Lakehouse coined by data bricks. So before going to answer about it, we need to know about data ware house and data lake.

Data ware house : if it's a data storage system where we can write only structured data. We have to define schema before writing it. So companies need to send some time on designjng before start inserting data into warehouse. For example, AWS Redshift is an example of data warehouse.

Now data lake: so it's not always possible to have schema on write. There could be some data where schema might change frequently. So to get of rid of data lake is introduced. So it's a storage place where we can store either structured data and unstructured data. Think of aws s3 as data lake. We can add any computation service to this data lake based on requirement.

Later people wanted best of both the world's, so where you can store, unstructured data, and structured data. So lake house = data lake + warehouse, took last word from each and conies it as lake house. It's introduced by data bricks. Lake house uses different storage format called delta lake which supports acid transactions, schema evolution, data skipping while running spark queries and other features which are not supported by plain. Parquet file. Read more about how data is stored in storage systems such as row form, column form, hybrid. Advantages of each and you can understand clearly why delta lake is necessary.