An enterprise is designing a compute capacity strategy for its analytical workloads in BigQuery. The workload environment consists of three distinct patterns: Production ELT pipelines: Run continuously 24/7 with a predictable, steady baseline compute demand. Data science ad-hoc analytics: Intermittent, bursty compute usage during business hours that requires rapid scaling without manual intervention. Departmental BI dashboards: Variable query volume throughout the day that can tolerate shared capacity and should utilize spare resources when available. You need to optimize compute costs, prevent resource contention between workloads, ensure predictable performance, and maximize resource utilization across the organization. Which capacity management and reservation strategy should you implement?

Create a centralized administration project. Purchase slot commitments for the steady-state baseline and assign them to an ELT reservation. Create separate reservations for data science and BI with configured autoscaling slot limits, and enable idle slot sharing across reservations.: ### What this strategy is This strategy leverages **BigQuery capacity-based workload management** centered in a single **administration project**. It combines **committed slot capacity** for predictable, steady-state workloads with **autoscaling slots** and **idle slot sharing** to serve bursty and variable analytics workloads cost-effectively. ### How it meets the requirements - **Optimize compute costs for steady-state workloads**: Purchasing **slot commitments** for the 24/7 production ELT baseline provides the highest discount rate compared to purely on-demand or non-committed capacity. - **Prevent resource contention**: Dedicated **reservations** (`elt`, `data-science`, `bi`) partition slot capacity so intensive queries in one workload domain do not starve critical production pipelines. - **Handle bursty workloads without manual intervention**: Configuring **autoscaling slot limits** on reservations allows data science and BI workloads to dynamically scale out slots as demand increases and scale down when idle. - **Maximize utilization and efficiency**: Grouping reservations within a single **centralized administration project** allows BigQuery to dynamically share **idle slots** across all reservations, ensuring that unused ELT or BI capacity accelerates active ad-hoc queries. ### Key advantages and capabilities - **Predictable billing and committed savings**: Blends long-term committed use discounts for baseline workloads with flexible autoscaling budgets. - **Noisy neighbor isolation**: Workload assignments to separate reservations guarantee dedicated floor allocations. - **Cross-reservation sharing**: Idle slots automatically shift to reservations experiencing temporary compute spikes without manual rebalancing. - **Centralized governance**: A dedicated administration project centralizes billing, slot management, and assignment policies. ### Why this is optimal This approach strikes the optimal trade-off between cost, performance, and flexibility. Steady-state baseline operations achieve maximum financial efficiency through commitments, while volatile queries scale elastically within defined slot caps and take advantage of shared idle capacity across the organization.

Bigquery Compute Capacity Reservations Strategy | professional-cloud-data-engineer

Bigquery Compute Capacity Reservations Strategy

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An enterprise is designing a compute capacity strategy for its analytical workloads in BigQuery. The workload environment consists of three distinct patterns:

Production ELT pipelines: Run continuously 24/7 with a predictable, steady baseline compute demand.
Data science ad-hoc analytics: Intermittent, bursty compute usage during business hours that requires rapid scaling without manual intervention.
Departmental BI dashboards: Variable query volume throughout the day that can tolerate shared capacity and should utilize spare resources when available.

You need to optimize compute costs, prevent resource contention between workloads, ensure predictable performance, and maximize resource utilization across the organization.

Which capacity management and reservation strategy should you implement?

Create a centralized administration project. Purchase slot commitments for the steady-state baseline and assign them to an ELT reservation. Create separate reservations for data science and BI with configured autoscaling slot limits, and enable idle slot sharing across reservations.

Create a single reservation in an administration project with a static baseline pool of slots, assign all projects to it, and disable idle slot sharing to ensure strict fair-share scheduling.

Keep all workloads on on-demand billing, and apply project-level and user-level byte scan quotas to manage costs and prevent query concurrency bottlenecks.

Create separate administration projects for each workload type. Purchase slot commitments in each administration project and configure static slot reservations without autoscaling to prevent budget overruns.

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Bigquery Compute Capacity Reservations Strategy

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