Explanation
With the defined constraint and expectation clause, when a batch of data is processed, any records that violate the expectation (in this case, where the timestamp is not greater than '2020-01-01') will be dropped from the target dataset. These dropped records will also be recorded as invalid in the event log, allowing for auditing and tracking of the data quality issues without causing the entire job to fail.
https://docs.databricks.com/en/delta-live-tables/expectations.html

References: Create a table using SQL | Databricks on AWS, Create a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks

JSON data is a text-based format that represents data as a collection of name-value pairs. By default, when Auto Loader infers the schema of JSON data, it treats all columns as strings. This is because JSON data can have varying data types for the same column across different files or records, and Auto Loader does not attempt to reconcile these differences. For example, a column named "age" may have integer values in some files, but string values in others. To avoid data loss or errors, Auto Loader infers the column as a string type. However, Auto Loader also provides an option to infer more precise column types based on the sample data. This option is called cloudFiles.inferColumnTypes and it can be set to true or false. When set to true, Auto Loader tries to infer the exact data types of the columns, such as integers, floats, booleans, or nested structures. When set to false, Auto Loader infers all columns as strings. The default value of this option is false. Reference: Configure schema inference and evolution in Auto Loader, Schema inference with auto loader (non-DLT and DLT), Using and Abusing Auto Loader's Inferred Schema, Explicit path to data or a defined schema required for Auto loader.

Cron syntax is a tool that can be used to represent and submit a complex run schedule programmatically. Cron syntax is a string of six fields that specify the frequency, date, and time of a job run. For example, the cron expression 0 0 12 * * ? means run the job at 12:00 PM every day. The data engineer can use the Databricks REST API to create or update a job with a cron schedule. The data engineer can also use the Databricks CLI to create or update a job with a cron schedule by using a JSON file that contains the cron expression. The other tools are either invalid or not suitable for representing and submitting a complex run schedule programmatically. Reference: Schedule a job, Jobs API, Databricks CLI, Cron expressions

Data Explorer is a graphical interface that allows you to browse, create, and manage data objects such as databases, tables, and views in your workspace. You can also review and modify the permissions on these data objects using Data Explorer. To access Data Explorer, you can click on the Data icon in the sidebar, or use the
%sql magic command in a notebook. You can then select a database and a table, and click on the Permissions tab to view and edit the access control lists (ACLs) for the table. You can also use SQL commands such as SHOW GRANT and GRANT to query and modify the permissions on a Delta table. References:
* Data Explorer
* Access control for Delta tables
* SHOW GRANT
* [GRANT]