Smart cars will quickly become a standard of life

The recent advancements in data fabric and data streaming technologies, are complementing the advancements in image recognition and artificial intelligence. The combination of edge computing with centralized intelligent DataOps systems are breaking the barriers to entry and assist in the successful delivery of ever more ambitious projects.


Why Lenses

  • You can now monitor in real-time every single event generated from connected systems
  • Analyze, route, enrich data points with geo-location context awareness
  • Faster time to production

British Gas

IoT for Smart Homes and trillions of messages from Kafka to Elasticsearch.

Driving billions of messages per day through multiple processing pipelines requires a significant amount of processing.

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Connecting the IoT

Millions of new devices join the ranks of the Internet of Things (IoT), ranging from DVRs and home thermostats to embedded factory controllers. We cover all the important IoT protocols, MQTT and COAP, and offer the Kafka Connect sources and sinks to store your for IoT generated data.

MQTT

MQTT stands for MQ Telemetry Transport. It is a lightweight messaging protocol, designed for embedded hardware, low-power or limited-network applications and microcontrollers with limited RAM and/or CPU. It is a protocol that drives the IoT expansion.

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Coap

The Constrained Application Protocol (CoAP) is a specialized web transfer protocol for use with constrained nodes and constrained networks in the Internet of Things. The protocol is designed for machine-to-machine (M2M) applications such as smart energy and building automation.

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InfluxDB

InfluxDB is used as a data store for any use case involving large amounts of timestamped data, including DevOps monitoring, application metrics, IoT sensor data, and real-time analytics. Conserve space on your machine by configuring InfluxDB to keep data for a defined length of time, automatically expiring & deleting any unwanted data from the system

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Kudu

Kudu is a storage for fast analytics on fast data—providing a combination of fast inserts and updates alongside efficient columnar scans to enable multiple real-time analytic workloads across a single storage layer."

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