SQL Server Google BigQuery Connector

In this article you will learn how to integrate Google BigQuery data to SQL Server without coding in just a few clicks (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 SQL Server. Follow the steps below to see how we would accomplish that.

Download  Help File  Buy 

Video Tutorial - Integrate Google BigQuery data in SQL Server

This video covers following and more so watch carefully. After watching this video follow the steps described in this article.

  • How to download / install required driver for Google BigQuery integration in SQL Server
  • How to configure connection for Google BigQuery
  • Features about API Driver (Authentication / Query Language / Examples / Driver UI)
  • Using Google BigQuery Connection in SQL Server

Create Data Source in ZappySys Data Gateway based on ZappySys API Driver

  1. Download and install ZappySys ODBC PowerPack.

  2. Search for gateway in start menu and Open ZappySys Data Gateway:
    Open ZappySys Data Gateway

  3. Go to Users Tab to add our first Gateway user. Click Add; we will give it a name tdsuser and enter password you like to give. Check Admin option and click OK to save. We will use these details later when we create linked server:
    ZappySys Data Gateway - Add User

  4. Now we are ready to add a data source. Click Add, give data source a name (Copy this name somewhere, we will need it later) and then select Native - ZappySys API Driver. Finally, click OK. And it will create the Data Set for it and open the ZS driver UI.

    GoogleBigQueryDSN

    ZappySys Data Gateway - Add Data Source

  5. 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
    ODBC DSN Template Selection

  6. 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.

    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. You can use this information now to configure Connection with UseCustomApp=true.

    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 Fill in the parameter...
    DatasetId 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...
    ODBC DSN Oauth Connection Configuration
    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...
    Service Account Private Key Path (i.e. *.p12) Fill in the parameter...
    ProjectId Fill in the parameter...
    DatasetId 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...
    ODBC DSN Oauth Connection Configuration

  7. 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:
    ODBC ZappySys Data Source Preview

  8. Click OK to finish creating the data source.

Read data in SQL Server from the ZappySys Data Gateway

  1. To read the data in SQL Server the first thing you have to do is create a Linked Server. Go to SQL Server Management Studio and configure it in a similar way:
    SSMS SQL Server Configure Linked Server

  2. Then click on Security option and configure username we created in ZappySys Data Gateway in one of the previous steps:
    SSMS SQL Server Configure Linked Server User Name

  3. Optional: Under the Server Options, Enable RPC and RPC Out and Disable Promotion of Distributed Transactions(MSDTC).

    RPC and MSDTC Settings

    You need to enable RPC Out if you plan to use EXEC(...) AT [MY_LINKED_SERVER_NAME] rather than OPENQUERY.
    If don't enabled it, you will encounter the Server 'MY_LINKED_SERVER_NAME' is not configured for RPC error.

    Query Example:

    EXEC('Select * from Products') AT [MY_LINKED_SERVER_NAME]


    If you plan to use 'INSERT INTO...EXEC(....) AT [MY_LINKED_SERVER_NAME]' in that case you need to Disable Promotion of Distributed Transactions(MSDTC).
    If don't disabled it, you will encounter the The operation could not be performed because OLE DB provider "SQLNCLI11" for linked server "MY_LINKED_SERVER_NAME" was unable to begin a distributed transaction. error.

    Query Example:

    Insert Into dbo.Products
     EXEC('Select * from Products') AT [MY_LINKED_SERVER_NAME]
    


  4. Finally, open a new query and execute a query we saved in one of the previous steps:

    SELECT * FROM OPENQUERY([MY_LINKED_SERVER_NAME], 'SELECT * FROM Products');

    SSMS SQL Server Query Data Results

Create Linked Server using Code

In previous section you saw how to create a Linked Server from UI. You can do similar action by code too (see below). Run below script after changing necessary parameters. Assuming your Data Source name on ZappySys Data Gateway UI is 'GoogleBigQueryDSN'

    USE [master]
    GO
    --///////////////////////////////////////////////////////////////////////////////////////
    --Run below code in SSMS to create Linked Server and use ZappySys Drivers in SQL Server
    --///////////////////////////////////////////////////////////////////////////////////////

