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.

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.

Banking is about financial transactions. These are executed by sending payments and instructions over middleware. If you control the middleware, then you control the business. If the middleware fails, then the company fails. If you can see and analyze the transactions going through the middleware, you can see the business itself. And if you have real-time analytics of that data and it’s automatically actioned, then you can innovate and accelerate the company’s development.

Today’s enterprises can relate to this logic, especially concerning digital transformation projects. They have broken systems that need fixing, but they also spend a lot of effort fixing systems that don’t necessarily need those levels of intervention. Specifically, companies often face a dilemma: digital modernisation requires apps and systems to be upgraded and operate in the new technology norms. Yet those same systems often exist for good reasons.

You can integrate Edge Flow Manager (EFM) with Apache Kafka and forward agent heartbeats to defined Kafka topics. Learn how to perform the integration with Apache Kafka. To integrate EFM with Kafka, you need to configure Kafka and EFM properties. EFM supports the forwarding of agent heartbeats and acknowledges messages exchanged on the C2 protocol between the EFM server and MiNiFi agents.

Both Artificial Intelligence and Machine Learning are complex things. There are so many things to know. These days human life has changed because of AI. So, before understanding the differences, let’s know about different factors. If I have to say the difference in simple words. AI helps us solve various tasks; on the other hand, Machine Learning is the subset of AI’s specific tasks. So, you can say that all Machine Learning is AI, but all AI is not machine learning.

Apache Kafka is a high-throughput, low-latency platform for handling real-time data feeds. Its storage layer is in essence a massively scalable pub/sub message queue designed as a distributed transaction log. It can be used to process streams of data in real-time, building up a commit log of changes. Kafka has strong ordering guarantees that enable it to handle all sorts of dataflow patterns including very low latency messaging and efficient multicast publish / subscribe.

There are a few key differences between distributed tracing and OpenTelemetry. One is that OpenTelemetry offers a more unified approach to instrumentation, while distributed tracing takes a more granular approach. This means that OpenTelemetry can be less time-consuming to set up, but it doesn’t necessarily offer as much visibility into your system as distributed tracing does.