setup.py

import apache_beam as beam
import json

from apache_beam.io import ReadFromText
from apache_beam.io import BigQuerySource
from apache_beam.io import BigQuerySink
from apache_beam.io import WriteToText
from apache_beam.io.gcp.bigquery_tools import parse_table_schema_from_json
from apache_beam.io.gcp.internal.clients import bigquery
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.options.pipeline_options import GoogleCloudOptions
from apache_beam.options.pipeline_options import StandardOptions

options = PipelineOptions()
google_cloud_options = options.view_as(GoogleCloudOptions)
google_cloud_options.project = "pascalwhoop"
google_cloud_options.job_name = "phone-sensors-cleanup"
google_cloud_options.staging_location = "gs://pascalwhoop-private/staging"
google_cloud_options.temp_location = "gs://pascalwhoop-private/temp"
#options.view_as(StandardOptions).runner = "DirectRunner"  # use this for debugging
options.view_as(StandardOptions).runner = "DataFlowRunner"

# see here for bigquery docs https://beam.apache.org/documentation/io/built-in/google-bigquery/
source_table_spec = bigquery.TableReference(
    projectId="pascalwhoop", datasetId="phone_sensors", tableId="heartbeat"
)
sink_table_spec = bigquery.TableReference(
    projectId="pascalwhoop", datasetId="phone_sensors", tableId="heartbeat_cleaned"
)

def make_sink_schema():
    mapping = {
        "altitude":          "FLOAT", 
        "battery_status":    "INTEGER",
        "bluetooth_status":  "STRING", 
        "cell_id":           "STRING", 
        "cell_strength":     "INTEGER",
        "gps_status":        "STRING", 
        "last_app":          "STRING", 
        "location_accuracy": "FLOAT", 
        "location_gps":      "STRING", 
        "location_net":      "STRING", 
        "location_seconds":  "STRING",
        "speed":             "FLOAT", 
        "timestamp":         "INTEGER"
    }
    mapping_list =  [{"mode": "NULLABLE", "name": k, "type": mapping[k]} for k in mapping.keys()]
    return json.JSONEncoder(sort_keys=True).encode({"fields": mapping_list})

table_schema = parse_table_schema_from_json(make_sink_schema())

#source = BigQuerySource(query="SELECT * FROM `pascalwhoop.phone_sensors.heartbeat` LIMIT 10", use_standard_sql=True)  # you can also use SQL queries
source = BigQuerySource(source_table_spec)
target = BigQuerySink(sink_table_spec, schema=table_schema)
#target = beam.io.WriteToText("output.txt")