Presented By O'Reilly and Cloudera
Make Data Work
22–23 May 2017: Training
23–25 May 2017: Tutorials & Conference
London, UK

Schedule: Real-time applications sessions

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14:5515:35 Wednesday, 24 May 2017
Location: Capital Suite 8/9
Level: Intermediate
Tristan Stevens (Cloudera)
Average rating: ***..
(3.67, 3 ratings)
Vodafone UK’s new SIEM system relies on Apache Flume and Apache Kafka to ingest over 1 million events per second. Tristan Stevens discusses the architecture, deployment, and performance-tuning techniques that enable the system to perform at IoT-scale on modest hardware and at a very low cost. Read more.
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12:0512:45 Thursday, 25 May 2017
Location: Capital Suite 8/9
Level: Intermediate
Bas Geerdink (ING)
Average rating: ***..
(3.25, 4 ratings)
As a data-driven enterprise, ING is heavily investing in big data, analytics, and stream processing. Bas Geerdink shares three use cases at ING and discusses their respective architectures and technology. All software is currently in production, running with modern tools such as Kafka, Cassandra, Spark, Flink, and H2O.ai. Read more.
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12:0512:45 Thursday, 25 May 2017
Location: Capital Suite 10/11
Level: Beginner
Xueyan Li (Qunar), Yupeng Fu (Alluxio)
Average rating: ***..
(3.00, 1 rating)
Alluxio—the first memory-speed virtual distributed storage system in the world—unifies the data from various under storage systems and presents a global namespace to various computation frameworks. Xueyan Li and Yupeng Fu explore how Alluxio has led to performance improvements averaging a 300x improvement at service peak time on stream processing workloads at Qunar. Read more.
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16:3517:15 Thursday, 25 May 2017
Location: Capital Suite 8/9
Secondary topics:  Deep learning, Streaming
Level: Intermediate
Kamran Yousaf (Redis Labs)
Average rating: ***..
(3.50, 6 ratings)
Kamran Yousaf explains how to substantially accelerate and radically simplify common practices in machine learning, such as running a trained model in production, to meet real-time expectations, using Redis modules that natively store and execute common models generated by Spark ML and TensorFlow algorithms. Read more.