Google Professional Data Engineer
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NO.22 You want to analyze hundreds of thousands of social media posts daily at the lowest cost and with the fewest steps. You have the following requirements:
- You will batch-load the posts once per day and run them through the Cloud Natural Language API.
- You will extract topics and sentiment from the posts.
- You must store the raw posts for archiving and reprocessing.
- You will create dashboards to be shared with people both inside and outside your organization.
- You need to store both the data extracted from the API to perform analysis as well as the raw social media posts for historical archiving. What should you do?
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A
Store the social media posts and the data extracted from the API in BigQuery.
B
Store the social media posts and the data extracted from the API in Cloud SQL.
C
Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.
D
Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.
Explanation:
Explanation:
Option D is the correct solution because:
- Lowest cost and fewest steps - Feeding posts directly from source to API eliminates intermediate storage steps
- Cloud Storage for archiving - Raw posts can be stored in Cloud Storage for cost-effective archival and reprocessing
- BigQuery for analytics - Extracted data (topics, sentiment) goes to BigQuery for analysis and dashboard creation
- Dashboard sharing - BigQuery integrates well with Data Studio for sharing dashboards internally and externally
- Cost optimization - Cloud Storage is cheaper for raw data storage than BigQuery
- Reprocessing capability - Raw posts in Cloud Storage can be reprocessed if needed
Other options are less optimal:
- Option A: Storing raw posts in BigQuery is expensive for archival
- Option B: Cloud SQL is not designed for large-scale analytics
- Option C: Adds unnecessary step of storing raw posts before processing
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