In today’s data-driven world, choosing the right architecture is crucial. This article compares data warehouse, data lake, data lakehouse, and data mart through real-world business use cases—exploring how data flows from raw sources to decision-making dashboards. Each serves a unique purpose, and choosing the right one depends on your team’s goals, tools, and data maturity.
Data Lake
Data lake is a large repository that stores huge amounts of raw data in its original format until you need to use it. There are no fixed limitations on data lake storage. That means that considerations—like format, file type, and specific purpose—do not apply. It is used when organizations need flexibility, and is required in data processing and analysis. Data lakes can store any type of data from multiple sources, whether that data is structured, semi-structured, or unstructured. As a result, data lakes are highly scalable, which makes them ideal for larger organizations that collect a vast amount of data.