Google BigQuery Connector for Python In this article you will learn how to integrate Google BigQuery data in Python (live / bi-directional connection to Google BigQuery). Read / write Google BigQuery data inside your app without coding using easy to use high performance API Connector. Using Google BigQuery Connector you will be able to connect, read, and write data from within Python. Follow the steps below to see how we would accomplish that. Download Documentation The driver mentioned above is part of ODBC PowerPack which is a collection of high-performance Drivers for various API data source (i.e. REST API, JSON, XML, CSV, Amazon S3 and many more). Using familiar SQL query language you can make live connections and read/write data from API sources or JSON / XML / CSV Files inside SQL Server (T-SQL) or your favorite Reporting (i.e. Power BI, Tableau, Qlik, SSRS, MicroStrategy, Excel, MS Access), ETL Tools (i.e. Informatica, Talend, Pentaho, SSIS). You can also call our drivers from programming languages such as JAVA, C#, Python, PowerShell etc. If you are new to ODBC and ZappySys ODBC PowerPack then check the following links to get started. ODBC PowerPack - System Requirements and Installation Getting Started - Using ODBC PowerPack ODBC API Driver Help File

Connect to Google BigQuery in other apps Connect to Google BigQuery in SSIS Connect to Google BigQuery in SQL Server Connect to Google BigQuery in Power BI Connect to Google BigQuery in SSRS Connect to Google BigQuery in Informatica Connect to Google BigQuery in MS Access Connect to Google BigQuery in MS Excel Connect to Google BigQuery in SSAS Connect to Google BigQuery in C# Connect to Google BigQuery in JAVA Connect to Google BigQuery in Tableau Connect to Google BigQuery in SAP Crystal Reports Connect to Google BigQuery in Azure Data Factory (Pipeline) Connect to Google BigQuery in Azure Data Factory (SSIS) Connect to Google BigQuery in Talend Studio Connect to Google BigQuery in UiPath Connect to Google BigQuery in PowerShell Connect to Google BigQuery in ODBC

Create ODBC Data Source (DSN) based on ZappySys API Driver

Step-by-step instructions

To get data from Google BigQuery using Python we first need to create a DSN (Data Source) which will access data from Google BigQuery. We will later be able to read data using Python. Perform these steps:

1. Install ZappySys ODBC PowerPack.

2. Open ODBC Data Sources (x64):
   

3. Create a User Data Source (User DSN) based on ZappySys API Driver

   ZappySys API Driver

   
   You should create a System DSN (instead of a User DSN) if the client application is launched under a Windows System Account, e.g. as a Windows Service. If the client application is 32-bit (x86) running with a System DSN, use ODBC Data Sources (32-bit) instead of the 64-bit version.

4. When the Configuration window appears give your data source a name if you haven't done that already, then select "Google BigQuery" from the list of Popular Connectors. If "Google BigQuery" is not present in the list, then click "Search Online" and download it. Then set the path to the location where you downloaded it. Finally, click Continue >> to proceed with configuring the DSN:

   GoogleBigQueryDSN

   Google BigQuery

   

5. Now it's time to configure the Connection Manager. Select Authentication Type, e.g. Token Authentication. Then select API Base URL (in most cases, the default one is the right one). More info is available in the Authentication section.

    User Account [OAuth]

   Steps to get Google BigQuery Credentials
   This connection can be configured using two ways. Use Default App (Created by ZappySys) OR Use Custom App created by you.
   To use minimum settings you can start with ZappySys created App. Just change UseCustomApp=false on the properties grid so you dont need ClientID / Secret. When you click Generate Token you might see warning about App is not trusted (Simply Click Advanced Link to expand hidden section and then click Go to App link to Proceed).

