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Splunk Observability Cloud and Splunk Cloud Platform Integrations and Enhancements Announced

Splunk is previewing the OpenTelemetry Collector as a technical add-on (TA), so Splunk Platform customers can more easily adopt Splunk Observability Cloud and deploy the Collector alongside their existing forwarders to capture metrics and traces.

This new feature eliminates the headaches of deploying and managing two agents by providing customers a unified view of their infrastructure and services. The introduction of the Collector is a milestone in Splunk’s commitment to the OpenTelemetry project and the open-source community by helping customers transmit their data with less complexity and greater flexibility.

With Splunk’s new Unified Identity, ITOps practitioners and engineers can now seamlessly and immediately access Splunk Cloud Platform and Splunk Observability Cloud data with one user identity. As a result, customers can enjoy an improved login experience and instantly access log data from Splunk Cloud Platform data for faster troubleshooting. The integration provides ITOps practitioners and engineers a common set of visualizations for more agile cross-team alignment to drive rapid detection and response.

In addition, the latest advancements in Splunk Cloud Platform and Splunk Enterprise 9.1 enable SecOps, ITOps and engineering teams to visualize data flows across their entire tech stack to foster deeper collaboration, so organizations can scale their operations to respond rapidly and minimize disruptions.

Platform enhancements include:

- Ingest Actions now expands capabilities for routing data to multiple, distinct Amazon S3 buckets, enabling greater granularity in data management.

- The new Federated Search for Amazon S3 preview offers a unified search experience of data at rest in Amazon S3 buckets - without having to ingest that data to Splunk - and across Splunk instances and third party data lakes through its integration with Ingest Actions and Edge Processor for better data movement. In turn, customers avoid latency and unnecessary charges.

- Edge Processor featuring SPL2 now enables data ingestion and export to Splunk using HTTP Event Collector (HEC), making it easier to manage data. In addition, to complement data sovereignty and compliance needs, users can set default destinations per Edge Processor for more flexibility in routing.

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Splunk Observability Cloud and Splunk Cloud Platform Integrations and Enhancements Announced

Splunk is previewing the OpenTelemetry Collector as a technical add-on (TA), so Splunk Platform customers can more easily adopt Splunk Observability Cloud and deploy the Collector alongside their existing forwarders to capture metrics and traces.

This new feature eliminates the headaches of deploying and managing two agents by providing customers a unified view of their infrastructure and services. The introduction of the Collector is a milestone in Splunk’s commitment to the OpenTelemetry project and the open-source community by helping customers transmit their data with less complexity and greater flexibility.

With Splunk’s new Unified Identity, ITOps practitioners and engineers can now seamlessly and immediately access Splunk Cloud Platform and Splunk Observability Cloud data with one user identity. As a result, customers can enjoy an improved login experience and instantly access log data from Splunk Cloud Platform data for faster troubleshooting. The integration provides ITOps practitioners and engineers a common set of visualizations for more agile cross-team alignment to drive rapid detection and response.

In addition, the latest advancements in Splunk Cloud Platform and Splunk Enterprise 9.1 enable SecOps, ITOps and engineering teams to visualize data flows across their entire tech stack to foster deeper collaboration, so organizations can scale their operations to respond rapidly and minimize disruptions.

Platform enhancements include:

- Ingest Actions now expands capabilities for routing data to multiple, distinct Amazon S3 buckets, enabling greater granularity in data management.

- The new Federated Search for Amazon S3 preview offers a unified search experience of data at rest in Amazon S3 buckets - without having to ingest that data to Splunk - and across Splunk instances and third party data lakes through its integration with Ingest Actions and Edge Processor for better data movement. In turn, customers avoid latency and unnecessary charges.

- Edge Processor featuring SPL2 now enables data ingestion and export to Splunk using HTTP Event Collector (HEC), making it easier to manage data. In addition, to complement data sovereignty and compliance needs, users can set default destinations per Edge Processor for more flexibility in routing.

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...