Data Warehouses Data Lakes and Lakehouses Compared
Data warehouses, data lakes, and lakehouses are three ways to store and analyze data. Each approach fits different work styles, and many teams use more than one at the same time. The choice often comes down to what you plan to do with the data.
A data warehouse stores structured data for fast, reliable analytics. It uses schema-on-write, strong governance, and optimized queries. People trust dashboards built on a warehouse because queries are predictable and the data is clean. This makes them a good home for reporting and business insights.
A data lake stores raw data in many formats. It emphasizes cheap storage and schema-on-read. You can land logs, images, sensor data, and more, then shape it later for analysis. Lakes are useful for data science, experimentation, and future needs that are not yet clear.
A lakehouse blends ideas from both. It aims to give fast analytics, with governance and support for diverse data types. A lakehouse often uses a single metadata layer and machine learning friendly features. It can use open storage formats while keeping data organized.
When deciding which pattern to use, think about workloads.
- For strict governance and fast dashboards, a data warehouse is a solid choice.
- For exploratory analytics, data science, or very large raw data sets, a data lake fits better.
- For a mix of analytics and ML with a simple toolchain, a lakehouse can cover many needs.
Consider these factors: data variety, latency needs, cost, team skills, and tool support. A warehouse shines with structured data and established BI tools. A lake helps when you want to store many data types cheaply. A lakehouse tries to offer the best of both, but it can be more complex to manage.
Example: an online retailer stores product tables in a warehouse for dashboards, raw logs in a data lake, and a lakehouse to combine customer data for marketing models. That setup supports fast reporting and smarter recommendations without moving data too many times.
Starting tips: map the workloads, write a simple data governance plan, and run a small pilot. Monitor storage costs, data quality, and user feedback. Over time, you can evolve your architecture to fit changes in data and needs.
Key Takeaways
- Warehouses, lakes, and lakehouses are three patterns with different strengths.
- Use governance, cost, and workload needs to guide your choice.
- A phased approach can start with a lake and evolve toward a lakehouse if needed.