Google BigQuery Connector for SQL Server
In this article you will learn how to integrate 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. 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. |
Connect to Google BigQuery in other apps
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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 API Driver
-
Download and install ZappySys ODBC PowerPack.
-
Search for gateway in start menu and Open ZappySys Data Gateway:
-
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:
-
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
-
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:
GoogleBigqueryDSNGoogle BigQuery -
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 accounts represent a developer, administrator, or any other person who interacts with Google APIs and services. User accounts are managed as Google Accounts, either with Google Workspace or Cloud Identity. They can also be user accounts that are managed by a third-party identity provider and federated with Workforce Identity Federation. [API reference]
Steps how to get and use Google BigQuery credentials
Follow these steps on how to create Client Credentials (User Account principle) to authenticate and access BigQuery API in SSIS package or ODBC data source:
WARNING: If you are planning to automate processes, we recommend that you use a Service Account authentication method. In case, you still need to use User Account, then make sure you use a system/generic account (e.g.automation@my-company.com
). When you use a personal account which is tied to a specific employee profile and that employee leaves the company, the token may become invalid and any automated processes using that token will start to fail.Step-1: Create project
This step is optional, if you already have a project in Google Cloud and can use it. However, if you don't, proceed with these simple steps to create one:
-
First of all, go to Google API Console.
-
Then click Select a project button and then click NEW PROJECT button:
-
Name your project and click CREATE button:
-
Wait until the project is created:
- Done! Let's proceed to the next step.
Step-2: Enable Google Cloud APIs
In this step we will enable BigQuery API and Cloud Resource Manager API:
-
Select your project on the top bar:
-
Then click the "hamburger" icon on the top left and access APIs & Services:
-
Now let's enable several APIs by clicking ENABLE APIS AND SERVICES button:
-
In the search bar search for
bigquery api
and then locate and select BigQuery API: -
If BigQuery API is not enabled, enable it:
-
Then repeat the step and enable Cloud Resource Manager API as well:
- Done! Let's proceed to the next step.
Step-3: Create OAuth application
-
First of all, click the "hamburger" icon on the top left and then hit VIEW ALL PRODUCTS:
-
Then access Google Auth Platform to start creating an OAuth application:
-
Start by pressing GET STARTED button:
-
Next, continue by filling in App name and User support email fields:
-
Choose Internal option, if it's enabled, otherwise select External:
-
Optional step if you used
Internal
option in the previous step. Nevertheless, if you had to useExternal
option, then click ADD USERS to add a user: -
Then add your contact Email address:
-
Finally, check the checkbox and click CREATE button:
- Done! Let's create Client Credentials in the next step.
Step-4: Create Client Credentials
-
In Google Auth Platform, select Clients menu item and click CREATE CLIENT button:
-
Choose
Desktop app
as Application type and name your credentials: -
Continue by opening the created credentials:
-
Finally, copy Client ID and Client secret for the later step:
-
Done! We have all the data needed for authentication, let's proceed to the last step!
Step-5: Configure connection
-
Now go to SSIS package or ODBC data source and use previously copied values in User Account authentication configuration:
- In the ClientId field paste the Client ID value.
- In the ClientSecret field paste the Client secret value.
-
Press Generate Token button to generate Access and Refresh Tokens.
-
Then choose ProjectId from the drop down menu.
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Continue by choosing DatasetId from the drop down menu.
-
Finally, click Test Connection to confirm the connection is working.
-
Done! Now you are ready to use Google BigQuery Connector!
Fill in all required parameters and set optional parameters if needed:
GoogleBigqueryDSNGoogle BigQueryUser Account [OAuth]https://www.googleapis.com/bigquery/v2Required 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 ClientSecret Scope https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigquery.insertdata https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/devstorage.full_control https://www.googleapis.com/auth/devstorage.read_only https://www.googleapis.com/auth/devstorage.read_write RetryMode RetryWhenStatusCodeMatch RetryStatusCodeList 429|503 RetryCountMax 5 RetryMultiplyWaitTime True Job Location Redirect URL (Only for Web App) Service accounts are accounts that do not represent a human user. They provide a way to manage authentication and authorization when a human is not directly involved, such as when an application needs to access Google Cloud resources. Service accounts are managed by IAM. [API reference]
Steps how to get and use Google BigQuery credentials
Follow these steps on how to create Service Account to authenticate and access BigQuery API in SSIS package or ODBC data source:
Step-1: Create project
This step is optional, if you already have a project in Google Cloud and can use it. However, if you don't, proceed with these simple steps to create one:
-
First of all, go to Google API Console.
-
Then click Select a project button and then click NEW PROJECT button:
-
Name your project and click CREATE button:
-
Wait until the project is created:
- Done! Let's proceed to the next step.
Step-2: Enable Google Cloud APIs
In this step we will enable BigQuery API and Cloud Resource Manager API:
-
Select your project on the top bar:
-
Then click the "hamburger" icon on the top left and access APIs & Services:
-
Now let's enable several APIs by clicking ENABLE APIS AND SERVICES button:
-
In the search bar search for
bigquery api
and then locate and select BigQuery API: -
If BigQuery API is not enabled, enable it:
-
Then repeat the step and enable Cloud Resource Manager API as well:
- Done! Let's proceed to the next step and create a service account.