   To register custom App, perform the following steps (Detailed steps found in the help link at the end)

   1. Go to Google API Console
   2. From the Project Dropdown (usually found at the top bar) click Select Project
   3. On Project Propup click CREATE PROJECT
   4. Once project is created you can click Select Project to switch the context (You can click on Notification link or Choose from Top Dropdown)
   5. Click ENABLE APIS AND SERVICES
   6. Now we need to Enable two APIs one by one (BigQuery API and Cloud Resource Manager API).
   7. Search BigQuery API. Select and click ENABLE
   8. Search Cloud Resource Manager API. Select and click ENABLE
   9. Go to back to main screen of Google API Console

   10. Click OAuth consent screen Tab. Enter necessary details and Save.

       1. Choose Testing as Publishing status
       2. Set application User type to Internal, if possible
       3. If MAKE INTERNAL option is disabled, then add a user in Test users section, which you will use in authentication process when generating Access and Refresh tokens
   11. Click Credentials Tab
   12. Click CREATE CREDENTIALS (some where in topbar) and select OAuth Client ID option.
   13. When prompted Select Application Type as Desktop App and click Create to receive your ClientID and Secret. Later on you can use this information now to configure Connection with UseCustomApp=true.
   14. Go to OAuth Consent Screen tab. Under Publishing Status click PUBLISH APP to ensure your refresh token doesnt expire often. If you planning to use App for Private use then do not have to worry about Verification Status after Publish.

   Fill in all required parameters and set optional parameters if needed:

   GoogleBigQueryDSN

   Google BigQuery

   User Account [OAuth]

   https://www.googleapis.com/bigquery/v2

   Required Parameters
   UseCustomApp	Fill in the parameter...
   ProjectId (Choose after [Generate Token] clicked)	Fill in the parameter...
   DatasetId (Choose after [Generate Token] clicked and ProjectId selected)	Fill in the parameter...
   Optional Parameters
   ClientId	Fill in the parameter...
   ClientSecret	Fill in the parameter...
   Scope	Fill in the parameter...
   RetryMode	Fill in the parameter...
   RetryStatusCodeList	Fill in the parameter...
   RetryCountMax	Fill in the parameter...
   RetryMultiplyWaitTime	Fill in the parameter...
   Job Location	Fill in the parameter...
   Redirect URL (Only for Web App)	Fill in the parameter...

   

    Service Account (Using Private Key File) [OAuth]

   Steps to get Google BigQuery Credentials
   Use these steps to authenticate as service account rather than Google / GSuite User. Learn more about service account here

   Basically to call Google API as Service account we need to perform following steps listed in 3 sections (Detailed steps found in the help link at the end)

   Create Project

   First thing is create a Project so we can call Google API. Skip this section if you already have Project (Go to next section)

   1. Go to Google API Console
   2. From the Project Dropdown (usually found at the top bar) click Select Project
   3. On Project Propup click CREATE PROJECT
   4. Once project is created you can click Select Project to switch the context (You can click on Notification link or Choose from Top Dropdown)
   5. Click ENABLE APIS AND SERVICES
   6. Now we need to Enable two APIs one by one (BigQuery API and Cloud Resource Manager API).
   7. Search BigQuery API. Select and click ENABLE
   8. Search Cloud Resource Manager API. Select and click ENABLE

   Create Service Account

   Once Project is created and APIs are enabled we can now create a service account under that project. Service account has its ID which looks like some email ID (not to confuse with Google /Gmail email ID)

   1. Go to Create Service Account
   2. From the Project Dropdown (usually found at the top bar) click Select Project
   3. Enter Service account name and Service account description
   4. Click on Create. Now you should see an option to assign Service Account permissions (See Next Section).

   Give Permission to Service Account

   By default service account cant access BigQuery data or List BigQuery Projects so we need to give that permission using below steps.

   1. After you Create Service Account look for Permission drop down in the Wizard.
   2. Choose BigQuery -> BigQuery Admin role so we can read/write data. (NOTE: If you just need read only access then you can choose BigQuery Data Viewer)
   3. Now choose one more Project -> Viewer and add that role so we can query Project Ids.
   4. Click on Continue. Now you should see an option to Create Key (See Next Section).

