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Building Private Wireless Networks
Utilize data streaming in private wireless networks to enhance their capabilities, efficiently transmit vast amounts of IoT data in real time, securely manage devices, and analyze data to optimize performance.
Stream and Process IoT Data at Scale
Confluent’s data streaming platform facilitates IoT communication by serving as a robust backbone that ensures reliable, real-time, and scalable data transfer between IoT devices, applications, and backend systems. It helps streamline data integration, processing, and analytics in IoT ecosystems, making it a valuable tool for developers and organizations.
Confluent helps wireless companies harness and analyze real-time data to be better positioned to thrive in a highly competitive industry.
Improve network performance and scale seamlessly with Confluent’s cloud-native platform powered by Kora engine.
Reduce costs with a fully managed Apache Kafka, connectors, Flink, and more.
Protect IoT data, prevent unauthorized access, and ensure data privacy with robust security features, including encryption, authentication, authorization, and audit logging.
Unlock new business opportunities by building new products & services that use real-time data.
Build with Confluent
This use case leverages the following building blocks in Confluent Cloud:
Reference Architecture
Confluent offers a variety of pre-built, fully managed connectors that facilitate the integration of Kafka with IoT devices and protocols, enabling seamless real-time data exchange.
Confluent’s ability to handle real-time data streams makes it ideal for IoT applications that require immediate processing of sensor data. Leverage stream processing to analyze IoT data in real time for actionable insights and rapid decision-making.
Stream Governance ensures data quality, security, and scalable data compatibility between producers and consumers. Data Portal makes it easy for teams and organizations to share, discover, and reuse data products.
Bidirectional Cluster Linking enables better disaster recovery and active-active architectures, with data and metadata flowing bidirectionally between two or more clusters.