03. Databricks vs Traditional Data Warehouse

Databricks and traditional analytical warehouses overlap in SQL analytics, but their architectural foundations differ.

Traditional warehouse

Sources → ETL / ELT → Data Warehouse → SQL → BI

Databricks lakehouse

Sources → Ingestion → Cloud Storage → Delta Lake
                                      │
                           ┌──────────┼──────────┐
                           ▼          ▼          ▼
                         Spark       SQL        ML/AI
                                      │
                                Unity Catalog

Comparison

Area	Traditional warehouse	Databricks lakehouse
Primary interface	SQL	SQL + Python + Spark + APIs
Storage	Warehouse managed/abstracted	Cloud object storage + table layer
Data engineering	ETL/ELT centric	Spark + pipelines
Streaming	Often additional components	Native Spark/streaming ecosystem
ML	Frequently separate	Integrated data/AI platform
Unstructured data	Usually secondary	Natural fit for lake storage
Governance	Warehouse-specific	Unity Catalog

When a warehouse is a strong choice

Choose a warehouse when workloads are almost entirely SQL, data is highly structured, existing warehouse tooling is mature, and Spark/ML requirements are limited.

When Databricks is attractive

Databricks is compelling when an organization needs large-scale Spark processing, streaming, open lake storage, data science, ML, GenAI, multiple data formats, and a governed platform spanning engineering and analytics.

Migration checklist

A serious migration evaluates:

1. Source ingestion.
2. SQL transformations.
3. Data models.
4. Incremental logic.
5. Workload SLAs.
6. Security.
7. BI dependencies.
8. Cost.
9. Operations.
10. Governance.

Interview answer

A strong answer is: Databricks can provide a common governed platform for data engineering, streaming, SQL analytics, ML, and AI while using scalable cloud object storage and workload-specific compute.

Next

Continue with the next topic in 01. Databricks Foundations.