   Create Key (P12)

   Once service account is created and Permission is assigned we need to create key file.

   1. In the Cloud Console, click the email address for the service account that you created.
   2. Click Keys.
   3. Click Add key, then click Create new key.
   4. Click Create and select P12 format. A P12 key file is downloaded to your computer. We will use this file in our API connection.
   5. Click Close.
   6. Now you may use downloaded *.p12 key file as secret file and Service Account Email as Client ID (e.g. some_name@some_name.iam.gserviceaccount.com).

   Manage Permissions / Give Access to Other Projects

   We saw how to add permissions for Service Account during Account Creation Wizard but if you ever wish to edit after its created or you wish to give permission for other projects then perform forllowing steps.

   1. From the top Select Project for which you like to edit Permission.
   2. Go to IAM Menu option (here)
      Link to IAM: https://console.cloud.google.com/iam-admin/iam
   3. Goto Permissions tab. Over there you will find ADD button.
   4. Enter Service account email for which you like to grant permission. Select role you wish to assign.

   Fill in all required parameters and set optional parameters if needed:

   GoogleBigQueryDSN

   Google BigQuery

   Service Account (Using Private Key File) [OAuth]

   https://www.googleapis.com/bigquery/v2

   Required Parameters
   Service Account Email	Fill in the parameter...
   P12 Service Account Private Key Path (i.e. *.p12)	Fill in the parameter...
   ProjectId	Fill in the parameter...
   DatasetId (Choose after ProjectId)	Fill in the parameter...
   Optional Parameters
   Scope	Fill in the parameter...
   RetryMode	Fill in the parameter...
   RetryStatusCodeList	Fill in the parameter...
   RetryCountMax	Fill in the parameter...
   RetryMultiplyWaitTime	Fill in the parameter...
   Job Location	Fill in the parameter...

   

6. Once the data source has been configured, you can preview data. Select the Preview tab and use settings similar to the following to preview data:
   

7. Click OK to finish creating the data source.

Video instructions

Read data in Python

Using ODBC DSN

1. Python code to get the data:
   

   GoogleBigQueryDSN')

   

2. When you run the code it will make the API call and read the data:
   

3. Here is Python program's code in text format:

   
       import pyodbc
       conn = pyodbc.connect('DSN=GoogleBigQueryDSN')
   
       cursor = conn.cursor()
   
       #execute query to fetch data from API service
       cursor.execute("SELECT id,title FROM products")
   
       row = cursor.fetchone()
       while row:
           print(row)
           row = cursor.fetchone()
   
       ##For loop example
       #for row in cursor:
       #    print(row)
   

Using a full ODBC connection string

If you want to avoid being dependent on a DSN and creating multiple DSNs for each platform (x86, x64), then you can use a fully qualified connection string. Simply go to your DSN and copy the Connection String:

1. Open ODBC data source configuration and click Copy settings:
   

   ZappySys API Driver - Google BigQuery

   Read / write Google BigQuery data inside your app without coding using easy to use high performance API Connector

   GoogleBigQueryDSN

   
2. The window opens, telling us the connection string was successfully copied to the clipboard:

3. Then in your Python code use Connection String when initializing OdbcConnection object, for example:

   conn = pyodbc.connect('DRIVER={ZappySys API Driver};ServiceUrl=https://yourservices.provider.com/api/xxxx....;AuthName=Http;')

How to install `pyodbc` in the Python?

Advanced topics

Create Custom Stored Procedure in ZappySys Driver

You can create procedures to encapsulate custom logic and then only pass handful parameters rather than long SQL to execute your API call.

