Operations | Monitoring | ITSM | DevOps | Cloud

This post was written by Talha Khalid, a full-stack developer and data scientist who loves to make the cold and hard topics exciting and easy to understand. No one has any doubt that microservices architecture has already proven to be efficient. However, implementing security, particularly in an immutable infrastructure context, has been quite the challenge.

Government and education leaders estimate that data volume at their organizations will increase by 59% over the next three years. Although having more information than you need is (arguably) better than not having it when you need it, the sheer volume of data can make it challenging for teams to pinpoint exactly what data will bring value to their mission goals.

This post was written by Siddhant Varma. Scroll down to read the author’s bio. Software development isn’t just about building and deploying software. There’s a wide range of operations and activities you need to tackle even after you’ve successfully deployed it. The two most common are observability and monitoring. While they’re similar in a lot of ways, it’s important to understand that they are not exactly the same, and each has its own purpose.

Since 2020, the Intel team has been contributing to Telegraf, including both telemetry from Intel-specific platform features (such as Intel® Resource Director Technology, Intel® Dynamic Load Balancer, or power statistics from Intel-based platforms) and telemetry gathered from generic tools and frameworks; for example, Data Plane Development Kit (DPDK), Libvirt, P4 Runtime, or Reliability Availability Serviceability (RAS).

As more companies rely on data insights to drive critical business decisions, data must be accurate, reliable, and of high quality. Gaining insights from data is essential, but so is the data’s integrity so that you can be sure that data isn’t missing, incorrectly added, or misused. This is where data observability comes in.