Operations | Monitoring | ITSM | DevOps | Cloud

Manually sharing mobile app builds with Quality Assurance (QA) engineers can be a tedious and error-prone process. Developers often find themselves exporting.apk or.ipa files, uploading them to Google Drive or Dropbox, and then pinging the QA team on Slack to announce the upload, all while juggling deadline and code reviews. This manual process not only slows down feedback cycles but also leaves room for human error, miscommunication, or outdated builds being tested.

If you’re running Calico using manifests, you may have found that enabling the observability features introduced in version 3.30, including Whisker and Goldmane, requires a more hands-on approach. Earlier documentation focused on the Tigera operator, which automates key tasks such as certificate management and secure service configuration. In a manifest-based setup, these responsibilities shift to the user.

For many enterprises, incident response feels like déjà vu. The same issues keep happening over and over, eating up time, draining resources, and wearing down your teams. In fact, 20-40% of IT incidents are typically recurring issues, created by unresolved underlying problems. Teams prioritize speed over permanence, patching symptoms instead of addressing the root cause. They often lack the right context, documentation, or shared knowledge to permanently fix issues.

Both user and server-side monitoring are important to make your apps better. Tools that offer monitoring of just one side leave gaps in your diagnosis, causing negative experiences and reliability issues. Here are the top 10 tools you should consider based on their benefits and coverage.

We’ve been working on a number of items here at InfluxData to give you even more options for creating visualizations and dashboards for your time series data in InfluxDB 3.

In today’s high-performance data centers, “all green” dashboards can mask catastrophic issues hiding just beneath the surface. If you’re missing the microbursts, hidden oversubscription, and routing imbalances that are devastating application performance, you’re flying blind. Learn how to close these visibility gaps and shift from reactive firefighting to proactive network intelligence.

Here's some excellent news for the Pythonistas in the room: You can now monitor the performance of your Python applications with Honeybadger. Last year, we launched Honeybadger Insights, a new logging and observability tool bundled with Honeybadger. Insights enables you to query your application logs and events to answer performance questions, perform root-cause analyses, and create charts and dashboards to see what's happening in real time.

Everyone is excited about AI, but few companies have successfully implemented it. While enthusiasm for generative AI (GenAI) has helped accelerate AI adoption across enterprises, the promises of artificial intelligence have yet to translate into measurable impact on most organizations’ bottom lines. The trouble isn’t the tech — it’s a lack of executive ownership.

Today’s enterprise networks are a far cry from the centralized, predictable infrastructures of the past. Instead, they are sprawling, dynamic ecosystems that stretch across cloud services, SaaS applications, on-premises data centers, distributed branches, and thousands of end users connecting from every imaginable location. This complexity creates a huge challenge for IT and network operations teams: How do you get a clear, real-time view of what’s really happening?

I’ve been in those late-night "war room" calls where, after hours of painstaking work, the team finally resolves a critical outage. The dashboards all turn green, a collective sigh of relief is shared, and the next day’s report highlights a victory: Mean time to resolution (MTTR) was reduced by 15% compared to the last major incident. It feels like a win.