This section describes how you interact through Cloud Dataprep by TRIFACTA® with your BigQuery tables.
Uses of BigQuery
Cloud Dataprep by TRIFACTA can use BigQuery for the following tasks:
- Create datasets by reading from BigQuery tables.
- Write data to BigQuery.
Before You Begin Using BigQuery
- Your BigQuery administrator must enable BigQuery for your Cloud Dataprep by TRIFACTA project.
- Your BigQuery administrator should provide datasets or locations and access for storing datasets within BigQuery.
- Users should know where shared data is located and where personal data can be saved without interfering with or confusing other users.
NOTE: Cloud Dataprep by TRIFACTA does not modify source data in BigQuery. Datasets sourced from BigQuery are read without modification from their source locations.
For more information on how data types are converted to and from BigQuery sources, see BigQuery Data Type Conversions.
Reading from Tables in BigQuery
You can create a dataset from a table stored in BigQuery.
- Standard SQL
- Nested tables are supported.
- Partitioned tables are supported, but these must include a schema.
- Partitioning filters are not supported.
NOTE: Reading from external tables or from tables without a schema is not supported.
Reading from other projects
If you have read access to other projects, you can read from BigQuery tables that are associated with those projects. You must have read access on any table from which you are reading.
For more information, see BigQuery Browser.
Writing to BigQuery
You can publish datasets back to BigQuery through the standard publishing methods.
NOTE: Object and Array data types are written back to BigQuery as string values.
Writing to other projects
If you have write access to other projects, you can write to BigQuery tables that are associated with those projects. You must have write access to any table to which you are writing.
You can specify the target table as part of the job specification. See Run Job Page.