How can I create a SparkSession in PySpark to start programming with the Dataset and DataFrame API on Databricks?
To create a SparkSession in PySpark, you can use the following code:

```python
from pyspark.sql import SparkSession

spark = SparkSession.builder \
   .appName('Example App') \
   .getOrCreate()
```

This code imports the `SparkSession` class from the `pyspark.sql` module and then creates a `SparkSession` object using the `builder` pattern. The `appName` method sets the name of the application, and the `getOrCreate` method either gets the existing `SparkSession` or creates a new one.

Once you have created a `SparkSession`, you can use it to read data into a DataFrame, transform the data, and write the results to a file or database. For example, the following code reads a CSV file into a DataFrame and prints the schema:

```python
data = spark.read.csv('path/to/file.csv', header=True, inferSchema=True)
data.printSchema()
```

This code reads the CSV file at `path/to/file.csv` into a DataFrame, inferring the schema from the data. The `printSchema` method prints the schema of the DataFrame to the console.

For more information about using PySpark on Databricks, see the [PySpark on Databricks](https://docs.databricks.com/spark/latest/spark-sql/pyspark.html) documentation.