DataBricks - How to Read CSV into Dataframe by Scala

In this tutorial, you will learn "How to Read CSV into Dataframe by Scala?" in Databricks.

In Databricks, you can use Scala for data processing and analysis using Spark. Here's how you can work with Scala in Databricks:

💎Interactive Scala Notebooks: Databricks provides interactive notebooks where you can write and execute Scala code. You can create a new Scala notebook from the Databricks workspace.

💎 Cluster Setup: Databricks clusters are pre-configured with Apache Spark, which includes Scala API bindings. When you create a cluster, you can specify the version of Spark and Scala you want to use.

💎Import Libraries: You can import libraries and dependencies in your Scala notebooks using the %scala magic command or by specifying dependencies in the cluster configuration.

💎Data Manipulation with Spark: Use Scala to manipulate data using Spark DataFrames and Spark SQL. Spark provides a rich set of APIs for data processing, including transformations and actions.

💎 Visualization: Databricks supports various visualization libraries such as Matplotlib, ggplot, and Vega for visualizing data processed using Scala and Spark.

💎 Integration with other Languages: Databricks notebooks support multiple languages, so you can integrate Scala with Python, R, SQL, etc., in the same notebook for different tasks.

%scala

val FilePath="dbfs:/FileStore/EmployeeData.csv"

//Import libraries

import org.apache.spark.sql.SparkSession

//Create Spark Session

val spark=SparkSession.builder().appName("Read_CSV_File").getOrCreate()

//Read the file into a Dataframe

val df=spark.read.option("header","true").csv(FilePath)

// display dataframe

df.show()

Make sure to replace "path/to/your/csv/file.csv" with the actual path to your CSV file. Additionally, you can adjust options according to your CSV file format, such as specifying delimiter, inferSchema, etc., using the .option() method.

Please watch our demo video at Youtube-

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