Mar 15–18, 2020

Schedule: Streaming Data sessions

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11:00am11:40am Tuesday, March 17, 2020
Location: LL20D
Navinder Pal Singh Brar (Walmart Labs)
One of the major use cases for stream processing is real-time fraud detection. Ecommerce has to deal with frauds on a wider scale as more and more companies are trying to provide customers with incentives such as free shipping by moving on to subscription-based models. Navinder Pal Singh Brar dives into the architecture, problems faced, and lessons from building such a pipeline. Read more.
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11:50am12:30pm Tuesday, March 17, 2020
Location: LL20D
Secondary topics:  Streaming and IoT
Teresa Tung (Accenture), William Gatehouse (Accenture)
The digital twin presents a problem of data and models at scale—how to mobilize IT and OT data, AI, and engineering models that work across lines of business and even across partners. Teresa Tung and William Gatehouse share their experience of implementing digital twins use cases that combine IoT, AI models, engineering models, and domain context. Read more.
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1:45pm3:15pm Tuesday, March 17, 2020
Location: 230 A
Austin Bennett (Sling Media)
Austin Bennett offers hands-on training with the Apache Beam programming model. Beam is an open-source unified model for Batch and Stream data processing that runs on execution engines like Google Cloud Dataflow, Apache Flink, and Apache Spark. Read more.
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4:15pm4:55pm Tuesday, March 17, 2020
Location: LL21 F
Secondary topics:  Streaming and IoT
Dave Nielsen (Redis Labs)
Redis Streams enables you to collect data in a time series format while matching the data processing rate of your continuous application. Apache Spark’s Structured Streaming API enables real-time decision making for your continuous data. Dave Nielsen demonstrates how to integrate open source Redis with Apache Spark’s Structured Streaming API using the Spark-Redis library. Read more.
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5:05pm5:45pm Tuesday, March 17, 2020
Location: LL21 F
Balaji Varadarajan (Uber), Vinoth Chandar (Apache Hudi)
Batch processing can benefit immensely from adopting some techniques from the streaming processing world. Balaji Varadarajan and Vinoth Chandar share how Apache Hudi (incubating), an open source project created at Uber and currently incubating with the ASF, can bridge this gap and enable more productive, efficient batch data engineering. Read more.
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11:00am11:40am Wednesday, March 18, 2020
Location: LL20C
Kai Wähner (Confluent)
Apache Kafka became the de facto standard for microservice architectures, which also introduced new challenges. Kai Wähner explores the problems of distributed microservices communication and how Kafka and a service mesh like Istio address them. You'll learn approaches for combining them to build a reliable and scalable microservice architecture with decoupled and secure microservices. Read more.
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11:50am12:30pm Wednesday, March 18, 2020
Location: LL20C
Jay Smith (Google), Remy Welch (Google Cloud)
Data is a valuable resource, but collecting and analyzing the data can be challenging. And the cost of resource allocation often prohibits the speed at which you can analyze the data. Jay Smith and Remy Welch break down how serverless architecture can improve the portability and scalability of streaming event-driven Apache Spark jobs and perform ETL tasks using serverless frameworks. Read more.
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1:45pm3:15pm Wednesday, March 18, 2020
Location: 210 C/G
Chris Bartholomew (Kafkaesque)
Chris Bartholomew walks you through the architecture and important concepts of Apache Pulsar. You'll set up a local Apache Pulsar environment and use the Python API to do publish/subscribe (pub/sub) message streaming, fanning out messages to multiple consumers. Read more.
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4:15pm4:55pm Wednesday, March 18, 2020
Location: 230 A
Sijie Guo (StreamNative), Yong Zhang (StreamNative)
Sijie Guo and Yong Zhang lead a deep dive into the details of Pulsar transaction and how you can use it in Pulsar Functions and other processing engines to achieve transactional event streaming. Read more.

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