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You can use Hibernate to map object-oriented domain models to a traditional relational database. The tutorial below shows how to use the CData JDBC Driver for BigQuery to generate an ORM of your BigQuery repository with Hibernate.
Though Eclipse is the IDE of choice for this article, the CData JDBC Driver for BigQuery works in any product that supports the Java Runtime Environment. In the Knowledge Base you will find tutorials to connect to BigQuery data from IntelliJ IDEA and NetBeans.
CData simplifies access and integration of live Google BigQuery data. Our customers leverage CData connectivity to:
Most CData customers are using Google BigQuery as their data warehouse and so use CData solutions to migrate business data from separate sources into BigQuery for comprehensive analytics. Other customers use our connectivity to analyze and report on their Google BigQuery data, with many customers using both solutions.
For more details on how CData enhances your Google BigQuery experience, check out our blog post: https://www.cdata.com/blog/what-is-bigquery
Follow the steps below to install the Hibernate plug-in in Eclipse.
Follow the steps below to add the driver JARs in a new project.
Follow the steps below to configure connection properties to BigQuery data.
Input the following values:
Connection URL: A JDBC URL, starting with jdbc:googlebigquery: and followed by a semicolon-separated list of connection properties.
Google uses the OAuth authentication standard. To access Google APIs on behalf of individual users, you can use the embedded credentials or you can register your own OAuth app.
OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, register an application to obtain the OAuth JWT values.
In addition to the OAuth values, specify the DatasetId and ProjectId. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.
For assistance in constructing the JDBC URL, use the connection string designer built into the BigQuery JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.
java -jar cdata.jdbc.googlebigquery.jar
Fill in the connection properties and copy the connection string to the clipboard.
๐ Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)A typical JDBC URL is below:
jdbc:googlebigquery:DataSetId=MyDataSetId;ProjectId=MyProjectId;InitiateOAuth=GETANDREFRESH;
Follow the steps below to select the configuration you created in the previous step.
Follow the steps below to generate the reveng.xml configuration file. You will specify the tables you want to access as objects.
Follow the steps below to generate plain old Java objects (POJO) for the BigQuery tables.
One or more POJOs are created based on the reverse-engineering setting in the previous step.
For each mapping you have generated, you will need to create a mapping tag in hibernate.cfg.xml to point Hibernate to your mapping resource. Open hibernate.cfg.xml and insert the mapping tags as so:
cdata.googlebigquery.GoogleBigQueryDriver jdbc:googlebigquery:DataSetId=MyDataSetId;ProjectId=MyProjectId;InitiateOAuth=GETANDREFRESH; org.hibernate.dialect.SQLServerDialect
Using the entity you created from the last step, you can now search and modify BigQuery data:
import java.util.*;
import org.hibernate.Session;
import org.hibernate.cfg.Configuration;
import org.hibernate.query.Query;
public class App {
public static void main(final String[] args) {
Session session = new
Configuration().configure().buildSessionFactory().openSession();
String SELECT = "FROM Orders O WHERE ShipCity = :ShipCity";
Query q = session.createQuery(SELECT, Orders.class);
q.setParameter("ShipCity","New York");
List<Orders> resultList = (List<Orders>) q.list();
for(Orders s: resultList){
System.out.println(s.getOrderName());
System.out.println(s.getFreight());
}
}
}
Download a free trial of the Google BigQuery Driver to get started:
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๐ Google BigQuery IconRapidly create and deploy powerful Java applications that integrate with Google BigQuery data including Tables and Datasets.