• A minimum of data (recipe steps and associated metadata) is transferred between systems. Everything else remains in BigQuery.
• Recipe steps are converted into SQL that is understandable and native to BigQuery. Execution times are much faster.
• Depending on your environment, total cost of executing the job may be lower in BigQuery.

In this scenario, the recipe steps are converted to SQL, which is sequentially executed on the Datasets and Tables of your source data into temporary tables, from which the results that you have defined for your output are written.

Requirements

• This feature must be enabled by the project owner. See Configure Running Environments.
• The permission to execute jobs in BigQuery must be enabled. In most environments, it is enabled by default. For more information, see Required Dataprep User Permissions.
• In your flow, you must enable all general and BigQuery-specific flow optimizations. When all of these optimizations are enabled, the job can be pushed down to BigQuery for execution. For more information, see Flow Optimization Settings Dialog.

If the requirements and limitations are met, the  automatically executes the job in BigQuery.

Limitations

BigQuery as a running environment requires that pushdowns be enabled for the project and for the specific flow for which the job is executed. If the flow and the project are properly configured, the job is automatically executed in BigQuery.

• For any job to be executed in BigQuery:
  • All datasources must be located in BigQuery or .
  • If the output is a file published to , then inputs must be in BigQuery.
• BigQuery does not publish CSV files with quotes. For example, when a column value is empty in transformer, BigQuery publishes it as an empty string.
• If a column string value has quotes or a delimiter, BigQuery encloses that string value with double quotes for CSV and JSON files.
• BigQuery does not write column values that are empty or null while publishing to JSON format.
•  must be selected as running environment.

• All recipe steps, including all  functions in the recipe, must be translatable to SQL. 

  NOTE: When attempting to execute a job in BigQuery, executes each recipe in BigQuery, until it reaches a step that cannot be executed there. At that point, data is transferred to , where the remainder of the job is executed.

• BigQuery imposes a limit of 1 MB for all submitted SQL queries. If this limit is exceeded during job execution,  falls back to submitting the job through .

• If the schemas have changed for your datasets, pushdown execution on BigQuery is not supported.  falls back to submitting the job through .

• Some transformations and functions are not currently supported for execution in BigQuery. See below.
• If your recipe includes data quality rules, the job cannot be fully executed in BigQuery.
• BigQuery does not permit partitioned tables to be replaced. As a result, the Drop and Load publishing action is not supported when writing to a partitioned table during BigQuery execution. For more information, see https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#create_table_statement. 
• In BigQuery, escaped whitespace characters (\s) match a broader set of Unicode space characters than , due to differences in implementation of regular expressions between the two running environments. Depending on your dataset, this difference may result in mismatches between rows in your results when running the same job across different running environments.
• Some uncommon date formats are not supported for pushdown.
• Publication of complex arrays to BigQuery is not supported for jobs executed in the BigQuery running environment. To publish these arrays as non-String values, you must disable all flow optimizations and run the job in .

 File Support

In addition to BigQuery sources, you can execute jobs in BigQuery on source files from  . 

• By default, the Full execution for GCS file option is enabled for new flows. For more information, see Flow Optimization Settings Dialog.

• For more information, see Google Cloud Storage Access.

Requirements:

• Publishing actions must be defined to be a target in BigQuery. 
• External views must be enabled in BigQuery. External views are used to query GCS files.  For more information, see Required Dataprep User Permissions.
• In the , the following flow optimization settings must be enabled at the flow level. 
  • BigQuery optimization
  • Full execution for GCS file
• In the Run Job page, the  + BigQuery running environment must be selected. For more information, see Run Job Page.

Supported file formats from  :

• CSV. CSV files that fail to meet the following requirements may cause job failures when executed in BigQuery, even though they can be imported into . Requirements:

  • For job execution of CSV files in BigQuery, source CSV files must be well-formatted.

  • Newlines must be inserted. 

  • Fields must be demarcated with quotes and commas.

    • Quotes in field value must be escaped with quotes when needed ("").

  • Each row must have the same number of columns.

  • For more information, see  https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-csv .

• TSV
• JSON (newline-delimited)
• TXT
• LOG

• Compressed Files (gz and bz)
  

  NOTE:  Snappy and bz2 file formats are not supported for pushdown execution in BigQuery. When these file formats are encountered as datasources, the job automatically reverts to run on .

  
  

Supported file encodings:

• UTF-8
• ISO-8859-1

Supported delimiters:

• Comma
• Tab
• Pipe

Supported quote characters:

• No quotes
• double quotes

Unsupported  for BigQuery Execution

The following transformations and functions are not currently supported for execution in BigQuery. 

General limitations

• Regex patterns used must be valid RE2. Operations on non-RE2 regex patterns are not pushed down.
• Source metadata references such as $rownumber  and $filepath  are not supported for pushdown.
  
• For more information on limitations on specific push-downs, see Flow Optimization Settings Dialog.

Unsupported transformations

None.

Unsupported functions

The following  functions are not currently supported for execution in BigQuery.

Aggregate functions

KTHLARGEST
KTHLARGESTIF
KTHLARGESTUNIQUE
KTHLARGESTUNIQUEIF
MODE
MODEIF
QUARTILE
APPROXIMATEMEDIAN
APPROXIMATEPERCENTILE
APPROXIMATEQUARTILE

For more information, see Aggregate Functions.

Math functions

LCM
NUMVALUE

Partially supported:

NUMFORMAT: Only supported when used for rounding.

For more information, see Math Functions.

Date functions

NETWORKDAYS
NETWORKDAYSINTL
MODEDATE
WORKDAY
WORKDAYINTL
MODEDATEIF
KTHLARGESTDATE
KTHLARGESTUNIQUEDATE
KTHLARGESTUNIQUEDATEIF
KTHLARGESTDATEIF
EOMONTH
SERIALNUMBER

Partially supported:

DATEFORMAT: Some uncommon formatting options are not supported for pushdown.

For more information, see Date Functions.

String functions

RIGHTFIND
EXACT
STRINGGREATERTHAN
STRINGGREATERTHANEQUAL
STRINGLESSTHAN
STRINGLESSTHANEQUAL
DOUBLEMETAPHONEEQUALS
TRANSLITERATE

For more information, see String Functions.

Window functions

SESSION

For more information, see Window Functions.

Verify Execution

To verify execution in BigQuery, please do the following:

Steps:

1. In the left nav bar, click the Jobs link. 
2. In the Job History page, select the job that you executed. 
3. In the Overview tab, the value for Environment under the Execution summary should be: BigQuery.

For more information, see Job Details Page.