Steps to create Custom Stored Procedure in ZappySys Driver. You can insert Placeholders anywhere inside Procedure Body. Read more about placeholders here

1. Go to Custom Objects Tab and Click on Add button and Select Add Procedure:
   

2. Enter the desired Procedure name and click on OK:
   

3. Select the created Stored Procedure and write the your desired stored procedure and Save it and it will create the custom stored procedure in the ZappySys Driver:
   Here is an example stored procedure for ZappySys Driver. You can insert Placeholders anywhere inside Procedure Body. Read more about placeholders here
   

   CREATE PROCEDURE [usp_get_orders]
       @fromdate = '<<yyyy-MM-dd,FUN_TODAY>>'
    AS
       SELECT * FROM Orders where OrderDate >= '<@fromdate>';
   

   
   

4. That's it now go to Preview Tab and Execute your Stored Procedure using Exec Command. In this example it will extract the orders from the date 1996-01-01:
   

   Exec usp_get_orders '1996-01-01';

   
   

5. Let's generate the SQL Server Query Code to make the API call using stored procedure. Go to Code Generator Tab, select language as SQL Server and click on Generate button the generate the code.
   As we already created the linked server for this Data Source, in that you just need to copy the Select Query and need to use the linked server name which we have apply on the place of [MY_API_SERVICE] placeholder.
   

   SELECT * FROM OPENQUERY([MY_API_SERVICE], 'EXEC usp_get_orders @fromdate=''1996-07-30''')

   
   

6. Now go to SQL served and execute that query and it will make the API call using stored procedure and provide you the response.
   

Create Custom Virtual Table in ZappySys Driver

ZappySys API Drivers support flexible Query language so you can override Default Properties you configured on Data Source such as URL, Body. This way you don't have to create multiple Data Sources if you like to read data from multiple EndPoints. However not every application support supplying custom SQL to driver so you can only select Table from list returned from driver.

If you're dealing with Microsoft Access and need to import data from an SQL query, it's important to note that Access doesn't allow direct import of SQL queries. Instead, you can create custom objects (Virtual Tables) to handle the import process.

Many applications like MS Access, Informatica Designer wont give you option to specify custom SQL when you import Objects. In such case Virtual Table is very useful. You can create many Virtual Tables on the same Data Source (e.g. If you have 50 URLs with slight variations you can create virtual tables with just URL as Parameter setting.

1. Go to Custom Objects Tab and Click on Add button and Select Add Table:
   

2. Enter the desired Table name and click on OK:
   

3. And it will open the New Query Window Click on Cancel to close that window and go to Custom Objects Tab.

4. Select the created table, Select Text Type AS SQL and write the your desired SQL Query and Save it and it will create the custom table in the ZappySys Driver:
   Here is an example SQL query for ZappySys Driver. You can insert Placeholders also. Read more about placeholders here
   

   SELECT
     "ShipCountry",
     "OrderID",
     "CustomerID",
     "EmployeeID",
     "OrderDate",
     "RequiredDate",
     "ShippedDate",
     "ShipVia",
     "Freight",
     "ShipName",
     "ShipAddress",
     "ShipCity",
     "ShipRegion",
     "ShipPostalCode"
   FROM "Orders"
   Where "ShipCountry"='USA'

   
   

5. That's it now go to Preview Tab and Execute your custom virtual table query. In this example it will extract the orders for the USA Shipping Country only:
   

   SELECT * FROM "vt__usa_orders_only"

   
   

6. Let's generate the SQL Server Query Code to make the API call using stored procedure. Go to Code Generator Tab, select language as SQL Server and click on Generate button the generate the code.
   As we already created the linked server for this Data Source, in that you just need to copy the Select Query and need to use the linked server name which we have apply on the place of [MY_API_SERVICE] placeholder.
   

   SELECT * FROM OPENQUERY([MY_API_SERVICE], 'EXEC [usp_get_orders] ''1996-01-01''')

   
   

7. Now go to SQL served and execute that query and it will make the API call using stored procedure and provide you the response.
   

Actions supported by Google BigQuery Connector

Google BigQuery Connector Examples for Python Connection

This page offers a collection of SQL examples designed for seamless integration with the ZappySys API ODBC Driver under ODBC Data Source (36/64) or ZappySys Data Gateway, enhancing your ability to connect and interact with Prebuilt Connectors effectively.

