Data Engineering Spark SQL - Tables - DML & Partitioning - Inserting Data using Stage Table

In this article, we will explore how to insert data into tables through a Stage Table using Parquet file format. We will establish a Spark context in this notebook to execute the provided code examples.

Key Concepts Explanation

Creating Spark Session

To begin, we need to create a Spark session to work with tables in Spark SQL. This session configures the necessary settings for processing data efficiently.

val username = System.getProperty("user.name")
import org.apache.spark.sql.SparkSession

val spark = SparkSession.
  builder.
  config("spark.ui.port", "0").
  config("spark.sql.warehouse.dir", s"/user/${username}/warehouse").
  enableHiveSupport.
  appName(s"${username} | Spark SQL - Managing Tables - DML and Partitioning").
  master("yarn").
  getOrCreate

Inserting Data from a Local File

We will load data from a local text file into a Stage Table and then insert it into the target table using SQL commands.

Hands-On Tasks

  1. Create a Stage Table with text file format and comma as the delimiter.
  2. Load data from the source files into the Stage Table.
  3. Insert data from the Stage Table into the target table.

Conclusion

In summary, this article demonstrates how to efficiently insert data into tables using a Stage Table. By following the step-by-step instructions and leveraging the provided code examples, readers can apply these concepts in their own projects to manage data effectively. Practice these tasks to enhance your skills in working with tables in Spark SQL and data manipulation.

[Click here to watch the video tutorial](Video Link)

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Watch the video tutorial here