    //Replace YOUR_GATEWAY_USER, YOUR_GATEWAY_PASSWORD
    //Replace localhost with IP/Machine name if ZappySys Gateway Running on different machine other than SQL Server
    //Replace Port 5000 if you configured gateway on a different port


    --1. Configure your gateway service as per this article https://zappysys.com/links?id=10036
    
    --2. Make sure you have SQL Server Installed. You can download FREE SQL Server Express Edition from here if you dont want to buy Paid version https://www.microsoft.com/en-us/sql-server/sql-server-editions-express

    --Uncomment below if you like to drop linked server if it already exists
    --EXEC master.dbo.sp_dropserver @server=N'LS_GoogleBigQueryDSN', @droplogins='droplogins'

    --3. Create new linked server
    
    EXEC master.dbo.sp_addlinkedserver
      @server = N'LS_GoogleBigQueryDSN'  --Linked server name (this will be used in OPENQUERY sql
    , @srvproduct=N''
    , @provider=N'SQLNCLI11'
    , @datasrc=N'localhost,5000' --//Machine / Port where Gateway service is running
    , @provstr=N'Network Library=DBMSSOCN;'
    , @catalog=N'GoogleBigQueryDSN' --Data source name you gave on Gateway service settings

    --4. Attach gateway login with linked server

    EXEC master.dbo.sp_addlinkedsrvlogin
      @rmtsrvname=N'LS_GoogleBigQueryDSN'  --linked server name
    , @useself=N'False'
    , @locallogin=NULL
    , @rmtuser=N'YOUR_GATEWAY_USER' --enter your Gateway user name
    , @rmtpassword='YOUR_GATEWAY_PASSWORD'  --enter your Gateway user's password
    GO

    --5. Enable RPC OUT (This is Optional - Only needed if you plan to use EXEC(...) AT YourLinkedServerName rather than OPENQUERY
    EXEC sp_serveroption 'LS_GoogleBigQueryDSN', 'rpc', true;
    EXEC sp_serveroption 'LS_GoogleBigQueryDSN', 'rpc out', true;

    --Disable MSDTC - Below needed to support INSERT INTO from EXEC AT statement
    EXEC sp_serveroption 'LS_GoogleBigQueryDSN', 'remote proc transaction promotion', false;
    
    --Increase query timeout if query is going to take longer than 10 mins (Default timeout is 600 seconds)
    --EXEC sp_serveroption 'LS_GoogleBigQueryDSN', 'query timeout', 1200;
    GO

Firewall settings

So far we have assumed that Gateway is running on the same machine as SQL Server. However there will be a case when ZappySys ODBC PowerPack is installed on a different machine than SQL Server. In such case you may have to perform additional Firewall configurations. On most computers firewall settings wont allow outside traffic to ZappySys Data Gateway. In such case perform following steps to allow other machines to connect to Gateway.

Method-1 (Preferred)

If you are using newer version of ZappySys Data Gateway then adding firewall rule is just a single click.

  1. Search for gateway in start menu and open ZappySys Data Gateway.
  2. Go to Firewall Tab and click Add Firewall Rule button like below. This will create Firewall rule to all Inbound Traffic on Port 5000 (Unless you changed it). Allow Inbound Traffic - Add Firewall Rule for ZappySys Data Gateway

Method-2 Here is another way to add / edit Inbound Traffic rule in windows firewall. Use below method if you choose to customize your rule (for advanced users).
  1. Search for Windows Firewall Advanced Security in start menu.
  2. Under Inbound Rules > Right click and click [New Rule] >> Click Next
  3. Select Port on Rule Type >> Click Next
  4. Click on TCP and enter port number under specified local port as 5000 (use different one if you changed Default port) >> Click Next
  5. Select Profile (i.e. Private, Public) >> Click Next
  6. Enter Rule name [i.e. ZappySys Data Gateway – Allow Inbound ] >> Click Next
  7. Click OK to save the rule
SQL Server Firewall Allow Inbound Data Gateway

OPENQUERY vs EXEC (handling larger SQL text)

