Data lakes vs warehouses

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! 🚀

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:

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:

Side-by-side comparison paapom:

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. 🎯

Idhu important concept — example la paapom:

Schema-on-Write (Warehouse) 📝

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

Schema-on-Read (Lake) 📖

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! 🤖

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:

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! 🚀

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:

⚠️ 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" 💸

Real-world cost comparison (approximate):

Storing 10 TB of data per month:

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

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! 🎶

Existing system la irundhu migrate pannanum na:

Warehouse → Lakehouse Migration:

1. 📋 Audit current warehouse tables
2. 🏊 Set up lakehouse (Delta Lake/Iceberg)
3. 🥉 Bronze layer la raw data replicate
4. 🥈 Silver layer la current transformations recreate
5. 🥇 Gold layer la warehouse tables mirror
6. ✅ Validate — results match aagudha?
7. 🔄 Gradually switch consumers
8. 🗑️ 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

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! 🎯

Challenge: Lakehouse vs Lake vs Warehouse practical experience

Hands-on comparison – architectures test pannu:

Setup: S3 Data Lake (5 min)

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)

Test Queries (20 min):

1. Lake – S3 Select query (structured query on unstructured)
2. Warehouse – SQL (instant, optimized)
3. 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! 💰⚡

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.