AWS DMS vs Glue for Incremental Data Lake Sync
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?
Community Votes
80% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
Tests knowledge of incremental ingestion patterns; the trap is assuming DMS is required for CDC when Glue's JDBC connector handles monotonically increasing columns more efficiently via job bookmarks.
Determines the most operationally efficient method to sync on-premises Oracle data to Amazon S3 using AWS services. Establishes that AWS Glue with job bookmarks is superior to DMS for simple incremental loads to S3 due to native bookmarking capabilities.
Candidates often choose AWS DMS (Option A) because it supports CDC, but they overlook that DMS to S3 requires custom logic or Lambda for incremental updates, whereas Glue has built-in job bookmarks for this exact scenario.
Community Discussion (3 comments)
Comments & Corrections
No comments yet — spotted an error or have a note? Share it below.
Expert Analysis
Why the Answer Is Correct
Option B is correct because AWS Glue's JDBC source connector supports 'Job Bookmarks,' which automatically track the progress of data extraction based on a specified column (in this case, the monotonically increasing values). This allows Glue to fetch only new records incrementally without needing custom application logic to manage state. It is the most operationally efficient solution as it minimizes code maintenance and leverages native service features.Why the Other Options Are Wrong
Option A suggests using AWS DMS with custom logic in AWS Glue. While DMS supports CDC, moving data from DMS to S3 incrementally typically requires additional components like AWS Lambda or Kinesis Data Firehose with custom transformation logic, making it more complex than necessary. Option C and D suggest full daily loads. Given the table size (<50 GB) and the requirement for synchronization, full loads are less efficient than incremental updates, especially if the goal is to minimize load windows and storage costs over time.Community Comment Notes
The community vote was split between A (80%) and C (20%). User Fawk correctly identified DMS as a potential candidate but dismissed C as obviously wrong due to the inefficiency of full daily overwrites. User Parandhaman_Margan supported A, likely focusing on DMS's robustness. However, the key differentiator is operational efficiency: Glue's job bookmarks eliminate the need for the 'custom logic' mentioned in Option A, making B the cleaner, more managed solution.Exam Strategy
When asked for 'operationally efficient' solutions involving incremental data loads from relational databases to object storage, prioritize AWS Glue with Job Bookmarks over DMS unless you specifically need database-to-database replication or complex schema evolution handling.
Frequently Asked Questions
Why not use AWS DMS for CDC to S3?
DMS can replicate to S3, but managing incremental loads usually requires extra Lambda functions or Kinesis streams. Glue Job Bookmarks handle this natively.
Is a full daily load ever acceptable here?
With tables under 50 GB, full loads are possible, but 'incremental' requirements and 'operational efficiency' favor Glue's bookmarked incremental approach.
Related Analysis
Practice All DEA-C01 Questions
Access 100 questions with complete answers and detailed explanations.
View Full DEA-C01 Practice Test →