Step-3: Create Service Account
Use the steps below to create a Service Account in Google Cloud:
-
First of all, go to IAM & Admin in Google Cloud console:
-
Once you do that, click Service Accounts on the left side and click CREATE SERVICE ACCOUNT button:
-
Then name your service account and click CREATE AND CONTINUE button:
-
Continue by clicking Select a role dropdown and start granting service account BigQuery Admin and Project Viewer roles:
-
Find BigQuery group on the left and then click on BigQuery Admin role on the right:
-
Then click ADD ANOTHER ROLE button, find Project group and select Viewer role:
-
Finish adding roles by clicking CONTINUE button:
You can always add or modify permissions later in IAM & Admin. -
Finally, in the last step, just click button DONE:
-
Done! We are ready to add a Key to this service account in the next step.
Step-4: Add Key to Service Account
We are ready to add a Key (P12 certificate) to the created Service Account:
-
In Service Accounts open newly created service account:
-
Next, copy email address of your service account for the later step:
-
Continue by selecting KEYS tab, then press ADD KEY dropdown, and click Create new key menu item:
-
Finally, select P12 option and hit CREATE button:
- P12 certificate downloads into your machine. We have all the data needed for authentication, let's proceed to the last step!
Step-5: Configure connection
-
Now go to SSIS package or ODBC data source and configure these fields in Service Account authentication configuration:
- In the Service Account Email field paste the service account Email address value you copied in the previous step.
- In the Service Account Private Key Path (i.e. *.p12) field use downloaded certificate's file path.
- Done! Now you are ready to use Google BigQuery Connector!
Fill in all required parameters and set optional parameters if needed:
GoogleBigqueryDSNGoogle BigQueryService Account [OAuth]https://www.googleapis.com/bigquery/v2Required 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 (Choose after ProjectId) Fill-in the parameter... Optional Parameters Scope https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigquery.insertdata https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/devstorage.full_control https://www.googleapis.com/auth/devstorage.read_only https://www.googleapis.com/auth/devstorage.read_write RetryMode RetryWhenStatusCodeMatch RetryStatusCodeList 429 RetryCountMax 5 RetryMultiplyWaitTime True Job Location Impersonate As (Enter Email Id) -
-
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:
-
Click OK to finish creating the data source.
Read data in SQL Server from the ZappySys Data Gateway
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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:
-
Then click on Security option and configure username we created in ZappySys Data Gateway in one of the previous steps:
-
Optional: Under the Server Options, Enable RPC and RPC Out and Disable Promotion of Distributed Transactions(MSDTC).
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 theServer '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 theThe 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]
-
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');
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''
---- For MSSQL 2012,2014,2016 and 2019 use below (SQL Server Native Client 11.0)---
, @provider=N'SQLNCLI11'
---- For MSSQL 2022 or higher use below (Microsoft OLE DB Driver for SQL Server)---
--, @provider=N'MSOLEDBSQL'
, @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.
- Search for gateway in start menu and open ZappySys Data Gateway.
- 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).
- Search for Windows Firewall Advanced Security in start menu.
- Under Inbound Rules > Right click and click [New Rule] >> Click Next
- Select Port on Rule Type >> Click Next
- Click on TCP and enter port number under specified local port as 5000 (use different one if you changed Default port) >> Click Next
- Select Profile (i.e. Private, Public) >> Click Next
- Enter Rule name [i.e. ZappySys Data Gateway – Allow Inbound ] >> Click Next
- Click OK to save the rule

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''
---- For MSSQL 2012,2014,2016 and 2019 use below (SQL Server Native Client 11.0)---
, @provider=N'SQLNCLI11'
---- For MSSQL 2022 or higher use below (Microsoft OLE DB Driver for SQL Server)---
--, @provider=N'MSOLEDBSQL'
, @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:

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 syntaxexec [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.
SQL Native Client 11.0 not visible in the Providers dropdown (Linked Server Creation)
If you are following some screenshots / steps from our article it might say use SQL Native Client to create Linked Server to ZappySys Gateway but for some users they dont see that driver entry in the dropdown. This is due to the fact that Microsoft has deprecated SQL Native Client OLEDB Driver (SQLNCLI and SQLNCLI11) going forward after SQL 2022. So you need to use [Microsoft OLE DB Driver for SQL Server] instead (MSOLEDBSQL). Please follow all other instructions except the driver type selection, use new suggested driver instead if you dont see SQL Native Client.
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
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.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
-
Go to Custom Objects Tab and Click on Add button and Select Add Procedure:
-
Enter the desired Procedure name and click on OK:
-
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>';
-
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';
-
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''')
-
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.
-
Go to Custom Objects Tab and Click on Add button and Select Add Table:
-
Enter the desired Table name and click on OK:
-
And it will open the New Query Window Click on Cancel to close that window and go to Custom Objects Tab.
-
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'
-
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"
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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''')
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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 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.Parameter | Description | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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timeout (Milliseconds) |
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all |
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Delete All Tables |
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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): Delete Multiple Records (Call DML) [Read more...]
This Server side SQL query example of BigQuery shows how to invoke DELETE statement. To do that 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 DELETE FROM TestDataset.MyTable Where Id > 5
Native Query (ServerSide): Query with CAST unix TIMESTAMP datatype column as datetime [Read more...]
This example shows how to query timestamp column as DateTime. E.g. 73833719.524272 should be displayed as 1972-05-04 or with milliseconds 1972-05-04 1:21:59.524 PM then use CAST function (you must use #DirectSQL prefix)
#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
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): UPDATE Table data for complex types (e.g. Nested RECORD, Geography, JSON) [Read more...]
Example of how to run Valid BigQuery DML statement ()e.g. UPDATE / INSERT / DELETE). This usecase shows how to update record with complex data types such as RECORD (i.e Array), Geography, JSON and more. Prefix SQL with word #DirectSQL to invoke server side engine (Pass-through SQL)
#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
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')
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
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