Schema inconsistency between Glue Data Catalog and Glue ETL Job

Question

I have setup an AWS Glue Crawler to read the AWS CUR data residing in S3. Yesterday, I have enabled new Cost Allocation tags in CUR and  today I can see them when I query the table in Athena. But **I cant access the new columns in AWS Glue ETL job.** I am reading the table in AWS Glue ETL as below. 
```
dyf = glueContext.create_dynamic_frame.from_catalog(database=source_db,
                                                       table_name=source_tbl)
usage_df = dyf.toDF()
usage_df = usage_df.filter(filter_clause)
usage_df.printSchema() ## Schema is not showing the new fields
```
Tried executing `MSCK REPAIR TABLE`, still no luck. The Crawler property set as `Update the table definition in the data catalog` and its a partitioned table with `year` and `month` as partition column. Am I missing anything ?

Accepted Answer

DynamicFrame doesn't use the catalog, it will infer the schema from the actual data files.  
DataFrame does and since you are converting to it, you can just do: 
```
usage_df = spark.table("source_db", "source_tbl")
```

Schema inconsistency between Glue Data Catalog and Glue ETL Job

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