Professional Data EngineerEnsuring solution qualityEasy
A marketing analytics team uses BigQuery for ad-hoc analysis and reporting. They frequently run queries on large datasets, leading to unpredictable monthly costs due to BigQuery's on-demand pricing model. The team has a consistent budget for data analytics and prefers predictable spending. You need to recommend a BigQuery pricing model that provides cost predictability and potentially better performance for their workload. Which pricing model should they choose?
- AFlat-rate pricing (Slots Reservations).
- BOn-demand pricing.
- CStorage pricing.
- DStreaming inserts pricing.
Show answer & explanationAnswer & explanation
Correct answer: A. Flat-rate pricing (Slots Reservations).
BigQuery's flat-rate pricing (Slots Reservations) allows users to purchase dedicated query processing capacity (slots) at a fixed monthly or annual cost. This provides predictable spending, regardless of the amount of data processed, aligning with the requirement for a consistent budget and potentially better performance for consistent workloads.
Why the other options are wrong
- B. On-demand pricing charges based on the amount of data processed by queries, leading to unpredictable costs, which is the problem the question aims to solve.
- C. Storage pricing applies to the data stored in BigQuery, not the query processing, and does not address query cost predictability.
- D. Streaming inserts pricing applies to data ingestion into BigQuery, not query processing, and is irrelevant to the problem of unpredictable query costs.
BigQuery Pricing Models
BigQuery offers different pricing models to accommodate various workloads and cost predictability needs.
- On-demand: Pay per query (data scanned).
- Flat-rate: Pay for dedicated processing capacity (slots).
- Storage: Pay for data stored.
Memory trick: BigQuery pricing is like choosing between a taxi (on-demand) or renting a car for a month (flat-rate).