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Question 273 - MLS-C01 discussion

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A data engineer needs to provide a team of data scientists with the appropriate dataset to run machine learning training jobs. The data will be stored in Amazon S3. The data engineer is obtaining the data from an Amazon Redshift database and is using join queries to extract a single tabular dataset. A portion of the schema is as follows:

...traction Timestamp (Timeslamp)

...JName(Varchar)

...JNo (Varchar)

Th data engineer must provide the data so that any row with a CardNo value of NULL is removed. Also, the TransactionTimestamp column must be separated into a TransactionDate column and a isactionTime column Finally, the CardName column must be renamed to NameOnCard.

The data will be extracted on a monthly basis and will be loaded into an S3 bucket. The solution must minimize the effort that is needed to set up infrastructure for the ingestion and transformation. The solution must be automated and must minimize the load on the Amazon Redshift cluster

Which solution meets these requirements?

A.
Set up an Amazon EMR cluster Create an Apache Spark job to read the data from the Amazon Redshift cluster and transform the data. Load the data into the S3 bucket. Schedule the job to run monthly.
Answers
A.
Set up an Amazon EMR cluster Create an Apache Spark job to read the data from the Amazon Redshift cluster and transform the data. Load the data into the S3 bucket. Schedule the job to run monthly.
B.
Set up an Amazon EC2 instance with a SQL client tool, such as SQL Workbench/J. to query the data from the Amazon Redshift cluster directly. Export the resulting dataset into a We. Upload the file into the S3 bucket. Perform these tasks monthly.
Answers
B.
Set up an Amazon EC2 instance with a SQL client tool, such as SQL Workbench/J. to query the data from the Amazon Redshift cluster directly. Export the resulting dataset into a We. Upload the file into the S3 bucket. Perform these tasks monthly.
C.
Set up an AWS Glue job that has the Amazon Redshift cluster as the source and the S3 bucket as the destination Use the built-in transforms Filter, Map. and RenameField to perform the required transformations. Schedule the job to run monthly.
Answers
C.
Set up an AWS Glue job that has the Amazon Redshift cluster as the source and the S3 bucket as the destination Use the built-in transforms Filter, Map. and RenameField to perform the required transformations. Schedule the job to run monthly.
D.
Use Amazon Redshift Spectrum to run a query that writes the data directly to the S3 bucket. Create an AWS Lambda function to run the query monthly
Answers
D.
Use Amazon Redshift Spectrum to run a query that writes the data directly to the S3 bucket. Create an AWS Lambda function to run the query monthly
Suggested answer: C

Explanation:

The best solution for this scenario is to set up an AWS Glue job that has the Amazon Redshift cluster as the source and the S3 bucket as the destination, and use the built-in transforms Filter, Map, and RenameField to perform the required transformations. This solution has the following advantages:

It minimizes the effort that is needed to set up infrastructure for the ingestion and transformation, as AWS Glue is a fully managed service that provides a serverless Apache Spark environment, a graphical interface to define data sources and targets, and a code generation feature to create and edit scripts1.

It automates the extraction and transformation process, as AWS Glue can schedule the job to run monthly, and handle the connection, authentication, and configuration of the Amazon Redshift cluster and the S3 bucket2.

It minimizes the load on the Amazon Redshift cluster, as AWS Glue can read the data from the cluster in parallel and use a JDBC connection that supports SSL encryption3.

It performs the required transformations, as AWS Glue can use the built-in transforms Filter, Map, and RenameField to remove the rows with NULL values, split the timestamp column into date and time columns, and rename the card name column, respectively4.

The other solutions are not optimal or suitable, because they have the following drawbacks:

A: Setting up an Amazon EMR cluster and creating an Apache Spark job to read the data from the Amazon Redshift cluster and transform the data is not the most efficient or convenient solution, as it requires more effort and resources to provision, configure, and manage the EMR cluster, and to write and maintain the Spark code5.

B: Setting up an Amazon EC2 instance with a SQL client tool to query the data from the Amazon Redshift cluster directly and export the resulting dataset into a CSV file is not a scalable or reliable solution, as it depends on the availability and performance of the EC2 instance, and the manual execution and upload of the SQL queries and the CSV file6.

D: Using Amazon Redshift Spectrum to run a query that writes the data directly to the S3 bucket and creating an AWS Lambda function to run the query monthly is not a feasible solution, as Amazon Redshift Spectrum does not support writing data to external tables or S3 buckets, only reading data from them7.

References:

1:What Is AWS Glue? - AWS Glue

2:Populating the Data Catalog - AWS Glue

3:Best Practices When Using AWS Glue with Amazon Redshift - AWS Glue

4:Built-In Transforms - AWS Glue

5:What Is Amazon EMR? - Amazon EMR

6:Amazon EC2 - Amazon Web Services (AWS)

7:Using Amazon Redshift Spectrum to Query External Data - Amazon Redshift

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asked 16/09/2024
Saran Lertrat
29 questions
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