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Answer: Define data quality rules using AWS Glue DataBrew by creating a new project, selecting the transaction records dataset, and specifying rules to ensure the data is consistent and accurate.
Option C is the correct answer. To ensure the data quality of the transaction records dataset, you should define data quality rules using AWS Glue DataBrew. By creating a new project, selecting the dataset, and specifying rules to ensure the data is consistent and accurate, you can maintain the integrity of the transaction records. Manually inspecting each transaction record (Option A) is not efficient for large datasets. Writing custom scripts (Option B) can be time-consuming and may not cover all possible data quality issues. Ignoring data quality checks (Option D) is not recommended as it can lead to poor data quality and incorrect analysis.
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Your team is working on a data pipeline that processes data from a financial institution. The data includes transaction records with sensitive information. You have been tasked with ensuring the data quality of the transaction records dataset. Describe the steps you would take to run data quality checks on the transaction records dataset and explain how you would define data quality rules to ensure the data is consistent and accurate.
A
Run data quality checks by manually inspecting each transaction record and identifying any inconsistencies or inaccuracies.
B
Use AWS Glue to run data quality checks by writing custom scripts that identify inconsistencies or inaccuracies in the transaction records.
C
Define data quality rules using AWS Glue DataBrew by creating a new project, selecting the transaction records dataset, and specifying rules to ensure the data is consistent and accurate.
D
Ignore data quality checks and assume the data is consistent and accurate.