Shubhi, the question is simple: When a modeler is giving a proposal to someone, should they structure the table and approach with technical rationale? Which platform are you using? In Databricks, if you are giving someone a model that works on finance data and the requirement is to maintain history, from an implementation point of view, what is the underlying architecture of Databricks?

Databricks is built on top of Apache Spark and follows a lakehouse architecture that combines the best of data lakes and data warehouses. The core storage layer is Delta Lake, which adds ACID transactions, schema enforcement, and time‑travel to raw data files. When a modeler proposes a table structure, they should first define the logical schema (columns, data types, primary keys) and then map it to a Delta table. Delta Lake automatically manages partitions, compaction, and versioning, so historical data is preserved without manual archiving. For finance data, you would typically enable Delta Lake’s schema‑evolution and enforce a primary key on a transaction ID or account number. The architecture also includes the Databricks Runtime, which optimizes Spark jobs, and the Unity Catalog for fine‑grained access control. By using Delta Lake, you get consistent reads, efficient updates, and the ability to query historical snapshots via time‑travel, satisfying the requirement to maintain history while keeping the implementation simple and scalable.