Google BigQuery Connector for Informatica
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 Informatica. 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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How to read API data in Informatica (Call JSON / XML SOAP Service)
How to write data to API (POST) in Informatica (Call JSON / XML SOAP Service)
Introduction
JSON / REST API is becoming more and more popular each day as everyone embrace cloud-centric services. This article is primarily focused on Informatica users who want to access Google BigQuery data or may be other API Integration in Informatica. However many tips and techniques described in this article will help you to understand how to integrate Google BigQuery / XML SOAP / JSON / REST API in other ETL / Reporting apps such as Tableau, Power BI, SSRS, Talend, Excel and many more.
After going through this article you will learn how to Read Google BigQuery / JSON / REST API data in Informatica and understand the concept of JSON / REST API. We will go through many screenshots and step-by-step examples to demonstrate Google BigQuery or REST API integration in Informatica PowerCenter.
XML / JSON can come from a local file or REST API service (internal or public) so we will include both examples in this article (i.e. Read JSON files in Informatica, Import REST API in Informatica). So let’s get started. Next article will focus on how to write data to API in Informatica (POST / PUT data)
Requirements
This article assumes that you have full filled following basic requirements.
- Download Install ZappySys ODBC PowerPack (API Driver for Google BigQuery included)
- Install Informatica PowerCenter Client Tools (e.g. Workflow and Mapping Designers)
- Access to a Relational database such as SQL Server (or use any of your choice e.g. Oracle, MySQL, DB2 ). If nothing available then you can use flat file target.
High level Steps for Import Google BigQuery data using Informatica (Read Google BigQuery API data)
Before we dive deep to learn how to load Google BigQuery data in Informatica (i.e. Google BigQuery to SQL Table), Here the summary of high-level steps you need to perform to import Google BigQuery in Informatica (same steps for Import JSON / XML / REST API).
- Download and Install ZappySys API Driver (for connecting to Google BigQuery)
- Create ODBC DSN using ZappySys API driver and choose Google BigQuery Connector during Wizard
- Create Relational > ODBC Connection in Informatica Workflow designer (Point to DSN we created in the previous step)
- Import Google BigQuery Source Definition in the Informatica Mapping Designer > Sources Tab
- Import Target Table Definition in the Informatica Mapping Designer > Targets Tab
- Create source to target mapping in Mappings tab
- Save mapping (name m_API_to_SQL_Load )
- Create Session using the mapping we created in the previous step
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Save Workflow and execute to load Google BigQuery data into SQL Table. Verify your data and log.
Loading Google BigQuery data to SQL Table in Informatica (Import REST API or JSON Files)
Video Tutorial – Read any API / JSON data in Informatica (Load Google BigQuery to SQL Table)
Below video is not about Google BigQuery API but its showing API access in general (for any API). By watching following ~5 min video can learn steps listed in this article to load JSON API data into SQL Server Table using ZappySys JSON Driver. You can go though full article to learn many useful details not covered in this video.
Getting Started – Import Google BigQuery to SQL Server in Informatica
Now let’s get started. For example purpose, we will read data from Google BigQuery and load data into SQL Server Table using Informatica Workflow.
Create ODBC Data Source (DSN) based on ZappySys API Driver
Step-by-step instructions
To get data from Google BigQuery using Informatica 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 Informatica. Perform these steps:
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Install ZappySys ODBC PowerPack.
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Open ODBC Data Sources (x64):
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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. -
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
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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:
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First of all, go to Google API Console.
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Then click Select a project button and then click NEW PROJECT button:
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Name your project and click CREATE button:
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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:
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Select your project on the top bar:
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Then click the "hamburger" icon on the top left and access APIs & Services:
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Now let's enable several APIs by clicking ENABLE APIS AND SERVICES button:
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In the search bar search for
bigquery apiand then locate and select BigQuery API:
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If BigQuery API is not enabled, enable it:
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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
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First of all, click the "hamburger" icon on the top left and then hit VIEW ALL PRODUCTS:
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Then access Google Auth Platform to start creating an OAuth application:
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Start by pressing GET STARTED button:
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Next, continue by filling in App name and User support email fields:
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Choose Internal option, if it's enabled, otherwise select External:
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Optional step if you used
Internaloption in the previous step. Nevertheless, if you had to useExternaloption, then click ADD USERS to add a user:
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Then add your contact Email address:
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Finally, check the checkbox and click CREATE button:
- Done! Let's create Client Credentials in the next step.
