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Apache Camel is an open source integration framework that allows you to integrate various systems consuming or producing data. When paired with the CData JDBC Driver for Databricks, you can write Java apps that use Camel routes that integrate with live Databricks data. This article explains how to create an app in NetBeans that connects, queries, and routes Databricks data to a JSON file.
With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Databricks data. When you issue complex SQL queries to Databricks, the driver pushes supported SQL operations, like filters and aggregations, directly to Databricks and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze Databricks data using native data types.
Accessing and integrating live data from Databricks has never been easier with CData. Customers rely on CData connectivity to:
While many customers are using CData's solutions to migrate data from different systems into their Databricks data lakehouse, several customers use our live connectivity solutions to federate connectivity between their databases and Databricks. These customers are using SQL Server Linked Servers or Polybase to get live access to Databricks from within their existing RDBMs.
Read more about common Databricks use-cases and how CData's solutions help solve data problems in our blog: What is Databricks Used For? 6 Use Cases.
Follow the steps below to create a new Java project and add the appropriate dependencies:
With the project created, we can start adding the dependencies needed to work with live Databricks data from our App. If you have not already done so, install Maven in your environment, as it is required to add the JAR file for the CData JDBC Driver to your project.
mvn install:install-file -Dfile="C:\Program Files\CData[product_name] 2019\lib\cdata.jdbc.databricks.jar" -DgroupId="org.cdata.connectors" -DartifactId="cdata-databricks-connector" -Dversion="19" -Dpackaging=jar
Once the JDBC Driver is installed, we can add dependencies to our project. To add a dependency, you can either edit the pom.xml file or right-click the dependencies folder and click Add Dependency. The properties for each dependency follow, but you can search through the available libraries by typing the name of the dependency in the Query box in the Add Dependency wizard.
👁 Selecting a dependency| Dependency | Group ID | Artifact ID | Version |
|---|---|---|---|
| camel-core | org.apache.camel | camel-core | 3.0.0 |
| camel-jackson | org.apache.camel | camel-jackson | 3.0.0 |
| camel-jdbc | org.apache.camel | camel-jdbc | 3.0.0 |
| camel-jsonpath | org.apache.camel | camel-jsonpath | 3.0.0 |
| cdata-databricks-connector | org.cdata.connectors | cdata-salesforce-connector | 19 |
| commons-dbcp2 | org.apache.commons | commons-dbcp2 | 2.7.0 |
| slf4j-log4j12 | org.slf4j | slf4j-log4j12 | 1.7.30 |
| log4j | org.apache.logging.log4j | log4j | 2.12.1 |
After adding the required dependencies, we can use the Java DSL (Domain Specific Language) to create routes with access to live Databricks data. Code snippets follow. Download the sample project (zip file) to follow along (make note of the TODO comments).
Start by importing the necessary classes into our main class.
import org.apache.camel.CamelContext; import org.apache.camel.builder.RouteBuilder; import org.apache.camel.impl.DefaultCamelContext; import org.apache.camel.support.SimpleRegistry; import org.apache.commons.dbcp2.BasicDataSource; import org.apache.log4j.BasicConfigurator;
Then in the main method, we configure logging, create a new BasicDataSource and add it to the registry, create a new CamelContext, and finally add a route to the context. In this sample, we route Databricks data to a JSON file.
BasicConfigurator.configure();
Create a BasicDataSource and set the driver class name (cdata.jdbc.salesforce.SalesforceDriver) and URL (using the required connection properties).
To connect to a Databricks cluster, set the properties as described below.
Note: The needed values can be found in your Databricks instance by navigating to Clusters, and selecting the desired cluster, and selecting the JDBC/ODBC tab under Advanced Options.
BasicDataSource basic = new BasicDataSource();
basic.setDriverClassName("cdata.jdbc.databricks.DatabricksDriver");
basic.setUrl("jdbc:databricks:Server=127.0.0.1;Port=443;TransportMode=HTTP;HTTPPath=MyHTTPPath;UseSSL=True;User=MyUser;Password=MyPassword;");
The CData JDBC Driver includes a built-in connection string designer to help you configure the connection URL.
For assistance in constructing the JDBC URL, use the connection string designer built into the Databricks JDBC Driver. Either double-click the JAR file or execute the jar file from the command line.
java -jar cdata.jdbc.databricks.jar
Fill in the connection properties and copy the connection string to the clipboard.
👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)SimpleRegistry reg = new SimpleRegistry();
reg.bind("myDataSource", basic);
CamelContext context = new DefaultCamelContext(reg);
The routing below uses a timer component to run one time and passes a SQL query to the JDBC Driver. The results are marshaled as JSON (and formatted for pretty print) and passed to a file component to write to disk as a JSON file.
context.addRoutes(new RouteBuilder() {
@Override
public void configure() {
from("timer://foo?repeatCount=1")
.setBody(constant("SELECT * FROM Account LIMIT 10"))
.to("jdbc:myDataSource")
.marshal().json(true)
.to("file:C:\\Users\\USER\\Documents?fileName=account.json");
}
});
With the route defined, start the CamelContext to begin the lifecycle. In this example, we wait 10 seconds and then shut down the context.
context.start(); Thread.sleep(10000); context.stop();
Now, you have a working Java application that uses Camel to route data from Databricks to a JSON file. Download a free, 30-day trial of the CData JDBC Driver for Databricks and the sample project (make note of the TODO comments) and start working with your live Databricks data in Apache Camel. Reach out to our Support Team if you have any questions.
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