Process & Analyze Amazon Athena 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 Amazon Athena data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Amazon Athena data in Databricks.

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

Install the CData JDBC Driver in Databricks

To work with live Amazon Athena 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.amazonathena.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Access Amazon Athena Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Amazon Athena 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 Amazon Athena, and create a basic report.

Configure the Connection to Amazon Athena

Connect to Amazon Athena 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.amazonathena.AmazonAthenaDriver"
url = "jdbc:amazonathena:RTK=5246...;AccessKey='a123';SecretKey='s123';Region='IRELAND';Database='sampledb';S3StagingDirectory='s3://bucket/staging/';"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Amazon Athena 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.

Authenticating to Amazon Athena

To authorize Amazon Athena requests, provide the credentials for an administrator account or for an IAM user with custom permissions: Set AccessKey to the access key Id. Set SecretKey to the secret access key.

Note: Though you can connect as the AWS account administrator, it is recommended to use IAM user credentials to access AWS services.

Obtaining the Access Key

To obtain the credentials for an IAM user, follow the steps below:

1. Sign into the IAM console.
2. In the navigation pane, select Users.
3. To create or manage the access keys for a user, select the user and then select the Security Credentials tab.

To obtain the credentials for your AWS root account, follow the steps below:

1. Sign into the AWS Management console with the credentials for your root account.
2. Select your account name or number and select My Security Credentials in the menu that is displayed.
3. Click Continue to Security Credentials and expand the Access Keys section to manage or create root account access keys.

Authenticating from an EC2 Instance

If you are using the CData Data Provider for Amazon Athena 2018 from an EC2 Instance and have an IAM Role assigned to the instance, you can use the IAM Role to authenticate. To do so, set UseEC2Roles to true and leave AccessKey and SecretKey empty. The CData Data Provider for Amazon Athena 2018 will automatically obtain your IAM Role credentials and authenticate with them.

Authenticating as an AWS Role

In many situations it may be preferable to use an IAM role for authentication instead of the direct security credentials of an AWS root user. An AWS role may be used instead by specifying the RoleARN. This will cause the CData Data Provider for Amazon Athena 2018 to attempt to retrieve credentials for the specified role. If you are connecting to AWS (instead of already being connected such as on an EC2 instance), you must additionally specify the AccessKey and SecretKey of an IAM user to assume the role for. Roles may not be used when specifying the AccessKey and SecretKey of an AWS root user.

Authenticating with MFA

For users and roles that require Multi-factor Authentication, specify the MFASerialNumber and MFAToken connection properties. This will cause the CData Data Provider for Amazon Athena 2018 to submit the MFA credentials in a request to retrieve temporary authentication credentials. Note that the duration of the temporary credentials may be controlled via the TemporaryTokenDuration (default 3600 seconds).

Connecting to Amazon Athena

In addition to the AccessKey and SecretKey properties, specify Database, S3StagingDirectory and Region. Set Region to the region where your Amazon Athena data is hosted. Set S3StagingDirectory to a folder in S3 where you would like to store the results of queries.

If Database is not set in the connection, the data provider connects to the default database set in Amazon Athena.

Load Amazon Athena Data

Once you configure the connection, you can load Amazon Athena 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 Amazon Athena Data

Check the loaded Amazon Athena data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Name"))

Analyze Amazon Athena 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 Amazon Athena data for reporting, visualization, and analysis.

% sql

SELECT Name, TotalDue FROM SAMPLE_VIEW ORDER BY TotalDue DESC LIMIT 5

The data from Amazon Athena 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 Amazon Athena and start working with your live Amazon Athena data in Databricks. Reach out to our Support Team if you have any questions.