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.

   The ExcelFile, under the Authentication section, must be set to a valid Excel File.

   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.

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

   scala> val excel_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:excel:Excel File='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:

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 200+ CData JDBC Drivers and get started today.