Sep 23–26, 2019
Schedule: Streaming and IoT sessions
9:00am–12:30pm Tuesday, September 24, 2019
Location: 1E 10
Viktor Gamov (Confluent)
Building stream processing applications is certainly one of the hot topics in the IT community. But if you've ever thought you needed to be a programmer to do stream processing and build stream processing data pipelines, think again. Viktor Gamov explores KSQL, the stream processing query engine built on top of Apache Kafka.
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9:00am–12:30pm Tuesday, September 24, 2019
Location: 1E 09
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.
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9:00am–12:30pm Tuesday, September 24, 2019
Location: 1E 11
Purnima Reddy Kuchikulla (Cloudera),
Timothy Spann (Cloudera),
Abdelkrim Hadjidj (Cloudera),
Andre Araujo (Cloudera),
Hemanth Yamijala (Cloudera)
There are too many edge devices and agents, and you need to control and manage them. Purnima Reddy Kuchikulla, Timothy Spann, Abdelkrim Hadjidj, and Andre Araujo 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 CEM, which addresses these challenges with ease.
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1:30pm–5:00pm Tuesday, September 24, 2019
Location: 1E 11
Go hands-on with Sophie Watson and William Benton to examine data structures that let you answer interesting queries about massive datasets in fixed amounts of space and constant time. This seems like magic, but they'll explain the key trick that makes it possible and show you how to use these structures for real-world machine learning and data engineering applications.
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1:30pm–5:00pm Tuesday, September 24, 2019
Location: 1E 14
Purnima Reddy Kuchikulla (Cloudera),
Dan Chaffelson (Cloudera),
Attila Kanto (Cloudera),
Tony Wu (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.
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11:20am–12:00pm Wednesday, September 25, 2019
Location: 1A 15/16
Navinder Pal Singh Brar (Walmart Labs)
Each week 275 million people shop at Walmart, generating interaction and transaction data. Navinder Pal Singh Brar explains how the customer backbone team enables extraction, transformation, and storage of customer data to be served to other teams. At 5 billion events per day, the Kafka Streams cluster processes events from various channels and maintains a uniform identity of a customer.
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1:15pm–1:55pm Wednesday, September 25, 2019
Location: 1E 12/13
Geotab is a world-leading asset-tracking company with millions of vehicles under service every day. Felipe Hoffa and Bob Bradley examine the challenges and solutions to create an ML- and geographic information system- (GI)S enabled petabyte-scale data warehouse leveraging Google Cloud. And they dive into the process to publish open, how you can access it, and how cities are using it.
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2:55pm–3:35pm Wednesday, September 25, 2019
Location: 1E 12/13
Tim McKenzie (Pitney Bowes)
Tim McKenzie examines why planning 5G network rollout and associated services requires a good understanding of location-based data. Accurate addressing and linking consumers to property or points of interest allows data enrichment with attributes, demographics and social data. Companies use location to organize and analyze network and customer data to understand where to target new services.
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2:55pm–3:35pm Wednesday, September 25, 2019
Location: 1A 15/16
As a fintech company of China Telecom with half of a billion registered users and 41 million monthly active users, risk control decision deployment has been critical to its success. Weisheng Xie and Jia Zhai explore how the company leverages Apache Pulsar to boost the efficiency of its risk control decision development for combating financial frauds of over 50 million transactions a day.
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4:35pm–5:15pm 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 Bing Ads platform available for all to use and extend. James Terwilliger, Badrish Chandramouli, and Jonathan Goldstein dive into the history of and insights from streaming data at Microsoft. They demonstrate how its API can power complex application logic and the performance that gives the engine its name.
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5:25pm–6:05pm Wednesday, September 25, 2019
Location: 1A 15/16
Bas Geerdink (Aizonic)
Streaming analytics (or fast data processing) is the field of making predictions based on real-time data. Bas Geerdink presents a fast data architecture that covers many use cases that follow a "pipes and filters" pattern. This architecture can be used to create enterprise-grade solutions with a diversity of technology options. The stack is Kafka, Ignite, and Spark Structured Streaming (KISSS).
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11:20am–12:00pm Thursday, September 26, 2019
Location: 1A 23/24
Michael Freedman (TimescaleDB | Princeton University)
Leveraging polyglot solutions for your time series data can lead to issues including engineering complexity, operational challenges, and even referential integrity concerns. Michael Freedman explains why, by re-engineering PostgreSQL to serve as a general data platform, your high-volume time series workloads will be better streamlined, resulting in more actionable data and greater ease of use.
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11:20am–12:00pm Thursday, September 26, 2019
Location: 1A 21/22
Stavros Kontopoulos and Debasish Ghosh explore online machine learning algorithm choices for streaming applications, especially those with resource-constrained use cases like IoT and personalization. They dive into Hoeffding Adaptive Trees, classic sketch data structures, and drift detection algorithms from implementation to production deployment, describing the pros and cons of each of them.
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2:05pm–2:45pm Thursday, September 26, 2019
Location: 1A 06/07
Ryan Foltz (Exabeam)
Unmanaged and foreign devices in the corporate networks pose a security risk, and the first step toward reducing this risk is the ability to identify them. Ryan Foltz walks you through a comprehensive device management machine learning model based on deep learning that performs anomaly detection based on only device names to flag devices that do not follow naming structures.
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2:05pm–2:45pm Thursday, September 26, 2019
Location: 1A 03
Stephan Ewen (Ververica)
Stephan Ewen details 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.
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2:05pm–2:45pm Thursday, September 26, 2019
Location: 3B - Expo Hall
Heitor Murilo Gomes and Albert Bifet introduce you to a machine learning pipeline for streaming data using the streamDM framework. You'll also learn how to use streamDM for supervised and unsupervised learning tasks, see examples of online preprocessing methods, and discover how to expand the framework by adding new learning algorithms or preprocessing methods.
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2:05pm–2:45pm Thursday, September 26, 2019
Location: 1A 15/16
Narvar provides next-generation posttransaction experience for over 500 retailers. Karthik Ramasamy and Anand Madhavan take you on the journey of how Narvar moved away from using a slew of technologies for their platform and consolidated its use cases using Apache Pulsar.
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3:45pm–4:25pm Thursday, September 26, 2019
Location: 1A 15/16
Jonghyok Lee Chon Yong Lee discuss T-CORE, SK Telecom’s monitoring and service analytics platform, which collects system and application data from several thousand servers and applications and provides a 3D visualization of the real-time status of the whole network. Join in to hear lessons learned during development.
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4:35pm–5:15pm Thursday, September 26, 2019
Location: 1E 10/11
Dean Wampler (Anyscale)
Dean Wampler dives into how (and why) to integrate ML into production streaming data pipelines and to serve results quickly; how to bridge data science and production environments with different tools, techniques, and requirements; how to build reliable and scalable long-running services; and how to update ML models without downtime.
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