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.
Kafka vs RabbitMQ – A side-by-side comparison of the performance and architectural differences between the two popular open-source messaging systems. As a big data architect or a big data developer, when working with Microservices-based systems, you might often end up in a dilemma whether to use Apache Kafka or RabbitMQ for messaging. Rabbit MQ vs. Kafka – Which one is a better message broker?
You go for a run, and when you’re done, your buddy, adidas Running, will tell you how you did. Maybe you hit a new goal this run or need encouragement to work harder next time. No matter what, behind the scenes there is a dedicated team of engineers relying on the stable foundation of RabbitMQ. Ever since sports tracking apps saw the light of day, it’s almost like a workout without tracking, somehow is less valuable to us. Imagine a run streak on day 1,000 without functioning measurements!
AIOps stands for artificial intelligence for IT Operations. AIOps, is a set of tools and algorithms that gather data from the entire IT environment, including different monitoring systems, log files and other IT data sources. It then analyzes and applies machine learning algorithms to determine the root cause of an incident. This means that instead of having to go through a long troubleshooting process by analyzing log files and manually looking for root causes, AIOps does it for you in minutes.
As defined by Amazon Web Services, DevOps is the integration of cultural concepts, practices, methods, and tools which allow an organization to provide services and applications at high speed: advancing and improving their products at a much faster rate than those using traditional software process for infrastructure management and development. This allows organizations to serve clients more effectively and compete in the market.
Developing a strategy for integrating data across your organization helps ensure that everyone has access to the most up-to-date data in a secure way. This article provides an example of a strategy you can use to develop your own. Despite the global digital acceleration of data use cases, many companies still struggle to be data-driven.
