Whether you're a data engineer or a backend developer building pipelines, you’ve probably heard these terms thrown around: Data Warehouse, Data Lake, and Data Lakehouse. But what do they really mean in practice?

We recently unpacked this in our blog at AQE Digital. Here’s the TL;DR:

Data Warehouse

Optimized for high-speed SQL queries and analytics. Great for structured, historical data.

Data Lake

Best when dealing with massive volumes of raw data from multiple sources—structured or unstructured.

Data Lakehouse

The modern hybrid combining benefits of both. Streamlines workflows and reduces data duplication.

This breakdown helps teams make the right call depending on their data maturity level, query performance needs, and infrastructure costs.

👉 Read the full post to go deeper into architecture comparisons, pros and cons, and real-world applications: