Skip to main content

SignalFx Introduces New Integration with Jira Software Cloud

SignalFx announced a new integration with Atlassian’s Jira Software to expedite problem resolution and streamline DevOps workflows when developers are using Jira Cloud or Jira Server.

The new functionality enables real-time alerts in SignalFx to automatically trigger Jira issues that include relevant metrics with additional context. As a result, developers can be faster and more efficient in resolving incidents and improving overall customer experience.

“We give teams the tools they need to work together to build great software,” said Matt Ryall, Head of Product, Jira Software Cloud. “By integrating Jira and SignalFx, developers and site reliability engineering (SRE) teams can collaborate more closely to resolve incidents and ensure they’re delivering the highest-quality software possible. As users of the SignalFx platform ourselves, we benefit from SignalFx’s unique ability to provide our DevOps teams with real-time visibility into the health and performance of our services. We are excited about the value this new integration will bring to all Jira users.”

With this new functionality, SignalFx alerts can now be configured to automatically trigger a Jira issue that immediately initiates an investigatory workflow, capturing the entire history throughout the resolution process. The SignalFx-generated Jira issue includes critical context about the incident, including time of occurrence and metrics that meet the configured alert conditions, along with default integration settings such as the Jira project, owner, and priority.

By integrating Jira and SignalFx, organizations can benefit from:

- Faster time to resolution. Alerting from SignalFx notifies the SRE team on service-wide patterns relevant to performance in real time. With the addition of Jira issues, developers can immediately begin investigating and remediating the issue, reducing mean time to resolution (MTTR). Once the alert condition clears, a comment is added to the issue, further improving MTTR and reducing effort by preventing teams from working on issues that have already cleared.

- Deeper collaboration between SRE and developer teams. With the integration, SRE teams are able to automatically provide their developer counterparts insights into incidents. They can also configure the issue to be assigned to a specific Jira project and developer based on specified conditions.

-Improved developer productivity. SignalFx-enriched Jira issues help accelerate a more efficient resolution process by making better use of developer time while speeding time to resolution.

- Proactive enhancements and issue resolution. With the integration, developers now have a closed-loop mechanism whereby they can push code and immediately track the impact of their changes in real time. This gives them the ability to proactively set alerts, uncover areas where their application is showing signs of early stress, and follow-up on issues during normal business hours without being under the time pressure of an outage.

“The DevOps model has allowed organizations to move faster and innovate more rapidly, but this velocity comes with an added complexity that also extends to incidents,” said Patrick Lin, Chief Product Officer, SignalFx. “Our new integration between SignalFx and Jira delivers real-time insights into issues that arise during the DevOps lifecycle. By tightly coupling the tools used by SRE teams and developers, incidents are resolved faster and more efficiently, getting developers back to their primary role of building great software.”

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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 ...

SignalFx Introduces New Integration with Jira Software Cloud

SignalFx announced a new integration with Atlassian’s Jira Software to expedite problem resolution and streamline DevOps workflows when developers are using Jira Cloud or Jira Server.

The new functionality enables real-time alerts in SignalFx to automatically trigger Jira issues that include relevant metrics with additional context. As a result, developers can be faster and more efficient in resolving incidents and improving overall customer experience.

“We give teams the tools they need to work together to build great software,” said Matt Ryall, Head of Product, Jira Software Cloud. “By integrating Jira and SignalFx, developers and site reliability engineering (SRE) teams can collaborate more closely to resolve incidents and ensure they’re delivering the highest-quality software possible. As users of the SignalFx platform ourselves, we benefit from SignalFx’s unique ability to provide our DevOps teams with real-time visibility into the health and performance of our services. We are excited about the value this new integration will bring to all Jira users.”

With this new functionality, SignalFx alerts can now be configured to automatically trigger a Jira issue that immediately initiates an investigatory workflow, capturing the entire history throughout the resolution process. The SignalFx-generated Jira issue includes critical context about the incident, including time of occurrence and metrics that meet the configured alert conditions, along with default integration settings such as the Jira project, owner, and priority.

By integrating Jira and SignalFx, organizations can benefit from:

- Faster time to resolution. Alerting from SignalFx notifies the SRE team on service-wide patterns relevant to performance in real time. With the addition of Jira issues, developers can immediately begin investigating and remediating the issue, reducing mean time to resolution (MTTR). Once the alert condition clears, a comment is added to the issue, further improving MTTR and reducing effort by preventing teams from working on issues that have already cleared.

- Deeper collaboration between SRE and developer teams. With the integration, SRE teams are able to automatically provide their developer counterparts insights into incidents. They can also configure the issue to be assigned to a specific Jira project and developer based on specified conditions.

-Improved developer productivity. SignalFx-enriched Jira issues help accelerate a more efficient resolution process by making better use of developer time while speeding time to resolution.

- Proactive enhancements and issue resolution. With the integration, developers now have a closed-loop mechanism whereby they can push code and immediately track the impact of their changes in real time. This gives them the ability to proactively set alerts, uncover areas where their application is showing signs of early stress, and follow-up on issues during normal business hours without being under the time pressure of an outage.

“The DevOps model has allowed organizations to move faster and innovate more rapidly, but this velocity comes with an added complexity that also extends to incidents,” said Patrick Lin, Chief Product Officer, SignalFx. “Our new integration between SignalFx and Jira delivers real-time insights into issues that arise during the DevOps lifecycle. By tightly coupling the tools used by SRE teams and developers, incidents are resolved faster and more efficiently, getting developers back to their primary role of building great software.”

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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 ...