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.
Apache Kafka is the de facto standard for event streaming use cases across industries. Many use cases can be applied to the aviation industry, too. Concepts like payment, customer experience, and manufacturing differ in detail. But in the end, it is about integrating systems and processing data in real-time at scale. For instance, omnichannel retail with Apache Kafka applies to airline, airports, global distribution systems (GDS), and other aviation industry sectors.
The Go standard library has fantastic support for making HTTP requests. However, sometimes, a request needs to be modified or an action needs to be taken upon response (eg. logging a response). In many cases, adding these logs to the every request would involve a lot of duplicate code. In other cases accessing the client might not be possible because of restricted access in a different package or third party library. Thus, I introduce RoundTrippers.
Service automation is the process of automating processes, events, tasks, and business functions. It offers multidimensional visibility of the business, which helps you streamline the business process. It integrates the domain functionality tools with various layers of automation within a unified interface or workflows.
Spending two decades in the middleware field has given me deep insight into the evolution of this technology domain. I began my career as a software engineer in a platform group, building reusable components using technologies like object linking and embedding (OLE), the distributed component object model (DCOM) and common object request broker architecture (CORBA).
Maximizing the value of streaming data requires carefully navigating operational tradeoffs when developing and managing cloud native applications. Organizations that are rapidly producing and processing high volumes of data — like Netflix, Salesforce, Shopify and even the United States Postal Service (USPS), are constantly applying and testing new methods to manage the complexity of data streaming in the cloud.
