In PySpark, spark.table() is used to read a table from the Spark catalog, whereas spark.read.table() 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, spark.read.table() 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 spark.read.table() to read a table from a database:
df = spark.read.format("jdbc") \ .option("url", "jdbc:postgresql://localhost/mydatabase") \ .option("dbtable", "mytable") \ .option("user", "username") \ .option("password", "password") \ .load()
Spark import urls to refer