Q: BigQuery Spark connector: Wildcard tables or TABLE_DATE_RANGE I use the Spotify BigQuery connector for Spark (https://github.com/spotify/spark-bigquery) in version 0.2.1-s_2.11 to load raw Google Analytics data from Google BigQuery. This lib uses the official BigQuery connector for Hadoop/Spark from Google (https://cloud.google.com/dataproc/docs/connectors/bigquery) internally. 
It works totally fine if I only load one table at a time, for example: 
sqlContext.bigQueryTable("<dataset-id>.<some-table-name")

But I want to load multiple tables at a time. I tried to use DATE_RANGE functions and table wildcards which are provided by BigQuery SQL dialect:
sqlContext.bigQueryTable("<dataset-id>.<some-table-name-with-wildcard>")

But this doesn't work and I get the following error: 
Invalid datasetAndTableString '<dataset-id>.<some-table-name-with-wildcard>'; must match regex '[a-zA-Z0-9_]+\.[a-zA-Z0-9_]+'

That's clear because the specification of a table reference says: "[...] The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_)[...]"
I want to load raw data as is, so I can't use sqlContext.bigQuerySelect() and provide a SQL statement, because then I would have to unnest the nested data stored in arrays (which I have a lot) because the connector only supports legacy SQL.
So is it possible to load data from multiple tables at one time without manually flatten it with something like TABLE_DATE_RANGE or wildcard functions which are provided by BigQuery itself with the BigQuery connector for Spark? Of cause I could load every single table one by one, but I want to avoid the overhead of fire a new query to BigQuery for every single table. So I thought I could load table ranges with one single query.
Thank you in advance!
