Querying databases comes with costs—wall clock time, CPU usage, memory consumption, and potentially actual dollars. As your application scales, optimizing these costs becomes crucial. Materialized views offer a powerful solution by creating a pre-computed, optimized data representation. Imagine a retail scenario with separate customer and product tables. Typically, retrieving product details for a customer's purchase requires cross-referencing both tables.

Today, Confluent, the data streaming pioneer, is excited to announce its entrance into MongoDB’s new AI Applications Program (MAAP). MAAP is designed to help organizations rapidly build and deploy modern generative AI (GenAI) applications at enterprise scale.

Recap: This is the last part of our four chapters: It’s been a long time coming, but we’ve finally arrived at the fourth and final installment of our blog series. In this series, we’ve been peeling back the layers of Apache Kafka to get a deeper understanding of how best to interact with the cluster using producer and consumer clients. At a high level, a fetch request is comprised of two parts: Let’s dive in.

The Connect with Confluent (CwC) Technology Partner Program consistently expands the reach of Confluent’s data streaming platform across an ever-growing landscape of enterprise data systems. In this blog, you’ll meet the latest program entrants who have built fully managed integrations with Confluent and discover new ways to leverage real-time data across your business.

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.