Google BigQuery Connector for MS Access In this article you will learn how to integrate Google BigQuery data in MS Access 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 MS Access. 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 Excel Connect to Google BigQuery in SSAS Connect to Google BigQuery in C# Connect to Google BigQuery in Python 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 MS Access 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 MS Access. 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]

   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:

   1. First of all, go to Google API Console.

   2. Then click Select a project button and then click NEW PROJECT button:

      

   3. Name your project and click CREATE button:

      

   4. Wait until the project is created:

      
   5. 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:

   1. Select your project on the top bar:

      

   2. Then click the "hamburger" icon on the top left and access APIs & Services:

      

   3. Now let's enable several APIs by clicking ENABLE APIS AND SERVICES button:

      

   4. In the search bar search for bigquery api and then locate and select BigQuery API:

      

   5. If BigQuery API is not enabled, enable it:

      

   6. Then repeat the step and enable Cloud Resource Manager API as well:

      
   7. Done! Let's proceed to the next step.

   Step-3: Create OAuth application

   1. First of all, click the "hamburger" icon on the top left and then hit VIEW ALL PRODUCTS:

      

   2. Then access Google Auth Platform to start creating an OAuth application:

      

   3. Start by pressing GET STARTED button:

      

   4. Next, continue by filling in App name and User support email fields:

      

   5. Choose Internal option, if it's enabled, otherwise select External:

      

   6. Optional step if you used Internal option in the previous step. Nevertheless, if you had to use External option, then click ADD USERS to add a user:

      

   7. Then add your contact Email address:

      

   8. Finally, check the checkbox and click CREATE button:

      
   9. Done! Let's create Client Credentials in the next step.

   Step-4: Create Client Credentials

   1. In Google Auth Platform, select Clients menu item and click CREATE CLIENT button:

      

   2. Choose Desktop app as Application type and name your credentials:

      

   3. Continue by opening the created credentials:

      

   4. Finally, copy Client ID and Client secret for the later step:

      

   5. Done! We have all the data needed for authentication, let's proceed to the last step!

   Step-5: Configure connection

   1. 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.

   2. Press Generate Token button to generate Access and Refresh Tokens.

   3. Then choose ProjectId from the drop down menu.

   4. Continue by choosing DatasetId from the drop down menu.

   5. Finally, click Test Connection to confirm the connection is working.

   6. Done! Now you are ready to use Google BigQuery Connector!

   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
   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 Account [OAuth]

   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:

   1. First of all, go to Google API Console.

   2. Then click Select a project button and then click NEW PROJECT button:

      

   3. Name your project and click CREATE button:

      

   4. Wait until the project is created:

      
   5. 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:

   1. Select your project on the top bar:

      

   2. Then click the "hamburger" icon on the top left and access APIs & Services:

      

   3. Now let's enable several APIs by clicking ENABLE APIS AND SERVICES button:

      

   4. In the search bar search for bigquery api and then locate and select BigQuery API:

      

   5. If BigQuery API is not enabled, enable it:

      

   6. Then repeat the step and enable Cloud Resource Manager API as well:

      
   7. 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:

   1. First of all, go to IAM & Admin in Google Cloud console:

      

   2. Once you do that, click Service Accounts on the left side and click CREATE SERVICE ACCOUNT button:

      

   3. Then name your service account and click CREATE AND CONTINUE button:

      

   4. Continue by clicking Select a role dropdown and start granting service account BigQuery Admin and Project Viewer roles:

      

   5. Find BigQuery group on the left and then click on BigQuery Admin role on the right:

      

   6. Then click ADD ANOTHER ROLE button, find Project group and select Viewer role:

      

   7. Finish adding roles by clicking CONTINUE button:

      
      You can always add or modify permissions later in IAM & Admin.

   8. Finally, in the last step, just click button DONE:

      

   9. 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:

   1. In Service Accounts open newly created service account:

      

   2. Next, copy email address of your service account for the later step:

      

   3. Continue by selecting KEYS tab, then press ADD KEY dropdown, and click Create new key menu item:

      

   4. Finally, select P12 option and hit CREATE button:

      
   5. 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

   1. 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.
   2. Done! Now you are ready to use Google BigQuery Connector!

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

   GoogleBigqueryDSN

   Google BigQuery

   Service Account [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 (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)

   

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 Microsoft Access from the ODBC data source

1. First of all, open MS Access and create a new MS Access database.

2. In the next step, start loading ODBC data source we created:

3. Then click next until data source selection window appears. Select the data source we created in one of the previous steps and hit OK:

   GoogleBigqueryDSN

   

4. Continue with tables and views selection. You can extract multiple tables or views:
   

5. Finally, wait while data is being loaded and once done you should see a similar view:

Using Linked Table for Live Data (Slow)

Linked tables in Microsoft Access are crucial for online databases because they enable real-time access to centralized data, support scalability, facilitate collaboration, enhance data security, ease maintenance tasks, and allow integration with external systems. They provide a flexible and efficient way to work with data stored in online databases, promoting cross-platform compatibility and reducing the need for data duplication.

1. Real-Time Data Access:
   Access can interact directly with live data in online databases, ensuring that users always work with the most up-to-date information.
2. Centralized Data Management:
   Online databases serve as a centralized repository, enabling efficient management of data from various locations.
3. Ease of Maintenance:
   Updates or modifications to the online database structure are automatically reflected in Access, streamlining maintenance tasks.
4. Adaptability to Changing Requirements:
   Linked tables provide flexibility, allowing easy adaptation to changing data storage needs or migration to different online database systems.

Let's create the linked table.

