Database, Warehouse, Lake, Lakehouse—Which Box Do You Actually Need?

Database, warehouse, lake, lakehouse: four tools that sound alike but solve different problems. Pick the wrong one and you either pay for idle storage or slow the business with the wrong engine. This guide shows what each layer does, when to add it, and when to wait.

Every data need has its layer.

Operational Database

An operational database is built for handling real-time transactions: every sale, user signup, or inventory update. You need this from day one if your business processes transactions or user events. However, it is not designed for analytics. If you run heavy reports or dashboards directly on your production database, expect slow performance and increased costs. When you notice reporting starting to slow down your main system, it’s time to look at separating analytics out.

Data Warehouse

A data warehouse is the right tool when your team outgrows spreadsheets and manual reports. Warehouses are designed for fast, reliable analytics on structured data, think monthly revenue dashboards or marketing funnel reports. If leadership is asking for more detailed, ad-hoc reporting, or if your finance and sales teams are arguing over which numbers are correct, a warehouse brings structure and consistency. Warehouses can be expensive if you’re just starting out, so only invest once you have regular reporting needs and someone in your business who can write SQL or manage BI tools.

Data Lake

A data lake is useful when your business starts to collect large volumes of data in various formats, like logs, sensor data, images, or raw event streams. If you’re only using CSVs and structured tables, you probably don’t need a data lake. But if your team wants to experiment with machine learning, archive raw data for future analysis, or store data you don’t know how you’ll use yet, a lake offers cheap, scalable storage. One warning: without clear cataloging and governance, data lakes can quickly become disorganized and hard to use. Only build a lake if you’re ready to invest in managing it.

Lakehouse

A lakehouse is a new generation architecture that combines the low-cost storage and flexibility of a data lake with the structured, governed tables of a data warehouse. This is useful if you need both business intelligence and advanced analytics or machine learning, and you want to avoid creating multiple copies of your data. A lakehouse lets you support dashboards, compliance, and data science from one platform. Consider this approach if your data volumes are growing fast and you have teams who need both standard reporting and direct access to raw data.

Which one fits your business?

If you are just launching and only have simple reporting needs, an operational database is enough. As your reporting becomes more frequent or complex, or you need to reconcile numbers across departments, it’s time to add a data warehouse. If you start collecting and storing a mix of unstructured and structured data for data science or compliance reasons, a data lake or lakehouse might make sense—but only if you have clear use cases and people to manage them. Most businesses will find a warehouse is the first big step. A lake or lakehouse comes later, once there’s a proven need.

Don’t choose technology for its buzzword. Map your choice to the pain points you actually have, and grow your stack as your business demands it. That way, you stay efficient and avoid both technical debt and wasted spend.

Sources

AWS Editorial Team. (n.d.). OLTP vs. OLAP: Difference Between Data Processing Systems. Amazon Web Services.

AWS Editorial Team. (n.d.). Data Lake vs. Data Warehouse vs. Data Mart. Amazon Web Services.

AWS Editorial Team. (n.d.). What Is a Data Lake? Amazon Web Services.

Databricks Documentation Team. (n.d.). What Is a Data Lakehouse? Databricks.

Yu, A. (2023, January 26). Database vs. Data Lake vs. Data Warehouse: What’s the Difference? Redpanda Blog.