In pyspark what is the difference between Spark spark.table() and

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In PySpark, spark.table() is used to read a table from the Spark catalog, whereas is used to read a table from a structured data source, such as a data lake or a database.

The spark.table() method requires that you have previously created a table in the Spark catalog and registered it using the spark.createTable() method or the CREATE TABLE SQL statement. Once a table has been registered in the catalog, you can use the spark.table() method to access it.

On the other hand, reads a table from a structured data source and returns a DataFrame. It requires a configuration specifying the data source and the options to read the table.

Here is an example of using to read a table from a database:

df ="jdbc") \
    .option("url", "jdbc:postgresql://localhost/mydatabase") \
    .option("dbtable", "mytable") \
    .option("user", "username") \
    .option("password", "password") \

Spark import urls to refer

  1. Spark Examples
  2. PySpark Blogs
  3. Bigdata Blogs
  4. Spark Interview Questions
  5. Official Page
Author: user

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