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A company uses an Amazon Redshift provisioned cluster for data analysis. The data is not encrypted at rest. A data analytics specialist must implement a solution to encrypt the data at rest.
Which solution will meet this requirement with the LEAST operational overhead?
A data analyst notices the following error message while loading data to an Amazon Redshift cluster:
"The bucket you are attempting to access must be addressed using the specified endpoint."
What should the data analyst do to resolve this issue?
The correct answer is
A Specify the correct AWS Region for the Amazon S3 bucket by using the REGION option with the COPY command.
A data analyst runs a large number of data manipulation language (DML) queries by using Amazon Athena with the JDBC driver. Recently, a query failed after It ran for 30 minutes. The query returned the following message
Java.sql.SGLException: Query timeout
The data analyst does not immediately need the query results However, the data analyst needs a long-term solution for this problem
Which solution will meet these requirements?
A company has a process that writes two datasets in CSV format to an Amazon S3 bucket every 6 hours. The company needs to join the datasets, convert the data to Apache Parquet, and store the data within another bucket for users to query using Amazon Athen
a. The data also needs to be loaded to Amazon Redshift for advanced analytics. The company needs a solution that is resilient to the failure of any individual job component and can be restarted in case of an error.
Which solution meets these requirements with the LEAST amount of operational overhead?
AWS Glue provides dynamic frames, which are an extension of Apache Spark data frames. Dynamic frames handle schema variations and errors in the data more easily than data frames. They also provide a set of transformations that can be applied to the data, such as join, filter, map, etc.
AWS Glue provides workflows, which are directed acyclic graphs (DAGs) that orchestrate multiple ETL jobs and crawlers. Workflows can handle dependencies, retries, error handling, and concurrency for ETL jobs and crawlers. They can also be triggered by schedules or events.
By creating an AWS Glue job using PySpark that creates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift, the company can perform the required ETL tasks with a single job. By using an AWS Glue workflow to orchestrate the AWS Glue job, the company can schedule and monitor the job execution with minimal operational overhead.
A large telecommunications company is planning to set up a data catalog and metadata management for multiple data sources running on AWS. The catalog will be used to maintain the metadata of all the objects stored in the data stores. The data stores are composed of structured sources like Amazon RDS and Amazon Redshift, and semistructured sources like JSON and XML files stored in Amazon S3. The catalog must be updated on a regular basis, be able to detect the changes to object metadata, and require the least possible administration.
Which solution meets these requirements?
