The data scene has seen a big change in recent years, moving from old-fashioned data warehouses to more easy and strong lakehouse architectures. This switch means not just a technical advancement but a basic rethinking of how groups keep, handle, and get value from their data assets.
The traditional data warehouse worked well with structured data because there was a defined schema, consistency, and strong BI performance. The high costs and inflexibility of such warehouses meant that they were not suitable for handling unstructured data- much less a large volume of such unstructured data.
This is what gave birth to the data lake: an inexpensive place to store enormous volumes of highly variegated datasets-with schema-on-read flexibility. It then often turned into the "data swamp" due to quality issues, analytical performance shortcomings, and governance issues.
Then came the lakehouse architecture, a brilliant amalgamation that took unto it the strengths of both warehouse and lake.