### Is there an existing issue for this?

- [X] I have searched the existing issues

### Feature description

Basically we want to only run the lambda when an insert is occurring in DynamoDB.
I tested using Dynobase deleting a record and the lambda still triggered.

```
resource "aws_lambda_event_source_mapping" "event-log" {
	event_source_arn  = aws_dynamodb_table.event-log.stream_arn
	function_name     = module.main.lambda-arn
	starting_position = "LATEST"
	enabled = true # change to false when truncating the table in localstack
	maximum_retry_attempts = 1 # limit retry on failure to 1 for now

	# https://dev.to/aws-builders/filtering-dynamodb-streams-before-lambda-p5p
	filter_criteria { # Doesn't seem to work on LocalStack for now
		filter {
			pattern = jsonencode({
				body = {
					eventName : ["INSERT"]
				}
			})
		}
	}
}
```


### 🧑‍💻 Implementation

_No response_

### Anything else?

 https://dev.to/aws-builders/filtering-dynamodb-streams-before-lambda-p5p
code_signing_configs: Dict[str, CodeSigningConfig]
    
    event_source_mappings: List[Dict]
    
    url_configs = Dict[str, Dict]
    def __init__(self):
        self.lambdas = {}
        self.code_signing_configs = {}
        self.event_source_mappings = []
        self.url_configs = {}
def cleanup():
    region = LambdaRegion.get()
    region.lambdas = {}
    region.event_source_mappings = []
    LAMBDA_EXECUTOR.cleanup()
def func_arn(function_name, remove_qualifier=True):
    parts = function_name.split(":function:")
    if remove_qualifier and len(parts) > 1:
        function_name = "%s:function:%s" % (parts[0], parts[1].split(":")[0])
    return aws_stack.lambda_function_arn(function_name)
def func_qualifier(function_name, qualifier=None):
    region = LambdaRegion.get()
    arn = aws_stack.lambda_function_arn(function_name)
    details = region.lambdas.get(arn)
    if not details:
        return details
    if details.qualifier_exists(qualifier):
        return "{}:{}".format(arn, qualifier)
    return arn
def check_batch_size_range(source_arn, batch_size=None):