@Amrendra Kumar​ :

The error message you provided suggests that there may be an issue with reading a file from the AWS S3 bucket. It could be due to various reasons such as network connectivity issues or access permission errors.

Here are a few things you could try to troubleshoot the issue:

1. Check the AWS S3 bucket access permissions: Ensure that the IAM user or role you are using to access the S3 bucket has the necessary permissions to read the files. You can check this by reviewing the permissions policy attached to the IAM user or role.
2. Check the network connectivity: Check if there is any network connectivity issue between the Databricks cluster and the S3 bucket. You can check this by testing the connectivity using the AWS CLI or by trying to access the bucket from another network.
3. Try accessing the file directly: Try accessing the file directly using the S3 URI instead of mounting the bucket. You can use the AWS S3 connector provided by Apache Spark to read files from S3.

Here's an example code snippet that shows how to read a CSV file directly from an S3 bucket using Spark:

s3_uri = "s3://<bucket-name>/<path-to-file>"
df = spark.read.format("csv").option("inferSchema", "true").option("header", "true").option("sep", ",").load(s3_uri)

4) Increase the executor memory: If the above steps do not help, you can try increasing the executor memory by setting the spark.executor.memory configuration to a higher value. This will give more memory to the Spark executor and may help in processing large files.

I hope this helps!