What are the differences between Data Lakehouse vs Data Warehouse vs Data lake

1. data warehouse delivers clean, structured data for BI analytics, while a data lake permanently and cheaply stores data of any nature in any format. Many organizations use data lakes for data science and machine learning, but not for BI reporting due to its unvalidated nature.
2. data lakehouse combines the advantage of the data lake and data warehouse. the reliable transactions of a data warehouse and the scalability and low cost of a data lake. Single Source of Truth. Unified data from all sources
3. The data lake table format is the most important component of a lakehouse architecture. There must be some way to organize and manage all the raw data files in the data lake storage. Table formats help abstract the physical data structure’s complexity and allow different engines to work simultaneously on the same data. The table format in a lakehouse architecture facilitates the ability to do data warehouse-level transactions (DML) along with ACID guarantees. Some of the other critical features of a table format are schema evolution, expressive SQL, time travel, data compaction (ie Delta Lake). Apache Spark can also be used as the query engine