Google BigQuery

Create an BigQuery Datasource
1.Navigate to: Resources > Data Sources
2.Select the BigQuery Icon:
Data Source Inputs
Name (required)
Google Cloud Project ID (required): Retrieve this from your GCP console
Credential Type: Select Shared 
This is the recommended type by Zepl, to reduce the number of users that require access to the service account key

See Data Source Security

Query Priority: For more information, see Google's Documentation​
Interactive: This is the default value and will count towards your concurrent rate limit and your daily limit​
Batch: This method queues each batch query on your behalf, and starts the query as soon as idle resources are available in the BigQuery shared resource pool.
​
Service account key (JSON): 
Copy the entire JSON key located in the service account credential file downloaded from BigQuery.
See Configure Authentication for more details
Use BigQuery
Read
Python
SQL
%python
from google.cloud import bigquery
import pandas as pd
​
# Construct a BigQuery client object.
client = z.getDatasource("BigQuery_Sample")
​
# Create Query
query = """
    SELECT name, SUM(number) as total_people
    FROM `bigquery-public-data.usa_names.usa_1910_2013`
    WHERE state = 'TX'
    GROUP BY name, state
    ORDER BY total_people DESC
    LIMIT 20
"""
​
# Execute Query and return Results
query_job = client.query(query).result()
​
# Convert query result object into a pandas dataframe
df = query_job.to_dataframe()
​
z.show(df)
Use z.getDatasource()  to return BigQuery client object: google.cloud.bigquery.client.Client​
// TODO: After Robert completes this
Write
Python
SQL
Create Sample Pandas DataFrame for Writing
%python
import pandas as pd
​
# Create data frame
data = {"names": ["Edward Norton", "Nicholas Cage", "Sean Connery", "Ed Harris"],
     "Row1": [5, 6, 7, 8],
     "Row2": [9, 10, 11, 12],
     "Row3": [13, 14, 15, 16],
     "Row4": [17, 18, 19, 20],
     "Row5": [21, 22, 23, 24]}
​
# Create Pandas DataFrame
df = pd.DataFrame.from_dict(data)
Create Client, Dataset, Table, and LoadJobConfig
%python
from google.cloud import bigquery
from google.cloud.exceptions import NotFound
​
# Create BigQuery Client Object
client = z.getDatasource("BigQuery_Sample")
​
# Get Project ID
project_id = client.project
​
# Create BigQuery Dataset Reference Name (ID)
dataset_id = "write_dataset4"
dataset_id_full = "{}.{}".format(project_id, dataset_id)
​
# Create BigQuery Dataset Object - Check if it already exists
dataset = bigquery.Dataset(dataset_id_full)
try:
    client.get_dataset(dataset_id)  # Make an API request.
    print("Dataset {} already exists".format(dataset_id))
except NotFound:
    # Create the new BigQuery dataset.
    dataset = client.create_dataset(dataset)
​
​
# Create BigQuery Table Reference Name (ID)
table_id = "write_df_to_table"
table_id_full = "{}.{}.{}".format(project_id, dataset_id, table_id)
​
# Create BigQuery Table Object
table_ref = dataset.table(table_id)
​
# Configure the query job.
job_config = bigquery.QueryJobConfig()
​
# Set the destination to write to the table object created above
job_config.destination = table_ref
​
# Create LoadJobConfig to specify the table schema and write characteristics
job_config = bigquery.LoadJobConfig(
    # Specify a (partial) schema. All columns are always written to the table. The schema is used to assist in data type definitions
    schema=[
        # Specify the type of columns whose type cannot be auto-detected. For example the "title" column uses pandas dtype "object", so its data type is ambiguous
        bigquery.SchemaField("names", bigquery.enums.SqlTypeNames.STRING)
    ],
    # Optionally, set the write disposition. BigQuery appends loaded rows to an existing table by default, but with WRITE_TRUNCATE write disposition it replaces the table with the loaded data
    write_disposition="WRITE_TRUNCATE",
)
Write DataFrame to Table
%python
# Execute write
job = client.load_table_from_dataframe(
    df, table_id_full, job_config=job_config
)
job.result()
​
# Display results jf loaded succesfully
table = client.get_table(table_id_full)
print( "Loaded {} rows and {} columns to {}".format(table.num_rows, len(table.schema), table_id_full))
//TODO: After Robert completes
Documentation: 
​https://cloud.google.com/bigquery/docs/quickstarts/quickstart-client-libraries​
​https://googleapis.dev/python/bigquery/latest/usage/pandas.html​
If support is needed for Scala, PySpark, and R, please contact [email protected]​
​Open all examples in Zepl​
Configure Authentication
Enable BigQuery API
1.Login to Google Cloud Platform
2.Select or Create a Project
3.If not already enabled, select "Enable APIs and Services". Enable the BigQuery APIs for this project
Create a Service Account Credential for Authentication from Zepl
1.Navigate to "APIs & Services" > Credentials
2.Create Credentials > Service Account
3.Service account details > Name (can be changed later): "zepl-bigquery-service-account"
4.Grant this service account access to project (optional) > Role: Scroll down to BigQuery and select the desired Roles to grant access to
5.Grant User (optional) ...
6.Edit the service account just created > Select Add Key > Create new Key > JSON > Create
This is the key that will be entered into Zepl's BigQuery Data Source. DO NOT LOSE THIS KEY.
7.Save