DEA-C01 practice question 9 of 25

A company maintains a data warehouse in an on-premises Oracle database. The company wants to build a data lake on AWS. The company wants to load data…

A company maintains a data warehouse in an on-premises Oracle database. The company wants to build a data lake on AWS. The company wants to load data warehouse tables into Amazon S3 and synchronize the tables with incremental data that arrives from the data warehouse every day. Each table has a column that contains monotonically increasing values. The size of each table is less than 50 GB. The data warehouse tables are refreshed every night between 1 AM and 2 AM. A business intelligence team queries the tables between 10 AM and 8 PM every day. Which solution will meet these requirements in the MOST operationally efficient way?

Answer options

A: Use an AWS Database Migration Service (AWS DMS) full load plus CDC job to load tables that contain monotonically increasing data columns from the on-premises data warehouse to Amazon S3. Use custom logic in AWS Glue to append the daily incremental data to a full-load copy that is in Amazon S3.

B: Use an AWS Glue Java Database Connectivity (JDBC) connection. Configure a job bookmark for a column that contains monotonically increasing values. Write custom logic to append the daily incremental data to a full-load copy that is in Amazon S3. Correct answer

C: Use an AWS Database Migration Service (AWS DMS) full load migration to load the data warehouse tables into Amazon S3 every day Overwrite the previous day's full-load copy every day.

D: Use AWS Glue to load a full copy of the data warehouse tables into Amazon S3 every day. Overwrite the previous day's full-load copy every day.

Correct Answer:

B. Use an AWS Glue Java Database Connectivity (JDBC) connection. Configure a job bookmark for a column that contains monotonically increasing values. Write custom logic to append the daily incremental data to a full-load copy that is in Amazon S3.

Explanation

The goal is the most operationally efficient way to perform a daily incremental load from a source with a monotonically increasing column. AWS Glue job bookmarks are designed for this exact scenario. By specifying the column with increasing values as the bookmark key, Glue automatically tracks the last value processed. On subsequent runs, the job processes only new rows with a key value greater than the last one, eliminating the need for custom tracking logic or inefficient daily full reloads.

Why Incorrect

A. AWS DMS with CDC is for continuous replication, which is overly complex for a once-daily batch load. It is not the most operationally efficient choice here.
C. Performing a full load with AWS DMS every day is inefficient, as it re-transfers all data instead of just the daily increment.
D. Performing a full load with AWS Glue every day is similarly inefficient and costly compared to an incremental load using job bookmarks.

References

1. AWS Glue Developer Guide. (2023). Tracking Processed Data Using Job Bookmarks. AWS Documentation. "AWS Glue tracks data that has already been processed during a previous run of an ETL job by persisting state information from the job run. This persisted state information is called a job bookmark... Job bookmarks help AWS Glue maintain state information and prevent the reprocessing of old data."

2. AWS Glue Developer Guide. (2023). Job bookmark keys. AWS Documentation. "For JDBC sources with monotonically increasing and unique values in a column, you can have AWS Glue bookmark the rows that have been processed."

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