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.
The Latest
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...
AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...
Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...
A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...
IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...
A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...
According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...
2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...
Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...