A utility company wants to visualize data for energy usage on a daily basis in Amazon QuickSight. A data analytics specialist at the company has built a data pipeline to collect and ingest the data into Amazon S3. Each day, the data is stored in an individual .csv file in an S3 bucket. This is an example of the naming structure:
20210707_data.csv
20210708_data.csv
…
To allow for data querying in QuickSight through Amazon Athena, the specialist used an AWS Glue crawler to create a table with the path "s3://powertransformer/20210707_data.csv." However, when the data is queried, it returns zero rows.
How can this issue be resolved?
A. Modify the IAM policy for the AWS Glue crawler to access Amazon S3.
B. Ingest the files again.
C. Store the files in Apache Parquet format.
D. Update the table path to "s3://powertransformer/".
20210707_data.csv
20210708_data.csv
…
To allow for data querying in QuickSight through Amazon Athena, the specialist used an AWS Glue crawler to create a table with the path "s3://powertransformer/20210707_data.csv." However, when the data is queried, it returns zero rows.
How can this issue be resolved?
A. Modify the IAM policy for the AWS Glue crawler to access Amazon S3.
B. Ingest the files again.
C. Store the files in Apache Parquet format.
D. Update the table path to "s3://powertransformer/".