Data Lakehouse
An architecture combining data lake flexibility with data warehouse reliability and performance.
In-Depth Explanation
A data lakehouse is a modern data architecture that combines the best features of data lakes (flexibility, low cost, diverse data) with data warehouses (reliability, performance, governance).
Lakehouse features:
- ACID transactions: Reliable data operations
- Schema enforcement: Data quality at write
- Time travel: Query historical versions
- Diverse workloads: BI, ML, streaming
- Open formats: Avoid vendor lock-in
Enabling technologies:
- Delta Lake (Databricks)
- Apache Iceberg
- Apache Hudi
Benefits over separate lake + warehouse:
- Single copy of data
- Simplified architecture
- Lower cost
- Fresher data for BI
- ML and BI on same data
Use cases:
- Unified analytics platform
- Real-time BI
- ML feature engineering
- Streaming + batch combined
Business Context
Lakehouses simplify architecture by eliminating the need for separate lakes and warehouses, reducing cost and complexity.
How Clever Ops Uses This
We implement lakehouse architectures for Australian businesses seeking unified platforms for both traditional analytics and AI/ML workloads.
Example Use Case
"Building a unified platform where raw IoT data lands, gets cleaned and transformed, then serves both real-time dashboards and ML model training."
Frequently Asked Questions
Related Terms
Related Resources
Data Lake
A storage repository holding vast amounts of raw data in native format until nee...
Data Warehouse
A centralised repository that stores integrated data from multiple sources for r...
Databricks
A unified analytics platform combining data engineering, data science, and machi...
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