Data Warehousing & Lakehouse Concepts
Data warehousing involves collecting, storing, and managing large volumes of structured data from various sources in a centralized repository for analytics and reporting. Lakehouse concepts blend data warehouse reliability and performance with the scalability and flexibility of data lakes, allowing storage of both structured and unstructured data. This hybrid approach streamlines data management, enables advanced analytics, and supports diverse data types, making it suitable for modern, data-driven organizations.
Data Warehousing & Lakehouse Concepts
Data warehousing involves collecting, storing, and managing large volumes of structured data from various sources in a centralized repository for analytics and reporting. Lakehouse concepts blend data warehouse reliability and performance with the scalability and flexibility of data lakes, allowing storage of both structured and unstructured data. This hybrid approach streamlines data management, enables advanced analytics, and supports diverse data types, making it suitable for modern, data-driven organizations.
💡 Key Takeaways
- Define data warehousing and how it centralizes structured data for analytics and reporting.
- Describe lakehouse concepts and how they blend data warehouse reliability with data lake scalability.
- Compare schema-on-write vs schema-on-read, and understand why ACID transactions and metadata matter.
- Identify typical architectures and workflows (ETL/ELT, data pipelines) and key governance and security considerations.
❓ Frequently Asked Questions
What is a data warehouse?
A centralized repository that stores integrated, structured data from multiple sources, optimized for fast analytics, reporting, and governance.
What is the difference between a data lake and a data warehouse?
A data lake stores raw, diverse data at scale (structured, semi-structured, unstructured) with schema-on-read, while a data warehouse stores curated, structured data optimized for fast queries (schema-on-write).
What is a lakehouse?
A unified architecture that combines the reliability and performance of a data warehouse with the scalability and flexibility of a data lake, enabling analytics on a single platform.
What is schema-on-write vs schema-on-read?
Schema-on-write enforces the schema when data is stored (typical in warehouses); schema-on-read applies the schema when data is queried, allowing raw data to be stored more flexibly (typical in data lakes).
Why are ACID properties important in data platforms?
ACID guarantees that transactions are atomic, consistent, isolated, and durable, ensuring reliable, accurate analytics even with concurrent access.