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PROFESSIONAL-DATA-ENGINEER · Question #206
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of…
The correct answer is A. Denormalize the data as must as possible. D. Develop a data pipeline where status updates are appended to BigQuery instead of updated. Explanation/Reference: Denormalization will help in performance by reducing query time, update are not good with bigquery.
Submitted by ahmad_uae· Mar 30, 2026Designing data processing systems
Question
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this data. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? (Choose two.)
Options
- ADenormalize the data as must as possible.
- BPreserve the structure of the data as much as possible.
- CUse BigQuery UPDATE to further reduce the size of the dataset.
- DDevelop a data pipeline where status updates are appended to BigQuery instead of updated.
- ECopy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file.
How the community answered
(33 responses)- A70% (23)
- B3% (1)
- C9% (3)
- E18% (6)
Explanation
Explanation/Reference: Denormalization will help in performance by reducing query time, update are not good with bigquery.
Topics
#BigQuery#data denormalization#append vs update#ML pipeline design
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