Databricks

History

1980 - Data warehouse	Collect and store structured data to provide support for for refined analysis and reporting.
2000 - Data lake	Collect and store raw data and conducting exploratory analysis
2021 - Data lakehouse	Unified plateform that benefits of both data lakes and data warehouses solution

Aspect	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured, processed, and refined data	Raw data: structured, semi-structured, and unstructured	Combines raw and processed data
Schema	Schema-on-write: Data is structured before storage	Schema-on-read: Structure applied when accessed	Flexible: Schema-on-read for raw data; schema-on-write for structured data
Purpose	Optimized for business intelligence (BI), reporting, and predefined analytics	Designed for big data analytics, machine learning, and exploratory analysis	Unified analytics platform for BI, AI/ML, streaming, and real-time analytics
Processing Approach	ETL: Data is cleaned and transformed before storage	ELT: Data is loaded first and transformed as needed	Both ETL and ELT; enables real-time processing
Scalability	Less scalable and more expensive to scale	Highly scalable and cost-effective for large volumes of diverse data	Combines scalability of lakes with performance optimization of warehouses
Users	Business analysts and decision-makers	Data scientists, engineers, and analysts	BI teams, data scientists, engineers
Accessibility	More rigid; changes to structure are complex	Flexible; easy to update and adapt	Highly adaptable; supports schema evolution
Security & Maturity	Mature security measures; better suited for sensitive data	Security measures evolving; risk of "data swamp" if not managed properly	Strong governance with ACID transactions; improved reliability
Use Cases	Operational reporting, dashboards, KPIs	Predictive analytics, AI/ML models, real-time analytics	Unified platform for BI dashboards, AI/ML workflows, streaming analytics