Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY

Schedule: IoT sessions

11:20am12:00pm Wednesday, September 27, 2017
Mateusz Dymczyk (, Mathieu Dumoulin (McKinsey & Company)
Average rating: ****.
(4.00, 2 ratings)
Mateusz Dymczyk and Mathieu Dumoulin showcase a working, practical, predictive maintenance pipeline in action and explain how they built a state-of-the-art anomaly detection system using big data frameworks like Spark, H2O, TensorFlow, and Kafka on the MapR Converged Data Platform. Read more.
1:15pm1:55pm Wednesday, September 27, 2017
Michael Freedman (TimescaleDB)
Average rating: ****.
(4.50, 4 ratings)
Michael Freedman offers an overview of TimescaleDB, a new scale-out database designed for time series workloads yet open-sourced and engineered up as a plugin to Postgres. Unlike most time series newcomers, TimescaleDB supports full SQL while achieving fast ingest and complex queries. Read more.
2:05pm2:45pm Wednesday, September 27, 2017
Julie Lockner (17 Minds Corporation)
Average rating: ****.
(4.00, 1 rating)
How can we empower individuals with special needs to reach their full potential? Julie Lockner offers an overview of a project to develop collaboration applications that use wearable device data to improve the ability to develop the best possible care and education plans. Join in to learn how real-time IoT data analytics are making this possible. Read more.
5:25pm6:05pm Wednesday, September 27, 2017
Dave Shuman (Cloudera), James Kirkland (Red Hat)
Eclipse IoT is an ecosystem of organizations that are working together to establish an IoT architecture based on open source technologies and standards. Dave Shuman and James Kirkland showcase an end-to-end architecture for the IoT based on open source standards, highlighting Eclipse Kura, an open source stack for gateways and the edge, and Eclipse Kapua, an open source IoT cloud platform. Read more.
11:20am12:00pm Thursday, September 28, 2017
Michael Crutcher (Cloudera), Ryan Lippert (Cloudera)
A long time ago in a data center far, far away, we deployed complex lambda architectures as the backbone of our IoT solutions. Though hard, they enabled collection of real-time sensor data and slightly delayed analytics. Michael Crutcher and Ryan Lippert explain why Apache Kudu, a relational storage layer for fast analytics on fast data, is the key to unlocking the value in IoT data. Read more.
2:05pm2:45pm Thursday, September 28, 2017
Javier Esplugas (DHL Supply Chain), Kevin Parent (Conduce)
DHL has created an IoT initiative for its supply chain warehouse operations. Javier Esplugas and Kevin Parent explain how DHL has gained unprecedented insight—from the most comprehensive global view across all locations to a unique data feed from a single sensor—to see, understand, and act on everything that occurs in its warehouses with immersive operational data visualization. Read more.
2:55pm3:35pm Thursday, September 28, 2017
Alexandra Gunderson (Arundo Analytics)
One of the main challenges when working with industrial data is linking the large amount of data and extracting value. Alexandra Gunderson shares a comprehensive preprocessing methodology that structures and links data from different sources, converting the IIoT analytics process from an unorganized mammoth to one more likely to generate insight. Read more.
4:35pm5:15pm Thursday, September 28, 2017
Lloyd Palum (Vnomics)
Average rating: *****
(5.00, 2 ratings)
A digital twin models a real-world physical asset using mobile data, cloud computing, and machine learning to track chosen characteristics. Lloyd Palum walks you through building a tractor trailer digital twin using Python and TensorFlow. You can then use the example model to track and optimize performance. Read more.
4:35pm5:15pm Thursday, September 28, 2017
Arun Kejariwal (Independent), Francois Orsini (MZ), Dhruv Choudhary (MZ)
Average rating: *****
(5.00, 3 ratings)
Services such as YouTube, Netflix, and Spotify popularized streaming in different industry segments, but these services do not center around live data—best exemplified by sensor data—which will be increasingly important in the future. Arun Kejariwal, Francois Orsini, and Dhruv Choudhary demonstrate how to leverage Satori to collect, discover, and react to live data feeds at ultralow latencies. Read more.