As per info available ingestion time clustering makes use of time of the time a file is written or ingested in databricks. In a use case where there is new delta table and an etl which runs in timely fashion(say daily) inserting records, am able to understand how the timing of ingestion of file can be made use in clustering.
However in most of the cases it might be a migration of data from an existing platform to databricks and would have an initial load. How would ingestion time clustering be useful in such scenarios where large of amounts of records would be inserted into a table in a single go initially.
The second question: Lets say in daily batches records are inserted into a delta table which is making use of ingestion time clustering. The most used query against the table is based on the column date(yyyymmdd) available(select * from table where ='yyyymmdd'). How does ingestion time clustering help in reducing the query time