1. Launch Microsoft Access and open the database where you want to create the linked table.

2. Go to the "External Data" tab on the Ribbon. >> "New Data Source" >> "From Other Sources" >> "ODBC Database"

3. Select the option "Link to Data Source by creating a linked table:

4. Continue by clicking 'Next' until the Data Source Selection window appears. Navigate to the Machine Data Source tab and select the desired data source established in one of the earlier steps. Click 'OK' to confirm your selection.

   GoogleBigqueryDSN

   

5. Proceed to the selection of Tables and Views. You have the option to extract multiple tables or views:
   

6. When prompted to select Unique Key column DO NOT select any column(s) and just click OK:

7. Finally, Simply double-click the newly created Linked Table to load the data:

Guide to Effectively Addressing Known Issues

Discover effective strategies to address known issues efficiently in this guide. Get solutions and practical tips to streamline troubleshooting and enhance system performance, ensuring a smoother user experience.

Fewer Rows Imported

The reason for this is that MS Access has a default query timeout of 60 seconds, which means it stops fetching data if the query takes longer than that. As a result, only a limited number of rows are fetched within this time frame.

To address this, we can adjust the Query Timeout by following the steps below.

The path may vary depending on the MS Access bitness, such as 32-bit versus 64-bit.

\HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Jet\4.0\Engines\ODBC

\HKEY_LOCAL_MACHINE\SOFTWARE\WOW6432Node\Microsoft\Jet\4.0\Engines\ODBC

\HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Office\ClickToRun\REGISTRY\MACHINE\Software\Microsoft\Office\16.0\Access Connectivity Engine\Engines\ODBC

We can identify this issue by examining the Fiddler Log, as MS Access doesn't display any error regarding partial import, which is quite unusual

Please refer to this link : How to use Fiddler to analyze HTTP web requests

#Deleted word appears for column value in MS Access for Linked Table mode

If you used Linked Table mode to get external data and it shows #deleted word rather than actual value for column after you open then most likely its following issue.

Make sure to re-create Linked Table and DO NOT select any key column when prompted (Just click OK)
How to Fix

Table Selection UI Opening Delays

The Table selection UI takes a significant amount of time to open after clicking the 'New Data Source' -> 'Other Data Sources' -> 'ODBC'

The reason for this issue is that MS Access sends a dummy query, leading to several unnecessary pagination cycles before an error is thrown. To mitigate this, we can prevent wasted cycles by configuring the 'Throw error if no match' setting on the Filter Options Tab.

Enhancing Performance through Metadata Addition (Reduces Query Time)

We can optimize query performance by creating Virtual Tables (i.e. views with custom SQL) on Datasource and incorporating META=static columns. Learn how to capture static metadata in this guide.
Performance Options - Generate Metadata Manually

Execute the query initially, save the metadata by selecting 'Save to Meta' (choose Compact Format), and then click 'Save to Clipboard.' Utilize the resulting list by pasting it into the META attribute as follows: 'META=paste here.'

SELECT * FROM products
    WITH(
        META='id:String(20); title:String(100);  description:String(500);'
    )

Optimize Workflow with Automated Import

Employ Automated Import when Linked Tables are not feasible, and we need to depend on Imported Tables with static data.

While using Linked Tables sometime it encounter errors, and we are left with no alternative but to utilize Imported Tables, Automatic Refresh becomes crucial in such scenarios.

Here's a guide on automating refreshes. We can set up automatic refresh on different events, such as when the database opens, a form is opened, or a button is clicked.

To initiate the import process, follow these steps:

• Perform the data import using the standard manual steps.
• In the final step, we'll encounter a checkbox labeled 'Save Import Steps.' Ensure to check this option.
• After saving the steps, we can locate their name in the Save Imports UI. Identify the name associated with the saved steps.
• "Now, we can execute the code as shown below:"

Private Sub cmdYes_Click()
    Label0.Visible = True
    DoCmd.RunSavedImportExport "Import-DATA.products"
    Label0.Visible = False
End Sub

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 MS Access 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 
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 MS Access and integrate data without any coding. Click here to Download Google BigQuery Connector for MS Access 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 MS Access Documentation 

More integrations

Other application integration scenarios for Google BigQuery

SSIS

SQL Server

Power BI

SSRS

Informatica

MS Excel

SSAS

C#

Python

JAVA

Tableau

SAP Crystal Reports

Azure Data Factory (Pipeline)

Azure Data Factory (SSIS)

Talend Studio

UiPath

PowerShell

ODBC

Other connectors for MS Access

Amazon Ads

Amazon Selling Partner (SP-API)

Asana

Azure DevOps

Cosmos DB

DropBox

ElasticSearch

FastSpring

Google BigQuery

Google Calendar

Google Drive

Google Sheets

Hubspot

Jira

Mailchimp

ManagedEngine ServiceDesk Plus (Zoho)

OData

OneDrive

Outlook Mail (Office 365)

Power BI

ServiceNow

SharePoint Online

Shopify

Smartsheet

Stripe

YouTube

Zendesk

Zoho CRM

Zoho SalesIQ

Zoom

REST API

JSON

JSON File

XML

SOAP

XML File

JDBC-ODBC Bridge

Salesforce

CSV

Amazon S3 JSON File

Amazon S3 XML File

Amazon S3 CSV File

Azure Blob JSON File

Azure Blob XML File

Azure Blob CSV File

FTP/SFTP JSON File

FTP/SFTP XML File

FTP/SFTP CSV File

Build your own Custom API Connector

Download Google BigQuery Connector for MS Access Documentation

Common Searches: