Operations | Monitoring | ITSM | DevOps | Cloud

Time series data is a fundamental part of numerous real-world applications, from stock market analysis to weather forecasting to financial market forecasting. Effectively managing, analyzing, and visualizing time series data is essential for extracting meaningful insights and making informed decisions. This is where pandas time series comes into play. It can help you organize, transform, and visualize data and examine details for a specific time period.

While many segments of the IT market move quickly, the observability space seems to move at lightning speed. Fueled by open source innovation, observability toolsets and best practices constantly evolve. Sometimes, it can be tough to keep up — and even tougher to know where your own observability strategy stands. That’s the exact challenge we aim to address with our new Observability Journey Maturity Model.

Among the arsenal of tools available for monitoring and managing an organisation’s mission critical applications and service, New Relic is a popular choice for many users. In our article, we will delve into the world of New Relic Dashboards, shedding light on the best use cases that demonstrate their visualisation capabilities.

Splunk Observability Cloud includes powerful features which automatically identify patterns within your data to surface trends. The resulting insights tell you why some customers aren’t getting an optimal experience from your application, and how you can improve it. Unlocking these features requires attributes to be included with your application traces. But how do you know which attributes are the most valuable for your application and business?

Google Cloud Platform (GCP) enables organizations to create and scale applications. Activities in applications, whether on Compute Engine or other services from virtual machines to serverless environments on GCP, produce a significant amount of logs. Logs play a crucial role in helping you achieve effective observability and troubleshooting. But the logs may experience irregular surges in data ingestion during major system events, posing challenges for network overhead.

Both OpenTelemetry and Prometheus are open-source projects under the Cloud Native Computing Foundation. OpenTelemetry is a more comprehensive observability framework with support for metrics, traces, and logs. In contrast, Prometheus is focused specifically on time-series metrics. OpenTelemetry is more versatile, and if you’re confused between choosing between the two, go for OpenTelemetry. We will delve deeper into the reason for choosing OpenTelemetry over Prometheus in this article.

Earlier today, Microsoft Teams experienced service degradation causing multiple issues for users. Users attempting to log in were presented with an “oops” page, while already-logged-in users were missing messages, experiencing issues with loading messages in channels and chats and preventing them from viewing or downloading media (images, video, audio, etc…) Exoprise proactive monitoring first detected the outage in North America starting shortly before 11 AM EST.

Observability isn't just a buzzword; it's a vital compass guiding us through the maze of system health and performance. As we’ve adopted microservice architectures, the ability to know ‘what is currently happening in our system’ has diminished as our operational resilience has increased. We find services scattered among a maze of interconnections and interdependencies. And even the logs that used to guide are now scattered throughout this maze.

Judging by the tweet storm (or is that called an X storm now?), productivity in corporate North America took a big hit as the week ended. Microsoft Teams had an issue that was “impacting multiple Microsoft Teams features”. Users reported problems of: messages not posting, inability to access files, and interrupted workflows.