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Datadog Monitoring for Oracle Cloud Infrastructure Released

Datadog announced the general availability of Datadog Monitoring for Oracle Cloud Infrastructure (OCI), which enables Oracle customers to monitor enterprise cloud-native and traditional workloads on OCI with telemetry in context across their infrastructure, applications and services.

With this launch, Datadog helps customers migrate with confidence from on-premises to cloud environments, execute multi-cloud strategies and monitor AI/ML inference workloads.

Datadog Monitoring for Oracle Cloud Infrastructure helps customers:

- Gain visibility into OCI and hybrid environments: Teams can collect and analyze metrics from their OCI stack by using Datadog’s integrations for 20+ major OCI services and 750+ other technologies. In addition, customers can visualize the performance of OCI cloud services, on-premises servers, VMs, databases, containers and apps in near-real time with customizable, drag-and-drop, and out-of-the-box dashboards and monitors.

- Monitor AI/ML inference workloads: Teams can monitor and receive alerts on the usage and performance of GPUs, investigate root causes, monitor operational performance and evaluate the quality, privacy and safety of LLM applications.

- Get code-level visibility into applications: Real-time service maps, AI-powered synthetic monitors and alerts on latency, exceptions, code-level errors, log issues and more give teams deeper insight into the health and performance of their applications, including those using Java.

“With today’s announcement, Datadog enables Oracle customers to unify monitoring of OCI, on-premises environments and other clouds in a single pane of glass for all teams,” said Yrieix Garnier, VP of Product at Datadog. “This helps teams migrate to the cloud and execute multi-cloud strategies with confidence, knowing that they can monitor services side-by-side, visualize performance data during all stages of a migration and immediately identify service dependencies.”

Datadog Monitoring for Oracle Cloud Infrastructure is generally available now.

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Datadog Monitoring for Oracle Cloud Infrastructure Released

Datadog announced the general availability of Datadog Monitoring for Oracle Cloud Infrastructure (OCI), which enables Oracle customers to monitor enterprise cloud-native and traditional workloads on OCI with telemetry in context across their infrastructure, applications and services.

With this launch, Datadog helps customers migrate with confidence from on-premises to cloud environments, execute multi-cloud strategies and monitor AI/ML inference workloads.

Datadog Monitoring for Oracle Cloud Infrastructure helps customers:

- Gain visibility into OCI and hybrid environments: Teams can collect and analyze metrics from their OCI stack by using Datadog’s integrations for 20+ major OCI services and 750+ other technologies. In addition, customers can visualize the performance of OCI cloud services, on-premises servers, VMs, databases, containers and apps in near-real time with customizable, drag-and-drop, and out-of-the-box dashboards and monitors.

- Monitor AI/ML inference workloads: Teams can monitor and receive alerts on the usage and performance of GPUs, investigate root causes, monitor operational performance and evaluate the quality, privacy and safety of LLM applications.

- Get code-level visibility into applications: Real-time service maps, AI-powered synthetic monitors and alerts on latency, exceptions, code-level errors, log issues and more give teams deeper insight into the health and performance of their applications, including those using Java.

“With today’s announcement, Datadog enables Oracle customers to unify monitoring of OCI, on-premises environments and other clouds in a single pane of glass for all teams,” said Yrieix Garnier, VP of Product at Datadog. “This helps teams migrate to the cloud and execute multi-cloud strategies with confidence, knowing that they can monitor services side-by-side, visualize performance data during all stages of a migration and immediately identify service dependencies.”

Datadog Monitoring for Oracle Cloud Infrastructure is generally available now.

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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 ...