So far we have seen examples of using OPENQUERY. It allows us to send pass-through query at remote server. The biggest limitation of OPENQUERY is it doesn't allow you to use variables inside SQL so often we have to use unpleasant looking dynamic SQL (Lots of tick, tick …. and escape hell). Well there is good news. With SQL 2005 and later you can use EXEC(your_sql) AT your_linked_server syntax . Disadvantage of EXEC AT is you cannot do SELECT INTO like OPENQUERY. Also you cannot perform JOIN like below in EXEC AT


    SELECT a.* FROM OPENQUERY([ls_GoogleBigQueryDSN],'select * from Customers') a
    JOIN OPENQUERY([ls_GoogleBigQueryDSN],'select * from Orders') b ON a.CustomerId=b.CustomerId;

However you can always do INSERT INTO SomeTable EXEC(…) AT your_linked_server. So table must exists when you do that way. Here is how to use it. To use EXEC(..) AT {linked-server} you must turn on RPC OUT option. Notice how we used variable in SQL to make it dynamic. This is much cleaner than previous approach we saw.

    USE [master]
    GO
    --Replace YOUR_GATEWAY_USER, YOUR_GATEWAY_PASSWORD
    --Replace localhost with IP/Machine name if ZappySys Gateway Running on different machine other than SQL Server

    --Create new linked server
    EXEC master.dbo.sp_addlinkedserver
      @server = N'LS_GoogleBigQueryDSN'  --Linked server name (this will be used in OPENQUERY sql)
    , @srvproduct=N''
    , @provider=N'SQLNCLI11'
    , @datasrc=N'localhost,5000' --//Machine / Port where Gateway service is running
    , @provstr=N'Network Library=DBMSSOCN;'
    , @catalog=N'GoogleBigQueryDSN' --Data source name you gave on Gateway service settings

    --Attach gateway login with linked server
    EXEC master.dbo.sp_addlinkedsrvlogin
      @rmtsrvname=N'LS_GoogleBigQueryDSN'  --linked server name
    , @useself=N'False'
    , @locallogin=NULL
    , @rmtuser=N'YOUR_GATEWAY_USER' --enter your Gateway user name
    , @rmtpassword='YOUR_GATEWAY_PASSWORD'  --enter your Gateway user's password
    GO

    --5. Enable RPC OUT (This is Optional - Only needed if you plan to use EXEC(...) AT YourLinkedServerName rather than OPENQUERY
    EXEC sp_serveroption 'LS_GoogleBigQueryDSN', 'rpc', true;
    EXEC sp_serveroption 'LS_GoogleBigQueryDSN', 'rpc out', true;
    --Disable MSDTC - Below needed to support INSERT INTO from EXEC AT statement
    EXEC sp_serveroption 'LS_GoogleBigQueryDSN', 'remote proc transaction promotion', false;
    --Increase query timeout if query is going to take longer than 10 mins (Default timeout is 600 seconds)
    --EXEC sp_serveroption 'LS_GoogleBigQueryDSN', 'query timeout', 1200;
    GO

Here is the difference between OPENQUERY vs EXEC approaches: OPENQUERY vs EXEC

Fetching Tables / Columns using metadata stored procs

ZappySys Data Gateway emulates certains system procs you might find in real SQL Server. You can call using below syntax using 4-Parts syntax
exec [linked-server-name].[gateway-datasource-name].[DATA].sp_tables
exec [linked-server-name].[gateway-datasource-name].[DATA].sp_columns_90 N'your-table-name'
Example:

    //List all tables
    exec [ls_GoogleBigQueryDSN].[GoogleBigQueryDSN].[DATA].sp_tables

    //List all columns and its type for specified table
    exec [ls_GoogleBigQueryDSN].[GoogleBigQueryDSN].[DATA].sp_columns_90 N'Account'

Known Issues

Let's explore some common problems that can occur when using OPENQUERY or Data Gateway connectivity.


Error: The data is invalid

There will be a time when, you may encounter unexpected errors like the ones listed below. These can include:

OLE DB provider "SQLNCLI11" for linked server "Zs_Csv" returned message "Deferred prepare could not be completed.".
OLE DB provider "SQLNCLI11" for linked server "Zs_Csv" returned message "Communication link failure".
Msg 13, Level 16, State 1, Line 0

Session Provider: The data is invalid.
Possible Cause:

There are few reasons for such error but below are two main reasons

  • If the query length exceeds 2000 characters, as shown below, you might encounter this error.

