AWS Glue Job Success But there's no data in DynamoDB Table

Question

Everyday I try to delete and re-create the dynamoDB table, the code successfully deletes the table, but the other job fails to transfer data from Redshift to DynamoDB.

The first AWS Glue job, I try to delete the table, then re-create it:

import boto3
table_name = 'VENDOR_INFO_GLUE'

client = boto3.client('dynamodb')

response = client.delete_table(
    TableName=table_name
)

waiter = client.get_waiter('table_not_exists')
waiter.wait(TableName= table_name)
print ("table deleted")

response = client.create_table(
    AttributeDefinitions=[
        {
            'AttributeName': 'vendor_code',
            'AttributeType': 'S'
        },
        {
            'AttributeName': 'uuid',
            'AttributeType': 'S'
        },
        {
            'AttributeName': 'owning_buyer',
            'AttributeType': 'S'
        },
    ],
    TableName= table_name,
    KeySchema=[
        {
            'AttributeName': 'vendor_code',
            'KeyType': 'HASH'
        },
        {
            'AttributeName': 'uuid',
            'KeyType': 'RANGE'
        },
    ],
      GlobalSecondaryIndexes=[
        {
            'IndexName': 'owning_buyer-index',
            'KeySchema': [
                {
                    'AttributeName': 'owning_buyer',
                    'KeyType': 'HASH'
                },
            ],
            'Projection': {
                'ProjectionType': 'ALL'
            }
        },
    ],
    BillingMode='PAY_PER_REQUEST'

)

waiter = client.get_waiter('table_exists')
waiter.wait(TableName= table_name)
print ("table created")

This job works fine, but then the other job works:

The second job actually doesn't receive errors, but there's no data in Dynamodb Table. I'm not sure if it's related to write.percent, or the time interval between two jobs. I tried to run the jobs where at least there is one hour time interval between two. But I'm still not sure why this is happening.

One another thing I noticed, when I re-create the second job manually again, the second job runs and there's data in dynamodb. But I'm not sure if it's related to my situation.

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
from awsglue import DynamicFrame


def sparkSqlQuery(glueContext, query, mapping, transformation_ctx) -> DynamicFrame:
    for alias, frame in mapping.items():
        frame.toDF().createOrReplaceTempView(alias)
    result = spark.sql(query)
    return DynamicFrame.fromDF(result, glueContext, transformation_ctx)


args = getResolvedOptions(sys.argv, ["JOB_NAME"])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args["JOB_NAME"], args)

# Script generated for node Redshift Cluster
RedshiftCluster_node1 = glueContext.create_dynamic_frame.from_catalog(
    database="bba",
    redshift_tmp_dir=args["TempDir"],
    table_name="ddl_tr_vendors",
    transformation_ctx="RedshiftCluster_node1",
)

# Script generated for node SQL
SqlQuery0 = """
select * from vendors
WHERE snapshot_day = (SELECT MAX(snapshot_day) FROM vendors)

"""
SQL_node1660806425146 = sparkSqlQuery(
    glueContext,
    query=SqlQuery0,
    mapping={"tr_vendors": RedshiftCluster_node1},
    transformation_ctx="SQL_node1660806425146",
)

# Script generated for node ApplyMapping
ApplyMapping_node2 = ApplyMapping.apply(
    frame=SQL_node1660806425146,
    mappings=[
        ("owning_buyer", "string", "owning_buyer", "string"),
        ("vendor_code", "string", "vendor_code", "string"),
        ("is_merchandising_vendor", "string", "is_merchandising_vendor", "string"),
        ("week", "int", "week", "int"),
        ("year", "int", "year", "int"),
        (
            "is_merchandise_ordering_active",
            "string",
            "is_merchandise_ordering_active",
            "string",
        ),
    ],
    transformation_ctx="ApplyMapping_node2",
)

# Script generated for node DynamoDB bucket
Datasink1 = glueContext.write_dynamic_frame_from_options(
    frame=ApplyMapping_node2,
    connection_type="dynamodb",
    connection_options={
        "dynamodb.output.tableName": "VENDOR_INFO_GLUE",
        "dynamodb.throughput.write.percent": "1.0"
    }
)
job.commit()

And this is read usage metric when the second job works: