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Nobl9 Introduces Composite SLO 2.0

Nobl9 announced Nobl9 Composite SLO 2.0 to allow for the aggregation of multiple Service Level Objectives (SLOs) within a single SLO to provide an overall reliability performance view of your service.

The addition of composites allows users to capture an end-to-end user journey for a complex service such as an ecommerce website or a digital banking platform.

Composite SLO 2.0 unlocks SLOs at scale, differentiated by business units or use cases, enabling IT leaders to link their strategy directly to business impact. Teams at different levels of an organization can combine the disparate elements of their projects into composite SLOs. In turn, managers can aggregate these composites into a unified overarching hierarchy — a composite built of composites — to view the system’s performance holistically, with rich context of each component’s behavior easily accessible in a single dashboard.

Before Composite SLO 2.0, engineers could create SLOs to track the reliability of system components they were directly responsible for; for example, an infrastructure engineer might build an SLO for their servers. While traditional SLOs offer important insight, they lack information on how an element affects the total reliability of a complex system. Composite SLO 2.0 brings this critical context, allowing teams to link individual elements into a dynamic system-wide SLO and see how their small portion of the system is impacting end-user experience. Providing such perspective not only informs IT decision-making but shows engineers, even those building small pieces of the backend, how meaningful their work is to overall customer experience.

Nobl9 is partnering with a growing ecosystem of data sources such as Datadog, Splunk, Google BigQuery, and Amazon CloudWatch to offer maximum value to its customers, who often utilize a wide variety of monitoring and observability tools. Nobl9 collects and normalizes data from these various systems, and contextualizes it in a user-friendly dashboard, so customers can simply log into Nobl9 and build SLOs with relevant data from anywhere.

Nobl9 Composite SLO 2.0 allows users to:

- Create composites from a large number of SLOs — Aggregate SLOs from many components of a system into a single hierarchy, giving teams and executives a reliability metric for their entire product or service.

- Combine data from different data sources and projects — Unite SLOs based on data from Dynatrace, Amazon CloudWatch, and other sources for enhanced flexibility, enabling more management-oriented SLOs tailored to each ongoing project.

- Easily identify the biggest sources of error budget burn — Observe the behavior of complex systems and drill down into the error budget consumption of each component to understand root causes of issues.

- Assign weights to component SLOs — Adjust the “weight” of each SLO to ensure that SLOs with larger impact on the user experience contribute more to error budget burn; and,

- Build composites out of other composites — Receive unified reliability metrics by building composite SLOs of near-infinite size and complexity out of any existing composites.

“Composite SLO 2.0 is the culmination of a years-long effort to support anyone responsible for reliable products with the most advanced SLO capabilities possible. Anyone who uses SLOs can now understand how they are steering the ship that stakeholders care about,” said Brian Singer, co-founder and Chief Product Officer, Nobl9. “Composite SLOs behave just like normal SLOs, but provide management with a detailed overview of reliability cascading down to the smallest element. We hope this will give engineers pride in their work, because they will understand how their efforts directly impact end-users, and by extension the health of their company.”

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Nobl9 Introduces Composite SLO 2.0

Nobl9 announced Nobl9 Composite SLO 2.0 to allow for the aggregation of multiple Service Level Objectives (SLOs) within a single SLO to provide an overall reliability performance view of your service.

The addition of composites allows users to capture an end-to-end user journey for a complex service such as an ecommerce website or a digital banking platform.

Composite SLO 2.0 unlocks SLOs at scale, differentiated by business units or use cases, enabling IT leaders to link their strategy directly to business impact. Teams at different levels of an organization can combine the disparate elements of their projects into composite SLOs. In turn, managers can aggregate these composites into a unified overarching hierarchy — a composite built of composites — to view the system’s performance holistically, with rich context of each component’s behavior easily accessible in a single dashboard.

Before Composite SLO 2.0, engineers could create SLOs to track the reliability of system components they were directly responsible for; for example, an infrastructure engineer might build an SLO for their servers. While traditional SLOs offer important insight, they lack information on how an element affects the total reliability of a complex system. Composite SLO 2.0 brings this critical context, allowing teams to link individual elements into a dynamic system-wide SLO and see how their small portion of the system is impacting end-user experience. Providing such perspective not only informs IT decision-making but shows engineers, even those building small pieces of the backend, how meaningful their work is to overall customer experience.

Nobl9 is partnering with a growing ecosystem of data sources such as Datadog, Splunk, Google BigQuery, and Amazon CloudWatch to offer maximum value to its customers, who often utilize a wide variety of monitoring and observability tools. Nobl9 collects and normalizes data from these various systems, and contextualizes it in a user-friendly dashboard, so customers can simply log into Nobl9 and build SLOs with relevant data from anywhere.

Nobl9 Composite SLO 2.0 allows users to:

- Create composites from a large number of SLOs — Aggregate SLOs from many components of a system into a single hierarchy, giving teams and executives a reliability metric for their entire product or service.

- Combine data from different data sources and projects — Unite SLOs based on data from Dynatrace, Amazon CloudWatch, and other sources for enhanced flexibility, enabling more management-oriented SLOs tailored to each ongoing project.

- Easily identify the biggest sources of error budget burn — Observe the behavior of complex systems and drill down into the error budget consumption of each component to understand root causes of issues.

- Assign weights to component SLOs — Adjust the “weight” of each SLO to ensure that SLOs with larger impact on the user experience contribute more to error budget burn; and,

- Build composites out of other composites — Receive unified reliability metrics by building composite SLOs of near-infinite size and complexity out of any existing composites.

“Composite SLO 2.0 is the culmination of a years-long effort to support anyone responsible for reliable products with the most advanced SLO capabilities possible. Anyone who uses SLOs can now understand how they are steering the ship that stakeholders care about,” said Brian Singer, co-founder and Chief Product Officer, Nobl9. “Composite SLOs behave just like normal SLOs, but provide management with a detailed overview of reliability cascading down to the smallest element. We hope this will give engineers pride in their work, because they will understand how their efforts directly impact end-users, and by extension the health of their company.”

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...