    SELECT * FROM OPENQUERY(LS, '--some really long text more than 2000 chars--')
  • If a query contains multiple OPENQUERY statements for JOINs or UNIONs, as shown below, it might fail due to a MARS compatibility issue where the gateway doesn't support parallel queries on a single connection.

    SELECT a.id, b.name from OPENQUERY(LS, 'select * from tbl1') a join OPENQUERY(LS, 'select * from tbl2') b on a.id=b.id
Possible Fix:

There are few ways to fix above error based on reason why you getting this error (i.e. Query Length issue OR JOIN/UNION in the same statement)

  • If your query has long SQL (more than 2000 chars ) then reduce SQL length using different techniques
    • e.g. use SELECT * FROM MyTable rather than SELECT col1,col2… FROM MyTable
    • Use Meta Option in WITH clause if you must use column name. (e.g. SELECT * FROM MyTable WITH(META=’c:\meta.txt’) this way you can define column in Meta file rather than SELECT query. Check this article
    • Consider using EXECT (….) AT [Linked_Server_name] option rather than OPENQUERY so you can use very long SQL (See next section on EXEC..AT usecase)
    • Consider using Virtual Table / Stored Proc to wrap long SQL so your call is very small (where usp_GetOrdersByYear is custom proc created on ZappySys Driver UI)
      SELECT * FROM OPENQUERY(LS, 'EXEC usp_GetOrdersByYear 2021')
  • If your query uses JOIN  / UNION with multiple OPENQUERY in same SQL then use multiple Linked servers (one for each OPENQUERY clause) as below.
    select a.id, b.name from OPENQUERY(LS_1, 'select * from tbl1') a join OPENQUERY(LS_2, 'select * from tbl2') b on a.id=b.id

Error: Unable to begin a distributed transaction (When INSERT + EXEC used)

If you try to use the EXEC statement to insert data into a table, as shown below, you might encounter the following error unless the MSDTC option is turned off.

INSERT INTO MyTable EXEC('select * from tbl') AT MyLinkedServer
"Protocol error in TDS stream"
The operation could not be performed because OLE DB provider "SQLNCLI11" for linked server "ls_Json2" was unable to begin a distributed transaction.
--OR--
The operation could not be performed because OLE DB provider "MSOLEDBSQL" for linked server "ls_Json" was unable to begin a distributed transaction.

Solution:
Method-1: Go to linked server properties | Server Options | Enable Promotion of Distributed Transaction | Change to false (Default is true)
Now your try your INSERT with EXEC AT and it should work

Method-2: Run the below command if you dont want to use UI

EXEC master.dbo.sp_serveroption @server=N'My_Linked_Server', @optname=N'remote proc transaction promotion', @optvalue=N'false'

Error: Cannot use OPENQUERY with JOIN / UNION

When you perform a JOIN or UNION ALL on the same Linked Server, it may fail to process sometimes because the Data Gateway doesn't support parallel query requests on the same connection. A workaround for that would be to create multiple linked servers for the same data source. Refer to the section above for the same workaround.


Error: Truncation errors due to data length mismatch

Many times, you may encounter truncation errors if a table column's length is less than the actual column size from the query column. To solve this issue, use the new version of Data Gateway and check the 'Use nvarchar(max) for string options' option found on the General Tab.


Performance Tips

Now, let's look at a few performance tips in this section.


Use INSERT INTO rather than SELECT INTO to avoid extra META request

We discussed some Pros and Cons of OPENQUERY vs EXEC (…) AT in previous section. One obvious advantage of EXEC (….) AT is it reduces number of requests to driver (It sends pass through query). With EXEC you cannot load data dynamically like SELECT INTO tmp FROM OPENQUERY. Table must exist before hand if you use EXEC.


INSERT INTO tmp_API_Report_Load(col1,col2)
EXEC('select col1,col2 from some_api_table') AT [API-LINKED-SERVER]
--OR--
INSERT INTO tmp_API_Report_Load(col1,col2)
select col1,col2 from OPENQUERY([API-LINKED-SERVER], 'select col1,col2 from some_api_table')

The advantage of this method is that your query speed will increase because the system only calls the API once when you use EXEC AT. In contrast, with OPENROWSET, the query needs to be called twice: once to obtain metadata and once to retrieve the data.


