, Warehouse $5-50/GB. But query: Lake $5-50 per TB scanned, Warehouse $6+ per TB. Volume dependent – petabytes → lake wins, terabytes + frequent queries → warehouse. Lakehouse – balanced cost 💡

Q5: Migration – warehouse → lakehouse strategy?

A: Incremental! Not big bang. 1) Setup lakehouse (Delta/Iceberg) 2) Replicate critical tables first 3) Validate results 4) Gradually migrate consumers 5) Keep warehouse read-only during transition 6) Finally decommission. Takes 3-6 months, but zero downtime! Patience needed.

Frequently Asked Questions

❓ Data Lake vs Data Warehouse — simple difference?

Data Lake stores raw data in any format (structured, unstructured). Data Warehouse stores only cleaned, structured data optimized for analytics and reporting.

❓ AI projects ku Data Lake ah Data Warehouse ah?

AI/ML projects ku usually Data Lake better — raw data venum for training. Analytics/reporting ku Data Warehouse better. Modern approach: Lakehouse — both benefits combine pannudhu.

❓ Data Lakehouse na enna?

Lakehouse = Data Lake + Data Warehouse combined. Raw data store panna mudiyum, ACID transactions support pannum, SQL queries fast ah run aagum. Databricks, Delta Lake ivanga popular lakehouse solutions.

❓ Data Lake "data swamp" aagama eppadi protect pannradhu?

Proper metadata management, data cataloging (Apache Atlas), access controls, data quality checks, and clear folder structure maintain pannunga. Governance illama lake swamp aaidum!

🧠Knowledge Check

Quiz 1 of 1

Schema-on-Read approach edhu use pannudhu?

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Exam Prep

Data lakes vs warehouses

Introduction

What is a Data Warehouse?

What is a Data Lake?

Head-to-Head Comparison

Schema-on-Write vs Schema-on-Read

The Lakehouse: Best of Both Worlds 🏠

Modern Data Architecture

When to Use What?

Data Swamp Alert!

Cost Comparison

Real-World Architecture: Spotify

Migration Strategy

Prompt: Design Storage Architecture

✅ Key Takeaways

🏁 🎮 Mini Challenge

💼 Interview Questions

Frequently Asked Questions