Many people wonder what the difference is between monitoring vs. observability. While monitoring is simply watching a system, observability means truly understanding a system’s state. DevOps teams leverage observability to debug their applications or troubleshoot the root cause of system issues. Peak visibility is achieved by analyzing the three pillars of observability: Logs, metrics and traces.
The volume of data that IT systems generate nowadays is overwhelming, and without intelligent monitoring and analysis tools, it can result in missed opportunities, alerts, and expensive downtime. However, with the advent of Machine Learning and Big Data, a new category of IT operations tool has emerged called AIOps. AIOps can be defined as the practical application of Artificial Intelligence to augment, support, and automate IT processes.
Artificial intelligence for IT operations (AIOps) is an umbrella term for the use of big data analytics, machine learning (ML) and other artificial intelligence (AI) technologies to automate the identification and resolution of common IT issues. The systems, services and applications in a large enterprise produce immense volumes of log and performance data. AIOps uses this data to monitor assets and gain visibility into dependencies within and outside of IT systems.
When discussing Prometheus and Grafana, our VP of Service Delivery said to me, "We can say whatever you want on our website or on a blog post but what REALLY makes a difference in terms of $$$ is DOING it in the field. Applying this in the field with real customers is where everything gets real. My customer was going full tilt to build a project for testing Prometheus, Grafana, ELK and Splunk for leveraging data intelligence until we stopped them. We told them unabashedly: 'Gents, we're sorry but that's just a messy strategy. You should be using XRay for that.' Sometimes you just have to go balls out with a customer ... well, they listened, and we delivered."
A digital platform may have billions of messages flowing through it each day, with real-time updates considered the standard by customers and enterprises. Ensuring that messages aren’t duplicated or lost in the process is an arduous task, and one that is the focus of IBM MQ, an enterprise-grade messaging solution that has been on the market for over 25 years.
Data observability is a relatively new discipline in the fields of data engineering and data management. While many are familiar with the longstanding concepts of observability and monitoring in enterprise IT networks and infrastructure, data observability has only really come into the spotlight in the last two years. However, it has managed to turn a lot of heads in that short time.
Modern AIOps doesn’t just fix outages — it prevents them – Is your business one accidental click away from a major outage? We saw it happen with Atlassian earlier this year. You may already have an incident management strategy and monitoring, but is it adjusted for the ever-changing IT infrastructure and application architectures? Putting appropriate protocols in place ensures that one human code push can’t shut down an entire system for three weeks.
Hybrid cloud has become a popular computing model in recent times. Find out all you need to know, including its features, pros and cons As computing needs evolve, enterprises continuously find it difficult to scale their business offerings on private or on-premises computing environments. That’s why there are third-party or public cloud providers to enable businesses to carry out larger computational workloads.
Asynchronous communication between various CX applications has long been made possible by enterprise messaging solutions like IBM MQ and – more recently – Apache Kafka. Developers might assume that these two technologies are interchangeable. However, once they scrape the surface, critical differences between IBM MQ and Apache Kafka come to light.
