Handling real-time data at scale? Apache Kafka is likely at the heart of your system. It’s robust, fast, and highly reliable. But as Kafka clusters grow, so does the complexity of maintaining balanced workloads across brokers and partitions.

Handling real-time data at scale? Apache Kafka is likely at the heart of your system. It’s robust, fast, and highly reliable. But as Kafka clusters grow, so does the complexity of maintaining balanced workloads across brokers and partitions.
Apache Kafka plays a critical role in financial services by providing a robust, scalable, and real-time data streaming platform. The financial industry relies heavily on processing vast amounts of data quickly and reliably, and Kafka's capabilities are well-suited for this environment.
Kafka can ingest real-time traffic data, vehicle positions, and road conditions, process this data using Kafka Streams, and then publish optimized routes back to the vehicles. If traffic conditions change, Kafka can instantly process the new data and update the routes accordingly.
Apache Kafka can be an essential component in optimizing fleet tracking by providing a scalable, reliable, and real-time data processing platform.
Introduction
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.
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.
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.
Introduction
In this era, machine learning is important. Machine learning helps in business Management operations and understanding customer behaviors. It also helps in the development of new products.
Every leading company is shifting towards machine learning.
Apache Kafka is an open-source software platform for processing streams of events. Developed for the streaming and archiving of data, Kafka is now supported by a large ecosystem of third-party applications. Developed by the founders of Confluent, Kafka was initially created in 2007 while the three creators were at LinkedIn.
OpenTelemetry is a collection of tools and APIs for collecting, processing, and exporting telemetry data from software. It is used to instrument applications for performance monitoring, logging, tracking, tracing, and other observability purposes.
What is Telemetry?
When IBM MQ 9.3 was released last month, as discussed here, it was notable (and noted) that this was just for distributed platforms and that news of the mainframe and the appliance would follow.