Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Schedule: Streaming and IoT sessions

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13:3017:00 Tuesday, 30 April 2019
Location: Capital Suite 10
Boris Lublinsky (Lightbend), Dean Wampler (Lightbend)
Average rating: ****.
(4.20, 5 ratings)
Boris Lublinsky and Dean Wampler walk you through using ML in streaming data pipelines and doing periodic model retraining and low-latency scoring in live streams. You'll explore using Kafka as a data backplane, the pros and cons of microservices versus systems like Spark and Flink, tips for TensorFlow and SparkML, performance considerations, model metadata tracking, and other techniques. Read more.
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13:3017:00 Tuesday, 30 April 2019
Location: S11 A
Arun Kejariwal (Independent), Karthik Ramasamy (Streamlio), Ivan Kelly (Streamlio)
Average rating: ***..
(3.00, 10 ratings)
Many industry segments have been grappling with fast data (high-volume, high-velocity data). Arun Kejariwal and Karthik Ramasamy walk you through the state-of-the-art systems for each stage of an end-to-end data processing pipeline—messaging, compute, and storage—for real-time data and algorithms to extract insights (e.g., heavy hitters and quantiles) from data streams. Read more.
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12:0512:45 Wednesday, 1 May 2019
Location: Expo Hall 2 (Capital Hall N24)
Ted Dunning (MapR)
Average rating: ****.
(4.67, 6 ratings)
As a community, we have been pushing streaming architectures, particularly microservices, for several years now. But what are the results in the field? Ted Dunning shares several (anonymized) case histories, describing the good, the bad, and the ugly. In particular, Ted covers how several teams who were new to big data fared by skipping MapReduce and jumping straight into streaming. Read more.
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14:5515:35 Wednesday, 1 May 2019
Location: Expo Hall 2 (Capital Hall N24)
Geir Engdahl (Cognite), Daniel Bergqvist (Google)
Average rating: ****.
(4.00, 2 ratings)
Geir Engdahl and Daniel Bergqvist explain how Cognite is developing IIoT smart maintenance systems that can process 10M samples a second from thousands of sensors. You'll explore an architecture designed for high performance, robust streaming sensor data ingest, and cost-effective storage of large volumes of time series data as well as best practices learned along the way. Read more.
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11:1511:55 Thursday, 2 May 2019
Location: Expo Hall 2 (Capital Hall N24)
Thomas Weise (Lyft)
Average rating: ****.
(4.50, 14 ratings)
Fast data and stream processing are essential for making Lyft rides a good experience for passengers and drivers. Lyft's systems need to track and react to event streams in real time to update locations, compute routes and estimates, balance prices, and more. Thomas Weise offers an overview of the streaming platform that powers these use cases. Read more.