In the world of data, two architectures have long dominated: the Data Warehouse, optimized for Business Intelligence (BI), and the Data Lake, designed for storing raw data at scale. Each has its strengths but also its weaknesses, often forcing companies to use both in parallel. The Data Lakehouse is emerging as a unified solution that promises the best of both worlds.
The Dilemma: Data Warehouse vs. Data Lake
Traditionally, companies loaded their structured data into a Data Warehouse for fast SQL analytics and BI reporting. It's a reliable and high-performance approach, but it's also expensive and inflexible for unstructured data (text, images) or Machine Learning use cases.
For example, a typical query for a sales report would look like this:
-- Calculate monthly revenue by country
SELECT
DATE_TRUNC('month', order_date) AS sales_month,
country,
SUM(revenue) AS total_revenue
FROM sales
GROUP BY 1, 2
ORDER BY 1, 3 DESC;The Data Lake emerged to address this need for flexibility. By storing data of any format at a low cost, it became the playground for Data Scientists. However, without strict governance, it can quickly turn into a "data swamp," where data quality and reliability are compromised.
What is a Data Lakehouse?
A Data Lakehouse is a new data architecture that implements the data structures and management features of a data warehouse directly on the low-cost storage of a data lake. In practice, it combines:
- The flexibility and low cost of a Data Lake.
- The reliability, governance, and performance (ACID transactions, schema enforcement) of a Data Warehouse.
This is made possible by a new transactional metadata layer on top of open file storage (like Apache Parquet), using formats such as Delta Lake, Apache Iceberg, or Apache Hudi.
Key Advantages
- Simplified Architecture: A single system for BI, SQL analytics, Data Science, and Machine Learning. No more data duplication.
- Reduced Costs: Uses standard cloud object storage (e.g., Amazon S3, Azure Data Lake Storage), which is much cheaper than proprietary data warehouse storage.
- Always-Fresh Data: BI and ML teams work on the same data source, eliminating latencies from complex ETL pipelines.
- Openness and Flexibility: Based on open file formats, it avoids vendor lock-in.
When to Use It?
The Data Lakehouse is particularly relevant if you are looking to modernize your data platform, unify your BI and Data Science teams, or if you are building a new architecture from scratch and want a future-proof, scalable design. It represents a major evolution, simplifying how companies unlock the full value of their data.