DA0-002 Data Acquisition and Preparation Practice Question
A retail company is migrating its on-premises data warehouse to a cloud data warehouse. The current ETL process extracts data from a transactional database (SQL Server) and a web analytics system (JSON logs). The ETL runs nightly and takes 6 hours. The business requires that the new cloud warehouse support real-time reporting with data latency of less than 15 minutes. The data engineer proposes using change data capture (CDC) from the SQL Server database and streaming the JSON logs via a message queue. However, management is concerned about cost and complexity. The engineer must design a solution that meets the latency requirement while minimizing operational overhead. Which approach should the engineer recommend?
⚠ Common exam trap
A common mix-up: candidates choose Option A or D because they seem simpler, but they fail to meet the strict latency requirement or introduce hidden operational complexity, while Option C's CDC and streaming approach is the only one that balances low latency with minimal overhead.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Implement CDC for the SQL Server database and stream the JSON logs via a message queue to the cloud warehouse
CDC captures only changed rows from SQL Server, minimizing data volume and enabling near-real-time ingestion, while streaming JSON logs via a message queue (e.g., Apache Kafka or Amazon Kinesis) provides sub-15-minute latency. This combination meets the latency requirement without the overhead of full batch exports or complex virtualization, addressing management's cost and complexity concerns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Export the SQL Server data to flat files every 15 minutes and use a cloud storage trigger to load
Why it's wrong here
Fifteen-minute flat-file exports still incur extraction, transfer and load time, so end-to-end latency exceeds the target, and the scheduling and file-management overhead is substantial. It is tempting because it reuses familiar batch tooling, and would be correct for hourly or daily reporting where near-real-time freshness is not required.
- ✗
Continue with nightly batch loads but increase the frequency to every hour
Why it's wrong here
Hourly batch loads leave data up to 60 minutes stale, breaching the 15-minute latency requirement regardless of cost savings. It is tempting because it is a minimal change to the existing ETL, and would be correct if the business only needed refreshed reporting each hour rather than near-real-time.
- ✓
Implement CDC for the SQL Server database and stream the JSON logs via a message queue to the cloud warehouse
Why this is correct
CDC captures only changed rows from SQL Server, and a message queue streams JSON logs continuously, cutting latency from six hours to under fifteen minutes. This satisfies the sub-15-minute requirement while avoiding full nightly batch reloads, keeping operational overhead lower than custom polling scripts.
- ✗
Use a data virtualization tool to query the source systems directly without moving data
Why it's wrong here
Data virtualisation queries sources at read time, so it cannot deliver the sub-15-minute latency reliably and pushes transformation work onto source systems, adding overhead rather than removing it. It is tempting because it avoids copying data, and would be correct for ad hoc federated queries where freshness matters less than storage cost.
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