Use Cached Metadata if possible

By default, most SQL queries sent to the Data Gateway need to invoke two phases: first, to get metadata, and second, to fetch data. However, you can bypass the metadata API call by supplying static metadata. Use the META property in the WITH clause, as explained in this article, to speed up your SQL queries.

Create Custom Store 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 Store 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:
    ZappySys Driver - Add Store Procedure

  2. Enter the desired Procedure name and click on OK:
    ZappySys Driver - Add Store Procedure Name

  3. Select the created Store Procedure and write the your desired store procedure and Save it and it will create the custom store 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>';
    

    ZappySys Driver - Create Custom Store Procedure

  4. That's it now go to Preview Tab and Execute your Store 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';

    ZappySys Driver - Execute Custom Store Procedure

  5. Let's generate the SQL Server Query Code to make the API call using store 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''')

    ZappySys Driver - Generate SQL Server Query

  6. Now go to SQL served and execute that query and it will make the API call using store procedure and provide you the response.
    ZappySys Driver - Generate SQL Server Query

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:
    ZappySys Driver - Add Table

  2. Enter the desired Table name and click on OK:
    ZappySys Driver - Add Table Name

  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'

    ZappySys Driver - Create Custom Table

  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"

    ZappySys Driver - Execute Custom Virtual Table Query

  6. Let's generate the SQL Server Query Code to make the API call using store 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''')

    ZappySys Driver - Generate SQL Server Query

  7. Now go to SQL served and execute that query and it will make the API call using store procedure and provide you the response.
    ZappySys Driver - Generate SQL Server Query

Conclusion

In this article we discussed how to connect to Google BigQuery in SQL Server and integrate data without any coding. Click here to Download Google BigQuery Connector for SQL Server 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 SQL Server Documentation 

