Raw data from IoT devices, like GPS trackers or electronic logging devices (ELDs), often lacks meaning on its own. However, if combined with information from other business systems, such as inventory management or customer relationship management (CRM), this data can now provide a richer, more complete picture for more effective decision-making. For example, combining GPS data with inventory levels can optimize logistics and delivery routes.

In today's data-driven world, staying ahead means acting on the most up-to-date information. That's where change data capture (CDC) comes in. CDC is a design pattern that tracks your database tables, capturing every row-level insert, update, and delete as it happens. This real-time monitoring allows downstream systems to react to changes instantly, without batch-based updates and resource-intensive full scans.

We are proud to announce the release of Apache Kafka 3.9.0. This is a major release, the final one in the 3.x line. This will also be the final major release to feature the deprecated Apache ZooKeeper mode. Starting in 4.0 and later, Kafka will always run without ZooKeeper.

A year in at Confluent, Product Manager Surabhi Singh has learned a lot about data streaming—and even more about herself. In this fast-paced environment, Surabhi is highly motivated and committed to her work strategically planning, coordinating, and delivering product improvements for customers whose business operations depend on Confluent Platform.

The headless data architecture is the formalization of a data access layer at the center of your organization. Encompassing both streams and tables, it provides consistent data access for both operational and analytical use cases. Streams provide low-latency capabilities to enable timely reactions to events, while tables provide higher-latency but extremely batch-efficient querying capabilities. You simply choose the most relevant processing head for your requirements and plug it into the data.

The headless data architecture is an organic emergence of the separation of data storage, management, optimization, and access from the services that write, process, and query it. With this architecture, you can manage your data from a single logical location, including permissions, schema evolution, and table optimizations. And, to top it off, it makes regulatory compliance a lot simpler, because your data resides in one place, instead of being copied around to every processing engine that needs it.

Welcome back to the third installment of our blog series where we’re diving into the beautiful black box that is Apache Kafka to better understand how we interact with the cluster through producer and consumer clients. Earlier in the series, we took a look at the Kafka producer to see how the client works before following a produce request as it’s processed by the cluster.