Explanation
The answer is Lakehouse supports schema enforcement and evolution,
Lakehouse using Delta lake can not only enforce a schema on write which is contrary to traditional big data systems that can only enforce a schema on read, it also supports evolving schema over time with the ability to control the evolution.
For example below is the Dataframe writer API and it supports three modes of enforcement and evolution, Default: Only enforcement, no changes are allowed and any schema drift/evolution will result in failure.
Merge: Flexible, supports enforcement and evolution
* New columns are added
* Evolves nested columns
* Supports evolving data types, like Byte to Short to Integer to Bigint How to enable:
* DF.write.format("delta").option("mergeSchema", "true").saveAsTable("table_name")
* or
* spark.databricks.delta.schema.autoMerge = True ## Spark session
Overwrite: No enforcement
* Dropping columns
* Change string to integer
* Rename columns
How to enable:
* DF.write.format("delta").option("overwriteSchema", "True").saveAsTable("table_name") What Is a Lakehouse? - The Databricks Blog Graphical user interface, text, application Description automatically generated

Explanation
The answer is Not Null, Check Constraints
https://docs.microsoft.com/en-us/azure/databricks/delta/delta-constraints
* CREATE TABLE events( id LONG,
* date STRING,
* location STRING,
* description STRING
* ) USING DELTA;
ALTER TABLE events CHANGE COLUMN id SET NOT NULL;
ALTER TABLE events ADD CONSTRAINT dateWithinRange CHECK (date > '1900-01-01'); Note: Databricks as of DBR 11.1 added support for Primary Key and Foreign Key when Unity Catalog is enabled but this is for information purposes only these are not actually enforced. You may ask then why are we defining these if they are not enforced, so especially these information constraints are very helpful if you have a BI tool that can benefit from knowing the relationship between the tables, so it will be easy when creating reports/dashboards or understanding the data model when using any Data modeling tool.
Primary and Foreign Key
Graphical user interface, text, application, email Description automatically generated

Explanation
TRANSFORM -> Transforms elements in an array in expr using the function func.
1.transform(expr, func)

Explanation
Answer is
1.CREATE TABLE transactions (
2.transactionId int,
3.transactionDate timestamp,
4.unitsSold int)
When creating a table in Databricks by default the table is stored in DELTA format.