Actions supported by Google BigQuery Connector

Google BigQuery Connector support following actions for REST API integration. If some actions are not listed below then you can easily edit Connector file and enhance out of the box functionality.
 Read Data using SQL Query -OR- Execute Script (i.e. CREATE, SELECT, INSERT, UPDATE, DELETE)
Runs a BigQuery SQL query synchronously and returns query results if the query completes within a specified timeout    [Read more...]
Parameter Description
SQL Statement (i.e. SELECT / DROP / CREATE)
Option Value
Example1 SELECT title,id,language,wp_namespace,reversion_id ,comment,num_characters FROM bigquery-public-data.samples.wikipedia LIMIT 1000
Example2 CREATE TABLE TestDataset.Table1 (ID INT64,Name STRING,BirthDate DATETIME, Active BOOL)
Example3 INSERT TestDataset.Table1 (ID, Name,BirthDate,Active) VALUES(1,&#39;AA&#39;,&#39;2020-01-01&#39;,true),(2,&#39;BB&#39;,&#39;2020-01-02&#39;,true),(3,&#39;CC&#39;,&#39;2020-01-03&#39;,false)
Use Legacy SQL Syntax?
Option Value
false false
true true
timeout (Milliseconds) Wait until timeout is reached.
Option Value
false false
true true
Job Location The geographic location where the job should run. For Non-EU and Non-US datacenters we suggest you to supply this parameter to avoid any error.
Option Value
System Default
Data centers in the United States US
Data centers in the European Union EU
Columbus, Ohio us-east5
Iowa us-central1
Las Vegas us-west4
Los Angeles us-west2
Montr&#233;al northamerica-northeast1
Northern Virginia us-east4
Oregon us-west1
Salt Lake City us-west3
S&#227;o Paulo southamerica-east1
Santiago southamerica-west1
South Carolina us-east1
Toronto northamerica-northeast2
Delhi asia-south2
Hong Kong asia-east2
Jakarta asia-southeast2
Melbourne australia-southeast2
Mumbai asia-south1
Osaka asia-northeast2
Seoul asia-northeast3
Singapore asia-southeast1
Sydney australia-southeast1
Taiwan asia-east1
Tokyo asia-northeast1
Belgium europe-west1
Finland europe-north1
Frankfurt europe-west3
London europe-west2
Madrid europe-southwest1
Milan europe-west8
Netherlands europe-west4
Paris europe-west9
Warsaw europe-central2
Z&#252;rich europe-west6
AWS - US East (N. Virginia) aws-us-east-1
Azure - East US 2 azure-eastus2
Custom Name (Type your own) type-region-id-here
 Read Table Rows
Gets the specified table resource by table ID. This method does not return the data in the table, it only returns the table resource, which describes the structure of this table.    [Read more...]
Parameter Description
ProjectId Leave this value blank to use ProjectId from connection settings
DatasetId Leave this value blank to use DatasetId from connection settings
TableId
 [$parent.tableReference.datasetId$].[$parent.tableReference.tableId$]
Read data from [$parent.tableReference.datasetId$].[$parent.tableReference.tableId$] for project .    [Read more...]
Parameter Description
 List Projects
Lists Projects that the caller has permission on and satisfy the specified filter.    [Read more...]
Parameter Description
SearchFilter An expression for filtering the results of the request. Filter rules are case insensitive. If multiple fields are included in a filter query, the query will return results that match any of the fields. Some eligible fields for filtering are: name, id, labels.{key} (where key is the name of a label), parent.type, parent.id, lifecycleState. Example: name:how*
 List Datasets
Lists all BigQuery datasets in the specified project to which the user has been granted the READER dataset role.    [Read more...]
Parameter Description
ProjectId
SearchFilter An expression for filtering the results of the request. Filter rules are case insensitive. If multiple fields are included in a filter query, the query will return results that match any of the fields. Some eligible fields for filtering are: name, id, labels.{key} (where key is the name of a label), parent.type, parent.id, lifecycleState. Example: name:how*
all Whether to list all datasets, including hidden ones
Option Value
True True
False False
 Create Dataset
Creates a new empty dataset.    [Read more...]
Parameter Description
ProjectId
Dataset Name Enter dataset name
Description
 Delete Dataset
Deletes the dataset specified by the datasetId value. Before you can delete a dataset, you must delete all its tables, either manually or by specifying deleteContents. Immediately after deletion, you can create another dataset with the same name.    [Read more...]
Parameter Description
ProjectId
DatasetId
Delete All Tables If True, delete all the tables in the dataset. If False and the dataset contains tables, the request will fail. Default is False
Option Value
True True
False False
 Delete Table
Deletes the dataset specified by the datasetId value. Before you can delete a dataset, you must delete all its tables, either manually or by specifying deleteContents. Immediately after deletion, you can create another dataset with the same name.    [Read more...]
Parameter Description
ProjectId
DatasetId
TableId
 List Tables
Lists BigQuery Tables for the specified project / dataset to which the user has been granted the READER dataset role.    [Read more...]
