Sep 23–26, 2019

Schedule: Streaming and IoT sessions

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9:00am12:30pm Tuesday, September 24, 2019
Location: 1E 09
Arun Kejariwal (Independent), Karthik Ramasamy (Streamlio), Anurag Khandelwal (RISELab, UC Berkeley)
Arun Kejariwal, Karthik Ramasamy, and Anurag Khandelwal walk you through the landscape of streaming systems and examine the inception and growth of the serverless paradigm. You'll take a deep dive into Apache Pulsar, which provides native serverless support in the form of Pulsar functions and get a bird’s-eye view of the application domains where you can leverage Pulsar functions. Read more.
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9:00am12:30pm Tuesday, September 24, 2019
Location: 1E 11
Purnima Reddy Kuchikulla (Cloudera), Timothy Spann (Cloudera), Abdelkrim Hadjidj (Cloudera), Andre Araujo (Cloudera)
There are too many edge devices and agents, and you need to control and manage them. Purnima Reddy Kuchikulla, Timothy Spann, and Abdelkrim Hadjidj walk you through handling the difficulty in collecting real-time data and the trouble with updating a specific set of agents with edge applications. Get your hands dirty with Cloudera Edge Management (CEM), which addresses these challenges with ease. Read more.
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1:30pm5:00pm Tuesday, September 24, 2019
Location: 1E 14
Purnima Reddy Kuchikulla (Cloudera), Dan Chaffelson (Cloudera)
Kafka is omnipresent and the backbone of streaming analytics applications and data lakes. The challenge is understanding what's going on overall in the Kafka cluster, including performance, issues, and message flows. Purnima Reddy Kuchikulla and Dan Chaffelson walk you through a hands-on experience to visualize the entire Kafka environment end-to-end and simplify Kafka operations via SMM. Read more.
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2:05pm2:45pm Wednesday, September 25, 2019
Location: 1A 15/16
Stephan Ewen (Ververica), Aljoscha Krettek (Ververica)
Stephan Ewen and Aljoscha Krettek detail how stream processing is becoming a "grand unifying paradigm" for data processing and the newest developments in Apache Flink to support this trend: new cross-batch-streaming machine learning algorithms, state-of-the-art batch performance, and new building blocks for data-driven applications and application consistency. Read more.
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4:35pm5:15pm Wednesday, September 25, 2019
Location: 1A 15/16
James Terwilliger (Microsoft Corporation), Badrish Chandramouli (Microsoft Research), Jonathan Goldstein (Microsoft Research)
Trill has been open-sourced, making the streaming engine behind services like the multi-billion-dollar Bing Ads platform available for all to use and extend. We give a brief history of streaming data at Microsoft and lessons learned. We then demonstrate how its API can power complex application logic, and the performance that gives the engine its name: a trillion events per day per node. Read more.
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11:20am12:00pm Thursday, September 26, 2019
Location: 1A 21
Stavros Kontopoulos (Lightbend), Debasish Ghosh (Lightbend )
In this talk, we discuss online machine learning algorithm choices for streaming applications. We motive the discussion with resource constrained use cases like IoT and personalization. We cover Hoeffding Adaptive Trees, classic sketch data structures, and drift detection algorithms, all the way from implementation to production deployment, describing the pros and cons of using each of them. Read more.
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2:05pm2:45pm Thursday, September 26, 2019
Location: 1A 15/16
Karthik Ramasamy (Streamlio), Anand Madhavan (Narvar)
Narvar provides next generation post transaction experience for over 500+ retailers. This talk explores the journey of how Narvar moving away from using a slew of technologies for their platform and consolidating their use cases using Apache Pulsar. Read more.
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3:45pm4:25pm Thursday, September 26, 2019
Location: 1A 15/16
Jonghyok Lee (SK Telecom), Chon Yong Lee (SK Telecom)
Architecture and lessons learned from development of T-CORE, SK Telecom’s monitoring and service analytics platform, which collects system and application data from several thousand servers and applications and provides 3D visualized real-time status of the whole network and services for the operators and analytics platform for data scientists, engineers and developers. Read more.

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