Datadog announced multiple product launches, including expanded monitoring capabilities for BigQuery.
Datadog’s expanded BigQuery monitoring capabilities, now in preview, help teams view BigQuery usage by user and project to identify those incurring the most spend, pinpoint the long-running queries in those segments to optimize, and detect data quality issues.
“BigQuery is an integral part of Google Cloud users’ tech stacks, enabling them to unlock insights from their proprietary datasets. With Datadog’s new monitoring capabilities, Google Cloud customers can more granularly track usage, attribute costs to users and teams, and ensure their BigQuery data is up to date for accurate insights,” said Yasmeen Ahmad, Managing Director of Strategy & Outbound Product Management for Data, Analytics & AI at Google Cloud.
“Today, it takes significant time to pinpoint where the largest BigQuery usage is coming from across projects and drill into the problematic queries to optimize. With our new BigQuery monitoring capabilities, which complement our existing 35+ Google Cloud integrations, Datadog customers can identify cross-project BigQuery cost centers, high-impact optimization opportunities and the stakeholders that need to be involved,” said Yrieix Garnier, VP of Product at Datadog. “Customers can also improve data quality by detecting data freshness and volume anomalies so they can fix issues quickly and ensure their business has accurate and up-to-date insights.”
Datadog’s expanded BigQuery monitoring capabilities build on the company’s existing capabilities for Google Cloud. Other recent product launches and integrations with Google Cloud include:
- LLM Observability: With Datadog LLM Observability, users can monitor, troubleshoot, improve and secure their Gemini and Vertex AI LLM applications, and get started quickly with auto-instrumentation.
- Cloud TPU Integration: With Datadog’s new Cloud TPU integration, teams can detect resource bottlenecks in—and underutilization of—their TPU infrastructure across workers and GKE clusters.
- Private Service Connect: Datadog users can now send their observability telemetry to Datadog’s Google Cloud-hosted sites with Google’s Private Service Connect for better data security and reduced data transfer costs.
- GKE Autoscaling (in Preview): Datadog Kubernetes Autoscaling gives users multi-dimensional workload scaling recommendations for their GKE environment and the ability to automate them within the Datadog platform, enabling teams to deliver cost savings while maintaining performance and stability.
- Storage Monitoring (in Preview): With Storage Monitoring for Google Cloud Storage, users get visibility into their Google Cloud Storage at the object and prefix levels, enabling teams to identify bottlenecks, track performance and quickly detect unusual growth in their storage consumption.
- Google Cloud Cost Recommendations (in Preview): Datadog Cloud Cost Management now automatically identifies cost inefficiencies in Google Cloud environments and provides optimization recommendations for Google Cloud services like Cloud Run and Cloud SQL.
These capabilities further enhance Datadog’s ability to provide world-class observability and security at scale for joint customers.
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