# Data Lakehouse

> Unifies the cost-effective storage of data lakes with the ACID transactions and governance of data warehouses.

- **Category**: Data Science
- **Subcategory**: data_storage
- **Canonical URL**: https://designpattern.fyi/patterns/data-lakehouse/

---

## Description
A Data Lakehouse implements a structured transactional layer (such as Delta Lake, Apache Iceberg, or Apache Hudi) directly on top of cheap object storage, enabling both analytics (BI queries) and machine learning on the same copy of data.



## Use Cases
Consolidating raw telemetry logs, semi-structured JSON, and structured financial tables into a single platform with strict compliance.



## Implementation Example

```sql
-- SQL example creating a Delta Lakehouse table with schema enforcement and partitions
CREATE TABLE IF NOT EXISTS lakehouse.sales (
    sale_id INT NOT NULL,
    customer_id INT,
    amount DECIMAL(10, 2),
    sale_date DATE
)
USING delta
PARTITIONED BY (sale_date)
COMMENT 'Transactional Sales Lakehouse Table';

-- Time travel query reading historical snapshot
SELECT * FROM lakehouse.sales TIMESTAMP AS OF '2026-06-01 00:00:00';

```



## Trade-offs


### Advantages

- Eliminates redundant, complex ETL pipelines between data lakes and warehouses.

- Supports ACID transactions, schema enforcement, and versioning (time travel).

- Open storage formats prevent vendor lock-in.




### Considerations & Drawbacks

- Still relatively complex compared to using a fully managed cloud-native data warehouse.