Parameter Description
ProjectId
DatasetId
 Get Query Schema (From SQL)
Runs a BigQuery SQL query synchronously and returns query schema    [Read more...]
Parameter Description
SQL Query
Use Legacy SQL Syntax?
Option Value
false false
true true
timeout (Milliseconds) Wait until timeout is reached.
Option Value
false false
true true
Job Location The geographic location where the job should run. For Non-EU and Non-US datacenters we suggest you to supply this parameter to avoid any error.
Option Value
System Default
Data centers in the United States US
Data centers in the European Union EU
Columbus, Ohio us-east5
Iowa us-central1
Las Vegas us-west4
Los Angeles us-west2
Montr&#233;al northamerica-northeast1
Northern Virginia us-east4
Oregon us-west1
Salt Lake City us-west3
S&#227;o Paulo southamerica-east1
Santiago southamerica-west1
South Carolina us-east1
Toronto northamerica-northeast2
Delhi asia-south2
Hong Kong asia-east2
Jakarta asia-southeast2
Melbourne australia-southeast2
Mumbai asia-south1
Osaka asia-northeast2
Seoul asia-northeast3
Singapore asia-southeast1
Sydney australia-southeast1
Taiwan asia-east1
Tokyo asia-northeast1
Belgium europe-west1
Finland europe-north1
Frankfurt europe-west3
London europe-west2
Madrid europe-southwest1
Milan europe-west8
Netherlands europe-west4
Paris europe-west9
Warsaw europe-central2
Z&#252;rich europe-west6
AWS - US East (N. Virginia) aws-us-east-1
Azure - East US 2 azure-eastus2
Custom Name (Type your own) type-region-id-here
 Get Table Schema
Gets the specified table resource by table ID. This method does not return the data in the table, it only returns the table resource, which describes the structure of this table.    [Read more...]
Parameter Description
DatasetId
TableId
 insert_table_data
   [Read more...]
Parameter Description
ProjectId
DatasetId
TableId
 post_[$parent.tableReference.datasetId$]_[$parent.tableReference.tableId$]
   [Read more...]
 Generic Request
This is generic endpoint. Use this endpoint when some actions are not implemented by connector. Just enter partial URL (Required), Body, Method, Header etc. Most parameters are optional except URL.    [Read more...]
Parameter Description
Url API URL goes here. You can enter full URL or Partial URL relative to Base URL. If it is full URL then domain name must be part of ServiceURL or part of TrustedDomains
Body Request Body content goes here
IsMultiPart Check this option if you want to upload file(s) (i.e. POST RAW file data) or send data using Multi-Part encoding method (i.e. Content-Type: multipart/form-data). Multi-Part request allows you to mix key/value and upload files in same request. On the other hand raw upload allows only single file upload (without any key/value) ==== Raw Upload (Content-Type: application/octet-stream) ===== To upload single file in raw mode check this option and specify full file path starting with @ sign in the Body (e.g. @c:\data\myfile.zip ) ==== Form-Data / Multipart Upload (Content-Type: multipart/form-data) ===== To treat your Request data as multi part fields you must specify key/value pairs separated by new lines into RequestData field (i.e. Body). Each key value pair is entered on new-line and key/value are separated using equal sign (=). Preceding and trailing spaces are ignored also blank lines are ignored. If field value has some any special character(s) then use escape sequence (e.g. For NewLine: \r\n, For Tab: \t, For at (@): \@). When value of any field starts with at sign (@) its automatically treated as File you want to upload. By default file content type is determined based on extension however you can supply content type manually for any field using this way [ YourFileFieldName.Content-Type=some-content-type ]. By default File Upload Field always includes Content-Type in the request (non file fields do not have content-type by default unless you supply manually). For some reason if you dont want to use Content-Type header in your request then supply blank Content-Type to exclude this header altogather [e.g. SomeFieldName.Content-Type= ]. In below example we have supplied Content-Type for file2 and SomeField1, all other fields are using default content-type. See below Example of uploading multiple files along with additional fields. If some API requires you to pass Content-Type: multipart/form-data rather than multipart/form-data then manually set Request Header =&gt; Content-Type: multipart/mixed (it must starts with multipart/ else will be ignored). file1=@c:\data\Myfile1.txt file2=@c:\data\Myfile2.json file2.Content-Type=application/json SomeField1=aaaaaaa SomeField1.Content-Type=text/plain SomeField2=12345 SomeFieldWithNewLineAndTab=This is line1\r\nThis is line2\r\nThis is \ttab \ttab \ttab SomeFieldStartingWithAtSign=\@MyTwitterHandle
Filter Enter filter to extract array from response. Example: $.rows[*] --OR-- $.customers[*].orders[*]. Check your response document and find out hierarchy you like to extract
Headers Headers for Request. To enter multiple headers use double pipe or new line after each {header-name}:{value} pair

