Operations | Monitoring | ITSM | DevOps | Cloud

Adoption of Azure Functions in cloud-native applications on Microsoft Azure has been increasing exponentially over the last few years. Serverless functions, such as the Azure Functions, provide a high level of abstraction from the underlying infrastructure and orchestration, given these tasks are managed by the cloud provider. Software development teams can then focus on the implementation of business and application logic.

InfluxDB is a powerful tool for managing time-series data. It is widely used in industries such as IoT, finance, healthcare, and more. Using InfluxDB, you can query and store large amounts of data in real-time, making it easier to identify patterns, trends, and anomalies. InfluxDB dashboards provide a comprehensive overview of your system performance, metrics, and KPIs in real-time. You can customize these dashboards to meet your specific requirements.

Enterprises are accumulating more and more observability and security data in isolated silos, not much different than the dust and spare change under couches and chairs in your grandparent’s rarely-used living room. There is something of value in both examples, but the nature of the value is very unclear and hard to measure without a lot of effort.

Today's applications are incredibly intricate and interconnected, often relying on numerous third-party services and libraries. With this complexity comes an increased likelihood of things going wrong. However, an error doesn't usually announce itself with great fanfare and a detailed explanation. More often than not, it shows up as an unexplained crash, a suspicious slowdown, or a surprising output. Error logging shines a spotlight on these problems.

At Logz.io, we’re seeing a very fast pace of adoption for Kubernetes–at this point, it’s even outpacing cloud adoption, with companies running on-prem fully adopting Kubernetes in production. Why are companies going in this direction? Kubernetes provides additional layers of abstraction, which helps create business agility and flexibility for deploying critical applications. At the same time, those abstraction layers create additional complexity for observability.