A company analyzes historical data and needs to query data that is stored in Amazon S3. New data is generated daily as .csv files that are stored in Amazon S3. The company's data analysts are using Amazon Athena to perform SQL queries against a recent subset of the overall data.
The amount of data that is ingested into Amazon S3 has increased to 5 PB over time. The query latency also has increased. The company needs to segment the data to reduce the amount of data that is scanned.
Which solutions will improve query performance? (Select TWO.)
A human resources company maintains a 10-node Amazon Redshift cluster to run analytics queries on the company's dat
a. The Amazon Redshift cluster contains a product table and a transactions table, and both tables have a product_sku column. The tables are over 100 GB in size. The majority of queries run on both tables.
Which distribution style should the company use for the two tables to achieve optimal query performance?
An ecommerce company stores customer purchase data in Amazon RDS. The company wants a solution to store and analyze historical dat
a. The most recent 6 months of data will be queried frequently for analytics workloads. This data is several terabytes large. Once a month, historical data for the last 5 years must be accessible and will be joined with the more recent data. The company wants to optimize performance and cost.
Which storage solution will meet these requirements?
A company is using an AWS Lambda function to run Amazon Athena queries against a cross-account AWS Glue Data Catalog. A query returns the following error:
HIVE METASTORE ERROR
The error message states that the response payload size exceeds the maximum allowed payload size. The queried table is already partitioned, and the data is stored in an
Amazon S3 bucket in the Apache Hive partition format.
Which solution will resolve this error?
A large company has a central data lake to run analytics across different departments. Each department uses a separate AWS account and stores its data in an Amazon S3 bucket in that account. Each AWS account uses the AWS Glue Data Catalog as its data catalog. There are different data lake access requirements based on roles. Associate analysts should only have read access to their departmental dat
a. Senior data analysts can have access in multiple departments including theirs, but for a subset of columns only.
Which solution achieves these required access patterns to minimize costs and administrative tasks?