Step-4: Create Client Credentials
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In Google Auth Platform, select Clients menu item and click CREATE CLIENT button:
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Choose
Desktop appas Application type and name your credentials:
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Continue by opening the created credentials:
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Finally, copy Client ID and Client secret for the later step:
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Done! We have all the data needed for authentication, let's proceed to the last step!
Step-5: Configure connection
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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.
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Press Generate Token button to generate Access and Refresh Tokens.
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Then choose ProjectId from the drop down menu.
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Continue by choosing DatasetId from the drop down menu.
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Finally, click Test Connection to confirm the connection is working.
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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:
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First of all, go to Google API Console.
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Then click Select a project button and then click NEW PROJECT button:
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Name your project and click CREATE button:
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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:
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Select your project on the top bar:
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Then click the "hamburger" icon on the top left and access APIs & Services:
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Now let's enable several APIs by clicking ENABLE APIS AND SERVICES button:
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In the search bar search for
bigquery apiand then locate and select BigQuery API:
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If BigQuery API is not enabled, enable it:
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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:
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First of all, go to IAM & Admin in Google Cloud console:
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Once you do that, click Service Accounts on the left side and click CREATE SERVICE ACCOUNT button:
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Then name your service account and click CREATE AND CONTINUE button:
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Continue by clicking Select a role dropdown and start granting service account BigQuery Admin and Project Viewer roles:
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Find BigQuery group on the left and then click on BigQuery Admin role on the right:
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Then click ADD ANOTHER ROLE button, find Project group and select Viewer role:
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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:
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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:
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In Service Accounts open newly created service account:
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Next, copy email address of your service account for the later step:
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Continue by selecting KEYS tab, then press ADD KEY dropdown, and click Create new key menu item:
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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
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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)
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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:
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Click OK to finish creating the data source.
Video instructions
Create Connection in Informatica Workflow Designer
Once you create DSN using API Driver our next step is to define a connection for Google BigQuery source in Informatica PowerCenter Workflow designer.
- Open Workflow designer [W] icon
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Goto Connections > Relational
Create a new connection for Google BigQuery in Informatica
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Click New and select ODBC
Select ODBC connection type in Informatica (Using ZappySys API ODBC DSN)
- Now on the ODBC connection setup enter connection name, some fake userid / password (this is a required field but its ignored by JSON Driver)
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In the Connection String field enter the exact same name of DSN (Open ODBC Data Sources UI to confirm)
Configure Google BigQuery connection in Informatica for REST API – Using ZappySys API Driver
- Click OK to close the connection properties.
That’s it. Now we ready to move to next step (define source and target in Mapping Designer).
Import Google BigQuery Source Definition in Informatica Mapping Designer
Now let’s look at steps to import Google BigQuery table definition.
- Open Informatica Mapping Designer (Click [D] icon)
- Click on Source Icon to switch to Sources designer
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From the top menu > Click on Sources > Import from Database …
Import Google BigQuery Source definition in Informatica Mapping Designer (JSON file or REST API)
- Select ODBC data source from the dropdown (Find out DSN we created earlier to use as JSON Source)
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Click Connect button to get a list of tables. Any array node is listed as a table. Also, you will see array node with parent columns (e.g. value_with_parent). You may get some warning like below but they are harmless so just ignore by clicking OK.
DLL name entry missing from C:\Informatica\PowerCenter8.6.1\client\bin\powrmart.ini Section = ODBCDLL Entry = ZappySys JSON Driver
—————————————————-
Using EXTODBC.DLL to support ZappySys JSON Driver. For native support of ZappySys JSON Driver make an entry in the .ini file.
Select Google BigQuery Source Table in Informatica Mapping Designer (JSON file or REST API)
- Select Table you wish to get (You can filter rows by custom SQL query. We will see later in this article how to do)
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Optionally once table structure is imported you can rename it
Rename imported table definition in Informatica Source Designer
- That’s it, we are now ready to perform similar steps to import Target table structure in the next section.
