Ultralight Data Movement for IoT with SDC Edge

Wednesday, April 18
2:00 PM - 2:40 PM
Room IV

Edge computing and the Internet of Things bring great promise, but often just getting data from the edge requires moving mountains. Let's learn how to make edge data ingestion and analytics easier using StreamSets Data Collector edge, an ultralight, platform independent and small-footprint Open Source solution written in Go for streaming data from resource-constrained sensors and personal devices (like medical equipment or smartphones) to Apache Kafka, Amazon Kinesis and many others. This talk includes an overview of the SDC Edge main features, supported protocols and available processors for data transformation, insights on how it solves some challenges of traditional approaches to data ingestion, pipeline design basics, a walk-through some practical applications (Android devices and Raspberry Pi) and its integration with other technologies such as Streamsets Data Collector, Apache Kafka, Apache Hadoop, InfluxDB and Grafana. The goal here is to make attendees ready to quickly become IoT data intake and SDC Edge Ninjas.

Presentation Video


Guglielmo Iozzia
Big Data Delivery Lead
Optum (UnitedHealth)
I am currently a Big Data Delivery Lead at Optum (UnitedHealth Group) and based in Dublin (Ireland). Me and my teams deal with projects in the PI (fraud, waste and abuse, claims processing) and the healthcare space. I worked previously at IBM Ireland, where I switched my career path from Test Automation to Analytics and Machine Learning. I am passionate about coding, Big Data, AI/ML/DL, test automation, Open Source, DevOps and cooking (home made pizza is my speciality). I share my tech thoughts through my blog (http://googlielmo.blogspot.ie/) and DZone (https://dzone.com/users/2532948/virtualramblas.html) where I am a Golden Member. During 2018 I have presentend to several international conferences such as DataWorks Summit Berlin, Google I/O Extended, Predictive Analytics World for Industry 4.0 and many others. My first book "Hands-on Deep Learning with Apache Spark" (https://tinyurl.com/y7d98s64) is going to be released in December 2018.