Process & Analyze Hive Data in Databricks (AWS)

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live Hive data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Hive data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Hive data. When you issue complex SQL queries to Hive, the driver pushes supported SQL operations, like filters and aggregations, directly to Hive 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 Hive data using native data types.

Install the CData JDBC Driver in Databricks

To work with live Hive data in Databricks, install the driver on your Databricks cluster.

1. Navigate to your Databricks administration screen and select the target cluster.
2. On the Libraries tab, click "Install New."
3. Select "Upload" as the Library Source and "Jar" as the Library Type.
4. Upload the JDBC JAR file (cdata.jdbc.apachehive.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Access Hive Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Hive data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query Hive, and create a basic report.

Configure the Connection to Hive

Connect to Hive by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

Step 1: Connection Information

driver = "cdata.jdbc.apachehive.ApacheHiveDriver"
url = "jdbc:apachehive:RTK=5246...;Server=127.0.0.1;Port=10000;TransportMode=BINARY;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Hive JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

Fill in the connection properties and copy the connection string to the clipboard.

Load Hive Data

Once you configure the connection, you can load Hive data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "Customers") \
	.load ()

Display Hive Data

Check the loaded Hive data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("City"))

Analyze Hive Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the Hive data for reporting, visualization, and analysis.

% sql

SELECT City, CompanyName FROM SAMPLE_VIEW ORDER BY CompanyName DESC LIMIT 5

The data from Hive is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData JDBC Driver for Apache Hive and start working with your live Hive data in Databricks. Reach out to our Support Team if you have any questions.