Google BigQuery Connector Examples for SQL Server 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.

Native Query (ServerSide): Query using Simple SQL    [Read more...]

Server side BigQuery SQL query example. Prefix SQL with word #DirectSQL to invoke server side engine (Pass-through SQL). Query free dataset table (bigquery-public-data.samples.wikipedia)

#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) */

Native Query (ServerSide): Query using Complex SQL    [Read more...]

Server side SQL query example of BigQuery. Prefix SQL with word #DirectSQL to invoke server side engine (Pass-through SQL). Query free dataset table (bigquery-public-data.usa_names.usa_1910_2013)

#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

Native Query (ServerSide): Create Table / Run Other DDL    [Read more...]

Example of how to run Valid BigQuery DDL statement. Prefix SQL with word #DirectSQL to invoke server side engine (Pass-through SQL)

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

Native Query (ServerSide): DROP Table (if exists) / Other DDL    [Read more...]

Example of how to run Valid BigQuery DDL statement. Prefix SQL with word #DirectSQL to invoke server side engine (Pass-through SQL)

#DirectSQL DROP TABLE IF EXISTS Myproject.Mydataset.Mytable

Native Query (ServerSide): Call Stored Procedure    [Read more...]

Example of how to run BigQuery Stored Procedure and pass parameters. Assuming you created a valid stored proc called usp_GetData in TestDataset, call like below.

#DirectSQL CALL TestDataset.usp_GetData(1)

INSERT Single Row    [Read more...]

This is sample how you can insert into BigQuery using ZappySys query language. You can also use ProjectId='myproject-id' in WITH clause.

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

INSERT Multiple Rows from SQL Server    [Read more...]

This example shows how to bulk insert into Google BigQuery Table from microsoft SQL Server as external source. Notice that INSERT is missing column list. Its provided by source query so must produce valid column names found in target BQ Table (you can use SQL Alias in Column name to produce matching names)

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 Multiple Rows from any ODBC Source (DSN)    [Read more...]

This example shows how to bulk insert into Google BigQuery Table from any external ODBC Source (Assuming you have installed ODBC Driver and configured DSN). Notice that INSERT is missing column list. Its provided by source query so it must produce valid column names found in target BQ Table (you can use SQL Alias in Column name to produce matching names)

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

INSERT Multiple Rows from any JSON Files / API (Using ZappySys ODBC JSON Driver)    [Read more...]

This example shows how to bulk insert into Google BigQuery Table from any external ODBC JSON API / File Source (Assuming you have installed ZappySys ODBC Driver for JSON). Notice that INSERT is missing column list. Its provided by source query so it must produce valid column names found in target BQ Table (you can use SQL Alias in Column name to produce matching names). You can also use similar approach to read from CSV files or XML Files. Just use CSV / XML driver rather than JSON driver in connection string. Refer this for more examples of JSON Query https://zappysys.com/onlinehelp/odbc-powerpack/scr/json-odbc-driver-sql-query-examples.htm

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='*')

List Projects    [Read more...]

Lists Projects for which user has access

SELECT * FROM list_projects

List Datasets    [Read more...]

Lists Datasets for specified project. If you do not specify ProjectId then it will use connection level details.

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

List Tables    [Read more...]

Lists tables for specified project / dataset. If you do not specify ProjectId or datasetId then it will use connection level details.

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

Delete dataset    [Read more...]

Delete dataset for specified ID. If you like to delete all tables under that dataset then set deleteContents='true'

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

Other App Integration scenarios for Google BigQuery

Other Connectors for SQL Server


Download Google BigQuery Connector for SQL Server Documentation 

  • How to connect Google BigQuery in SQL Server?

  • How to get Google BigQuery data in SQL Server?

  • How to read Google BigQuery data in SQL Server?

  • How to load Google BigQuery data in SQL Server?

  • How to import Google BigQuery data in SQL Server?

  • How to pull Google BigQuery data in SQL Server?

  • How to push data to Google BigQuery in SQL Server?

  • How to write data to Google BigQuery in SQL Server?

  • How to POST data to Google BigQuery in SQL Server?

  • Call Google BigQuery API in SQL Server

  • Consume Google BigQuery API in SQL Server

  • Google BigQuery SQL Server Automate

  • Google BigQuery SQL Server Integration

  • Integration Google BigQuery in SQL Server

  • Consume real-time Google BigQuery data in SQL Server

  • Consume realtime Google BigQuery API data in SQL Server

  • Google BigQuery ODBC Driver | ODBC Driver for Google BigQuery | ODBC Google BigQuery Driver | SSIS Google BigQuery Source | SSIS Google BigQuery Destination

  • Connect Google BigQuery in SQL Server

  • Load Google BigQuery in SQL Server

  • Load Google BigQuery data in SQL Server

  • Read Google BigQuery data in SQL Server

  • Google BigQuery API Call in SQL Server