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An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures. The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date. As the amount of data increases, the company wants to optimize the storage solution to improve query performance. Which combination of solutions will meet these requirements? (Choose two.)
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A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class. A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year. The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability. Which solution will meet these requirements in the MOST cost-effective way?
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Question 99 - DEA-C01 discussion

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A company uploads .csv files to an Amazon S3 bucket. The company's data platform team has set up an AWS Glue crawler to perform data discovery and to create the tables and schemas.

An AWS Glue job writes processed data from the tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creates the Amazon Redshift tables in the Redshift database appropriately.

If the company reruns the AWS Glue job for any reason, duplicate records are introduced into the Amazon Redshift tables. The company needs a solution that will update the Redshift tables without duplicates.

Which solution will meet these requirements?

A.

Modify the AWS Glue job to copy the rows into a staging Redshift table. Add SQL commands to update the existing rows with new values from the staging Redshift table.

Answers
A.

Modify the AWS Glue job to copy the rows into a staging Redshift table. Add SQL commands to update the existing rows with new values from the staging Redshift table.

B.

Modify the AWS Glue job to load the previously inserted data into a MySQL database. Perform an upsert operation in the MySQL database. Copy the results to the Amazon Redshift tables.

Answers
B.

Modify the AWS Glue job to load the previously inserted data into a MySQL database. Perform an upsert operation in the MySQL database. Copy the results to the Amazon Redshift tables.

C.

Use Apache Spark's DataFrame dropDuplicates() API to eliminate duplicates. Write the data to the Redshift tables.

Answers
C.

Use Apache Spark's DataFrame dropDuplicates() API to eliminate duplicates. Write the data to the Redshift tables.

D.

Use the AWS Glue ResolveChoice built-in transform to select the value of the column from the most recent record.

Answers
D.

Use the AWS Glue ResolveChoice built-in transform to select the value of the column from the most recent record.

Suggested answer: A

Explanation:

To avoid duplicate records in Amazon Redshift, the most effective solution is to perform the ETL in a way that first loads the data into a staging table and then uses SQL commands like MERGE or UPDATE to insert new records and update existing records without introducing duplicates.

Using Staging Tables in Redshift:

The AWS Glue job can write data to a staging table in Redshift. Once the data is loaded, SQL commands can be executed to compare the staging data with the target table and update or insert records appropriately. This ensures no duplicates are introduced during re-runs of the Glue job.

Alternatives Considered:

B (MySQL upsert): This introduces unnecessary complexity by involving another database (MySQL).

C (Spark dropDuplicates): While Spark can eliminate duplicates, handling duplicates at the Redshift level with a staging table is a more reliable and Redshift-native solution.

D (AWS Glue ResolveChoice): The ResolveChoice transform in Glue helps with column conflicts but does not handle record-level duplicates effectively.

Amazon Redshift MERGE Statements

Staging Tables in Amazon Redshift

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asked 29/10/2024
Ilia Voronkov
41 questions
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