Embrace launched Network Watchdog, a network monitoring and reporting solution to provide its customers with transparency into application issues.
By using Watchdog by Embrace, the endpoint is measured by how the mobile users' experience the app, and errors and issues are uncovered that cannot be detected from the server-side.
Embrace's new networking dashboard details a mobile app's networking performance, all in one place, surfacing top erroring and slowest calls from all the app's APIs and third-party SDKs. Customers can easily click into any path to see all the users and sessions affected by those calls. Detailed session timelines reproduce exactly where failed calls occur in the user's experience, and whether that user was noticeably affected.
Key features of Embrace's Network Monitoring Dashboards include:
- The ability to solve unresolved crashes for which a trace is not helpful but every single network call would resolve.
- Unsampled tracking of every single network call whether 1st-party API, CDN, or 3rd-party SDK.
- All network errors before they even reach the server so that no error is dropped.
- Automatic insights into which calls most frequently error or are slow to help prioritize which issues to resolve first.
- Clickable workflows into any domain to view every single path belonging to that domain and all the network calls in a given session.
- Alerts by Slack and email on spikes in network errors to catch network outages before end users do. Know when an API changes or errors or a vendor, especially attribution or analytics, is down.
More than 15% of users have suboptimal experiences directly tied to app performance, and while many users complain of “crashes,” almost all of these issues are not true app crashes. Embrace's platform analyzes and replays the details of every single user session to more accurately and quickly detect, diagnose and resolve any user-impacting issues. The platform typically uncovers 5X more issues than previously known by mobile teams and app developers.
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 ...