A Community for
Accelerating Analytics at the Edge

Apache Edgent (incubating)

Apache Edgent is a programming model and micro-kernel style runtime that can be embedded in gateways and small footprint edge devices enabling local, real-time, analytics on the continuous streams of data coming from equipment, vehicles, systems, appliances, devices and sensors of all kinds (for example, Raspberry Pis or smart phones). Working in conjunction with centralized analytic systems, Apache Edgent provides efficient and timely analytics across the whole IoT ecosystem: from the center to the edge.

Meetup: Introduction to Apache Edgent at Galvanize

Wednesday, Jan 18 6pm-7:30pm PDT

In this Meetup , we will discuss the basic concepts of data streaming, explore some of the use cases behind Edgent, and compare Edgent to its other streaming counterparts.

Hangout: Apache Edgent on Raspberry Pi

Thursday, May 12 10am-11am PDT

In this Google+ Hangout, we showed you how you can use Edgent to work with real sensor data on a Raspberry Pi. We also demonstrated an application where we implemented a smart sprinkler system using Apache Edgent, Raspberry Pi, Watson IoT Platform and the Streaming Analytics service.

Hangout: Intro to Apache Edgent

Wednesday, April 13 10am-12pm PDT

In this Google+ Hangout, the committers presented a high-level overview of the Apache Edgent project and demonstrated how you can write, monitor and debug your Edgent application. They also showed you how you can get involved and contribute to the Edgent community.

An Open IoT Ecosystem

Apache Edgent is API driven and modular and can be used in conjunction with vendor and open source data and analytics solutions such as Apache Kafka, Apache Spark and Apache Storm.

Why Apache Edgent?

Reduced Communication Costs

Edgent performs real-time analytics on the edge device, separating the interesting from the mundane, so you don’t have to send every sensor reading over a network. If 99% of readings are normal, Edgent detects the 1% anomalies and just transmits those for further processing.

Local and Faster Time to Action

Edgent makes devices more intelligent, enabling them to take immediate action. For example, a connected vehicle running Edgent can adjust traction control based on the weight of the cargo/passengers.

Learning From Related Devices

Edgent enables connected devices to learn from related devices. For example, a truck maneuvering roads in Oregon can adjust based on the data received from trucks operating under similar loads and conditions in Colorado; data such as altitude, cargo, weather and traffic conditions.