Streamline your IoT deployment by turning Kafka into a fully-fledged MQTT broker with Zilla. Enable your MQTT clients to directly consume and produce Kafka data streams without any coding, integration pipelines or dedicated MQTT brokers.
MQTT is a data transport protocol that is the de-facto standard for IoT. It is lightweight, scalable, and resilient, but it is not designed for data integration and processing — Apache Kafka though, is! Zilla brings these two technologies together enabling business value to be extracted in near real-time from large volumes of IoT data.
Apache Kafka can distribute large volumes of data across a range of applications and services. With Kafka, IoT data can be made readily available to microservices, databases and analytics platforms.
Apache Kafka has a rich ecosystem of stream processing solutions. When IoT data lands in Kafka, it can be processed, enriched, and propagated through data streaming pipelines powered by Kafka Streams, ksqlDB, Apache Flink, etc.
Ensuring safe and proper use of business data is critical for upholding consistent security policies and complying with regulations. Publishing IoT data streams into Kafka enables the use of data governance tools and practices designed specifically for streaming.
Kafka can tie together IoT data with both historical and real-time data from across applications and services. This in turn supports a centralized analytics plane that can span an entire deployment.
Apache Kafka can distribute large volumes of data across a range of applications and services. With Kafka, IoT data can be made readily available to microservices, databases and analytics platforms.
Apache Kafka has a rich ecosystem of stream processing solutions. When IoT data lands in Kafka, it can be processed, enriched, and propagated through data streaming pipelines powered by Kafka Streams, ksqlDB, Apache Flink, etc.
Ensuring safe and proper use of business data is critical for upholding consistent security policies and complying with regulations. Publishing IoT data streams into Kafka enables the use of data governance tools and practices designed specifically for streaming.
Kafka can tie together IoT data with both historical and real-time data from across applications and services. This in turn supports a centralized analytics plane that can span an entire deployment.
Zilla is a multi-protocol edge and service proxy that can mediate between MQTT and Kafka wire protocols. When deployed in front of Kafka, Zilla persists MQTT messages and client state across pre-configured Kafka topics. Once theses messages are in Kafka, they become readily available to Kafka clients, consumers, and stream processing pipelines. Zilla works bi-directionally, so data can also be forwarded from Kafka producers back to MQTT clients.
Zilla is stateless, therefore easy to scale. It can be deployed behind a load balancer and run as as a standalone process or as part of a Kubernetes cluster. Zilla also handles connection offloading ensuring that Kafka is never directly exposed to an IoT-scale number of clients.
For observability, Zilla exposes metrics via OpenTelemetry, while message validation is supported via AsyncAPI and Schema Registry.
While different ways exist of interfacing MQTT clients to Kafka, they are either functionally compromised, operationally compex, or both!
