How to work with Excel Data in Apache Spark using SQL

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for Excel, Spark can work with live Excel data. This article describes how to connect to and query Excel data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Excel data due to optimized data processing built into the driver. When you issue complex SQL queries to Excel, the driver pushes supported SQL operations, like filters and aggregations, directly to Excel and utilizes the embedded SQL engine to process unsupported operations (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze Excel data using native data types.

Install the CData JDBC Driver for Excel

Download the CData JDBC Driver for Excel installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Excel Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Excel JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for Excel/lib/cdata.jdbc.excel.jar
   

2. With the shell running, you can connect to Excel with a JDBC URL and use the SQL Context load() function to read a table.

   Connecting to Local or Cloud-Stored (Box, Google Drive, Amazon S3, SharePoint) Excel Files

   CData Drivers let you work with Excel files stored locally and stored in cloud storage services like Box, Amazon S3, Google Drive, or SharePoint, right where they are.

   Setting connection properties for local files

   Set the URI property to local folder path.

   Setting connection properties for files stored in Amazon S3

   To connect to Excel file(s) within Amazon S3, set the URI property to the URI of the Bucket and Folder where the intended Excel files exist. In addition, at least set these properties:

   • AWSAccessKey: AWS Access Key (username)
   • AWSSecretKey: AWS Secret Key

   Setting connection properties for files stored in Box

   To connect to Excel file(s) within Box, set the URI property to the URI of the folder that includes the intended Excel file(s). Use the OAuth authentication method to connect to Box.

   Dropbox

   To connect to Excel file(s) within Dropbox, set the URI proprerty to the URI of the folder that includes the intended Excel file(s). Use the OAuth authentication method to connect to Dropbox. Either User Account or Service Account can be used to authenticate.

   SharePoint Online (SOAP)

   To connect to Excel file(s) within SharePoint with SOAP Schema, set the URI proprerty to the URI of the document library that includes the intended Excel file. Set User, Password, and StorageBaseURL.

   SharePoint Online REST

   To connect to Excel file(s) within SharePoint with REST Schema, set the URI proprerty to the URI of the document library that includes the intended Excel file. StorageBaseURL is optional. If not set, the driver will use the root drive. OAuth is used to authenticate.

   Google Drive

   To connect to Excel file(s) within Google Drive, set the URI property to the URI of the folder that includes the intended Excel file(s). Use the OAuth authentication method to connect and set InitiateOAuth to GETANDREFRESH.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.excel.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.)
   

   Configure the connection to Excel, using the connection string generated above.

   scala> val excel_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:excel:URI='C:/MyExcelWorkbooks/SampleWorkbook.xlsx';").option("dbtable","Sheet").option("driver","cdata.jdbc.excel.ExcelDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Excel data as a temporary table:

   scala> excel_df.registerTable("sheet")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> excel_df.sqlContext.sql("SELECT Name, Revenue FROM Sheet WHERE Name = Bob").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

   👁 Data in Apache Spark (Salesforce is shown)
   

Using the CData JDBC Driver for Excel in Apache Spark, you are able to perform fast and complex analytics on Excel data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the hundreds of CData JDBC Drivers and get started today.