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Lessons Learned Developing and Managing High Volume Apache Spark Pipelines in Production

Quby is the creator and provider of Toon, a leading European smart home platform and as a data driven company, Quby uses machine learning algorithms to generate actionable insights for it's end users.They developed data driven services to ensure that users do not needlessly waste energy and can receive real-time alerts about problems with their heating system. In this talk Erni Durdevic a Machine Learning Engineer at Quby describes their journey of productionizing data science algorithms.


AI and Machine Learning for the Connected Home

Quby is the creator and provider of Toon, a leading European smart home platform. As a data driven company, Quby uses AI and machine learning to generate actionable insights for their users. Using the data they collect via IoT devices Quby has introduced multiple data driven services, including an energy waste checker and a boiler monitoring service. In this presentation Stephen Galsworthy the Head of Data Science at Qubywill describes how AI and machine learning are implemented on the Toon platform, and highlights multiple AI use cases relating to the connected home. He dicusses how Deep Learning algorithms are used to detect inefficient appliances from electricity meter data and how streaming algorithms allow users to be alerted to anomalies with their heating systems in near real-time. Stephen also shares the experiences from the Data Science and Data Engineering teams at Quby with bringing data science algorithms from R&D to production and the lessons learned in offering multiple data driven services to hundreds of thousands of users on a daily basis.


Transforming Devon’s Data Pipeline with an Open Source Data Hub—Built on Databricks

Learn how Devon moved from a traditional reporting and data warehouse approach to a modern data lake. Through a visionary program, driven by Databricks, Devon has begun a transformation of how it consumes data and enables engineers, analysts, and IT developers to deliver data driven solutions along all levels of the data analytics spectrum.


Scaling Geographic Analytics with Azure Databricks and Spark GraphX

Devon Energy has developed a solution that analyzes subsurface attributes and readings in order to predict the drill’s position. This solution which is built on Azure Databricks, scales to over a thousand cores in order to process a full well in one to two hours and uses graph and statistics libraries to help maximize oil and gas production.


Saving Energy with Apache Spark and Toon

Toon, a leading European smart thermostat and energy display, enables users to control and monitor gas and electricity consumption in their homes. Using the energy data they collect from over 400,000 homes they have developed a new Energy Waste Checker app to give actionable advice to end users to ensure that they do not needlessly waste energy. In this talk  Stephen Galsworthy, the Head of Data Science at Toon, describes how their novel disaggregation algorithms are implemented in Spark and shows how Toon uses cloud-based big data processing to offer data driven services to hundreds of thousands of users.


Billions of Rows Transformed in Record Time Using Matillion ETL for Amazon Redshift

GE Power & Water develops advanced technologies to help solve some of the world’s most complex challenges related to water availability and quality. They had amassed billions of rows of data on on-premises databases, but decided to migrate some of their core big data projects to the AWS-Cloud, specifically AWS-Redshift.