Question 123
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 initial 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.)
- A. Denormalize the data as must as possible.
- B. Preserve the structure of the data as much as possible.
- C. Use BigQuery UPDATE to further reduce the size of the dataset.
- D. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
- E. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file. Use BigQuery's support for external data sources to query.
https://cloud.google.com/bigquery/docs/best-practices-performance-nested
Best practice: Use nested and repeated fields to denormalize data storage and increase query performance. Denormalization is a common strategy for increasing read performance for relational datasets that were previously normalized. The recommended way to denormalize data in BigQuery is to use nested and repeated fields. It's best to use this strategy when the relationships are hierarchical and frequently queried together, such as in parent-child relationships.