Import SQL Server Target Definition in Informatica Mapping Designer
Now let’s look at steps to import Target table definition (very similar to the previous section, the only difference is this time we will select DSN which points to SQL Server or any other Target Server).
Now lets look at steps to import target table definition in Informatica mapping designer.
- In the Mapping Designer, Click on Target Icon to switch to Target designer
- From the top menu > Click on Targets > Import from Database …
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Select DSN for your Target server (if DSN doesn’t exist then create one by opening ODBC Sources just like we created one for JSON API source (see the previous section about creating DSN).
Import target Table Definition in informatica
- Enter your userid , password and Schema name and click Connect to see tables
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Select Table name to and click OK import definition.
Import Target SQL Table Definition in Informatica – Select table from the list
Create Source to Target Mapping in Informatica (Import JSON to SQL Server)
Once you have imported source and target table definition, we can create mapping and transformation to load data from JSON to SQL Table.
- First open Mapping Designer (Click [D] icon)
- Drag JSON Source from sources folder
- Drag SQL Table from Targets folder
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Map desired columns from Source to target
Define Source to Target mapping for Google BigQuery to SQL Table load in Informatica
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For certain columns you may have to do datatype conversion. For example to convert OrderDate form nstring to DataTime you have to use Expression Transform like below and map it to target. In below example, our JSON has date format (e.g. 2018-01-31 12:00:00 AM ). To import this to DateTime field in SQL server we need to convert it using TO_DATE function. Use double quotes around T to make this format working.
TO_DATE(OrderDate,'YYYY-MM-DD H12:MI:SS AM') --For ISO use below way TO_DATE(OrderDate,'YYYY-MM-DD"T"HH24:MI:SS')
Informatica Google BigQuery to SQL Table Mapping – Datatype conversion (nstring to datetime)
- Once you done with mapping save your mapping and name it (i.e. m_Api_To_SQL)
- Now lets move to next section to create workflow.
Create Workflow and Session in Informatica
Now the final step is to create a new workflow. Perform following steps to create workflow which with a session task to import JSON data into SQL table.
- Open workflow designer by click [W] icon.
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Launch new workflow creation wizard by click Workflow top menu > Wizard
name your workflow (e.g. wf_Api_Tp_Sql_Table_Import)
Creating Informatica Workflow – Wizard UI (Import Google BigQuery data to SQL Table)
- Finish the wizard and double-click the Session to edit some default properties.
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First change Error settings so we fail session on error (By default its always green)
Fail Informatica Session on error (Google BigQuery data to SQL Load)
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Select JSON connection for Source
Select Google BigQuery Source Connection in Informatica – Load Google BigQuery data to SQL Table
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Change default Source query if needed. You can pass parameters to this query to make it dynamic.
Modify Google BigQuery Source SQL query – Pass parameters, change URL, set filter etc
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Select Target connection of SQL Target Table
Select SQL Target Connection in Informatica – Load Google BigQuery data to SQL Table
- Save workflow
- That’s it. We ready to run our first workflow to load JSON data to SQL.
Execute Workflow and Validate Log in Informatica
Now once you are done with your workflow, execute it to see the log.
Loading Google BigQuery data to SQL Table in Informatica (Import REST API or JSON Files)
POST data to Google BigQuery in Informatica
There will be a time when you like to send Source data to REST API or SOAP Web Service. You can use below Query for example. For detailed explanation on how to POST data in Informatica check this article.
Video Tutorial – How to POST data to REST API in Informatica
Here is detailed step by step video on REST API POST in informatica PowerCenter
Keywords
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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
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Go to Custom Objects Tab and Click on Add button and Select Add Procedure:
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Enter the desired Procedure name and click on OK:
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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>';
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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';
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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 @fromdate=''1996-07-30''')
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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.
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.
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Go to Custom Objects Tab and Click on Add button and Select Add Table:
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Enter the desired Table name and click on OK:
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And it will open the New Query Window Click on Cancel to close that window and go to Custom Objects Tab.
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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'
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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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Google BigQuery Connector Examples for Informatica 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 10Native 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 > 5Native 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 10Native 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=1Native 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.MytableNative 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_projectsList 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 Informatica and integrate data without any coding. Click here to Download Google BigQuery Connector for Informatica 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).
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