#DirectSQL SELECT * FROM bigquery-public-data.samples.wikipedia LIMIT 1000 /* try your own dataset or Some FREE dataset like nyc-tlc.yellow.trips -- 3 parts ([Project.]Dataset.Table) */

#DirectSQL 
SELECT name, gender, SUM(number) AS total
FROM bigquery-public-data.usa_names.usa_1910_2013
GROUP BY name, gender
ORDER BY total DESC
LIMIT 10

#DirectSQL DELETE FROM TestDataset.MyTable Where Id > 5

#DirectSQL 
SELECT id, col_timestamp, CAST(col_timestamp as DATE) AS timestamp_as_date, CAST(col_timestamp as DATETIME) AS timestamp_as_datetime
FROM MyProject.MyDataset.MyTable
LIMIT 10

#DirectSQL CREATE TABLE TestDataset.Table1 (ID INT64,Name STRING,BirthDate DATETIME, Active BOOL)

#DirectSQL 
#DirectSQL 
Update TestDataset.DataTypeTest 
Set ColTime='23:59:59.123456',
 ColGeography=ST_GEOGPOINT(34.150480, -84.233870),
 ColRecord=(1,"AA","Column3 data"),
 ColBigNumeric=1222222222222222222.123456789123456789123456789123456789,
 ColJson= JSON_ARRAY('{"doc":1, "values":[{"id":1},{"id":2}]}') 
Where ColInteger=1

#DirectSQL DROP TABLE IF EXISTS Myproject.Mydataset.Mytable

#DirectSQL CALL TestDataset.usp_GetData(1)

INSERT INTO MyBQTable1(SomeBQCol1, SomeBQCol2) Values(1,'AAA')
--WITH(DatasetId='TestDataset',Output='*')
--WITH(DatasetId='TestDataset',ProjectId='MyProjectId',Output='*')

INSERT INTO MyBQTable1 
SOURCE(
    'MSSQL'
  , 'Data Source=localhost;Initial Catalog=tempdb;Initial Catalog=tempdb;Integrated Security=true'
  , 'SELECT Col1 as SomeBQCol1,Col2 as SomeBQCol2 FROM SomeTable Where SomeCol=123'
)
--WITH(DatasetId='TestDataset',Output='*')
--WITH(DatasetId='TestDataset',ProjectId='MyProjectId',Output='*')

INSERT INTO MyBQTable1 
SOURCE(
    'ODBC'
  , 'DSN=MyDsn'
  , 'SELECT Col1 as SomeBQCol1,Col2 as SomeBQCol2 FROM SomeTable Where SomeCol=123'
) 
WITH(DatasetId='TestDataset')

INSERT INTO MyBQTable1 
SOURCE(
    'ODBC'
  , 'Driver={ZappySys JSON Driver};Src='https://some-url/get-data''
  , 'SELECT Col1 as SomeBQCol1,Col2 as SomeBQCol2 FROM _root_'
)
--WITH(DatasetId='TestDataset',Output='*')
--WITH(DatasetId='TestDataset',ProjectId='MyProjectId',Output='*')

SELECT * FROM list_projects

SELECT * FROM list_datasets
--WITH(ProjectId='MyProjectId')

SELECT * FROM list_tables
--WITH(ProjectId='MyProjectId')
--WITH(ProjectId='MyProjectId',DatasetId='MyDatasetId')

SELECT * FROM delete_dataset WITH(DatasetId='MyDatasetId', deleteContents='False')

Conclusion

In this article we discussed how to connect to Google BigQuery in Python and integrate data without any coding. Click here to Download Google BigQuery Connector for Python and try yourself see how easy it is. If you still have any question(s) then ask here or simply click on live chat icon below and ask our expert (see bottom-right corner of this page).

Download Google BigQuery Connector for Python Documentation 

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Build your own Custom API Connector

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