AI Agent for
Google BigQuery Cost Optimization

Auto slot reservation reduces spend by 60% when usage increases by 50%

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Case Study

Used as default platform by the Largest Google BigQuery Customer in the US.

The challenge

1. Limited cost visibility and accountability:

Lack of cost transparency and capacity visibility made it difficult to attribute BigQuery spend and make informed migration and contract renewal decisions.

2. Fragmented monitoring and incident management:

Lack of automated detection and alerting led to delayed incident response and reactive pipeline management.

The expectation

Spend was a leaky black box with no way to trace BigQuery costs back to Looker or business users driving them.

The result

100%

Data-SLA alerting & RCA routed through one platform

3x

Expansion in connected systems: BigQuery, Looker,
Composer & Gemini

Automated Cost
Optimization

Performance and
Optimization Insights

Automatic Data
Observability and Quality

Automated Spend Optimization

Observability

Automated Warehouse Optimization continuously optimizes warehouses for optimal efficiency.
By analyzing historical and real-time data, it right-sizes resources and improves query execution.
Proactively adjusts warehouse and cluster sizes based on workload demands.

Performance and Optimization Insights

Observability

Warehouse Performance Report: Automatically know annualized cost savings achieved through dynamic warehouse optimization strategies with query performance and detailed performance metrics
Usage and Efficiency Metrics: Use detailed insights into Snowflake resource usage, query distribution, and user-level credit consumption with warehouse efficiency, credit trends, and idle resources, enabling precise cost optimization and planning.
Automated Warehouse Management: Automatically optimizes your warehouse maximizing annual savings, and policies, helping with strategic resource allocation.

Automatic Data Observability and Quality

Observability

Reduced Downtime: Quick identification and remediation of anomalies mitigate potential disruptions in analytics or operations and preserves the accuracy and trustworthiness of data assets.
Enhanced Operational Productivity: Automating monitoring frees data teams to focus on strategic projects rather than manual quality checks.
End-to-End Visibility: Insight into both upstream causes and downstream impacts provides holistic data quality management.

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Introducing Revefi's AI Agent for Snowflake Data Cost Optimization

Watch a short demo of Revefi’s AI Agent for Snowflake Data Spend Optimization

Key Benefits:

Automated Cost Optimization

Full visibility into spend and resource utilization helps identify and quickly address spending inefficiencies automatically or assisted.

Enhanced Operational Efficiency

Actionable insights on slot utilization and query activity facilitate immediate efficiency improvements.

Automated Data Observability and Quality

Automatic real-time monitoring and identification of data quality incidents strengthens overall trust in organizational data, reducing risk.

24x7 Cost Optimization

Automated Slot Reservation: Automatically optimizes your reservations maximizing annual savings, helping with strategic resource allocation.
Data Infrastructure Health and Operations Management: Proactive data governance and reliability metrics with automatic Data Observability monitors identify operational performance and failures to troubleshoot and reduce downtime,
Usage Insights: Analyze millions of queries and scanned data to identify performance trends and user engagement.

Observability

Observability

Performance and Optimization Insights

Slot Performance Report: Automatically realize annualized cost savings achieved through dynamic slot optimization strategies with query performance and detailed performance metrics,
Usage and Efficiency Metrics: Use detailed insights into Big Query resource usage, query distribution, and user-level credit consumption with slot efficiency, credit trends, and idle resources, enabling precise cost optimization and planning.
Automatically identifies savings while interactive mode helps maximize savings and tune performance.

Automatic Data Observability and Quality

Reduced Downtime: Quick identification and remediation of anomalies mitigate potential disruptions in analytics or operations and preserves the accuracy and trustworthiness of data assets
Enhanced Operational Productivity: Automating monitoring frees data teams to focus on strategic projects rather than manual quality checks
End-to-End Visibility: Insight into both upstream causes and downstream impacts provides complete data quality management

Observability

Videos

Google BigQuery

Google BigQuery Users with the most usage

Google BigQuery

Google BigQuery Usage Driven by Automated Jobs

Google BigQuery

Google BigQuery Slot Contention and Utilization

Google BigQuery

Google BigQuery Overscanning

Google BigQuery

Google BigQuery Effective Partitioning and Clustering

Google BigQuery

Google BigQuery Large Shuffling

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