Amazon Web Services DAS-C01 Dumps

This solution meets the requirements because:

• AWS Transfer Family is a fully managed service that enables secure file transfers to and from Amazon S3 or Amazon EFS using standard protocols such as SFTP, FTPS, and FTP1. By using AWS Transfer Family, the company can reduce the overhead of maintaining the on-premises SFTP server and storing large amounts of data on premises.
• The company can create an SFTP-enabled server with a publicly accessible endpoint using AWS Transfer Family. This endpoint can be accessed by the third-party providers over the internet using their existing SFTP clients. The company can also change the server name to match the name of the on-premises SFTP server, so that the existing upload process for the third-party providers does not change. For more information, see Create an SFTP-enabled server.
• The company can configure the new SFTP server to use Amazon S3 as the storage service. This way, the data files uploaded by the third-party providerswill be stored in an Amazon S3 bucket. The company can also use AWS Identity and Access Management (IAM) roles and policies to control access to the S3 bucket and its objects. For more information, see Using Amazon S3 as your storage service.
• The company can schedule a Python shell job in AWS Glue to use the existing Python script to run periodically and transform the uploaded files. AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data for analytics2. A Python shell job is a type of job that runs Python scripts in a managed Apache Spark environment3. The company can use AWS Glue triggers to schedule the Python shell job based on time or events4. For more information, see Working with Python shell jobs.

This solution meets the requirements because:

• AWS Glue is a fully managed extract, transform, and load (ETL) service that can be used to prepare and load data for analytics1. You can use AWS Glue to create a job that copies the CloudTrail logs from the source S3 bucket to a new S3 bucket, and converts them to Apache Parquet format2. Parquet is a columnar storage format that is optimized for analytics and supports compression3. Snappy is a compression codec that provides a good balance between compression ratio and speed4.
• AWS Glue can also create a table based on the Parquet files in the new S3 bucket, and partition the table by date2. Partitioning is a technique that divides a large dataset into smaller subsets based on a partition key, such as date5. Partitioning can improve query performance by reducing the amount of data scanned and filtering out irrelevant data5.
• Amazon Athena is an interactive query service that allows you to analyze data in S3 using standard SQL6. You can use Athena to query the table created by AWS Glue, and specify the partitions you want to query based on the daterange. Athena can leverage the benefits of Parquet format and partitioning to run queries faster and more cost-effectively.

The correct answer is A. Specify the correct AWS Region for the Amazon S3 bucket by using the REGION option with the COPY command.

The error message indicates that the Amazon S3 bucket and the Redshift cluster are not in the same region. To load data from a different region, the COPY command needs to specify the source region using the REGION option. For example, if the Redshift cluster is in US East (N. Virginia) and the S3 bucket is in Asia Pacific (Mumbai), the COPY command should include REGION ‘ap-south-1’. This option tells Redshift to use the appropriate endpoint to access the S3 bucket. For more information, seeCopy command optionsandCOPY - Amazon Redshift.