Data Engineering Spark SQL - Spark SQL Functions - Data Type Conversion

Let us understand how we can type cast to change the data type of extracted value to its original type. Let us start spark context for this Notebook so that we can execute the code provided. You can sign up for our 10 node state of the art cluster/labs to learn Spark SQL using our unique integrated LMS.

Key Concepts Explanation

Using Spark SQL

spark2-sql \
    --master yarn \
    --conf spark.ui.port=0 \
    --conf spark.sql.warehouse.dir=/user/${USER}/warehouse

Using Scala

spark2-shell \
    --master yarn \
    --conf spark.ui.port=0 \
    --conf spark.sql.warehouse.dir=/user/${USER}/warehouse

Using Pyspark

pyspark2 \
    --master yarn \
    --conf spark.ui.port=0 \
    --conf spark.sql.warehouse.dir=/user/${USER}/warehouse

Explanation of Key Concepts:

  • Using the provided code snippets, we can execute Spark SQL, Scala, and Pyspark commands to interact with Spark.
  • The provided commands set up the Spark session and configure parameters for the interactive sessions.

Hands-On Tasks

  1. Create external table orders_single_column in Spark SQL.
  2. Extract and cast columns from the table to their respective data types for further analysis.

Conclusion

In this article, we covered the basics of data type conversion in Spark using Spark SQL. By understanding how to cast data types, we can ensure accurate data processing and analysis. Practice these concepts and engage with the community for further learning.

Watch the video tutorial here