Conference Speakers

Stephan Ewen is CTO and co-founder at Ververica where he leads the development of the stream processing platform based on open source Apache Flink. He is also a PMC member and one of the original creators of Apache Flink. Before working on Apache Flink, Stephan worked on in-memory databases, query optimization, and distributed systems. He holds a Ph.D. from the Berlin University of Technology.

Keynote

From Stream Processor to a Unified Data Processing System

Jon has been working with Flink and dAP inside The Trade Desk (TTD) since he met dA at this conference exactly one year ago. Prior to the TTD's Data Pipeline Team, Jon built custom Big Data pipelines and time-series analytic engines inside Microsoft's Shared Data group and Skype's Big Data team.

Keynote

The Trade Desk’s Year with Flink

Dave is an Enterprise Software Architect with over twenty-five years of technical leadership in the telecommunications, financial services, and healthcare domains. Dave’s diverse experience ranges from engineering event-based and rule processing systems at “PaaS” (Platform as a Service) scale to building an autonomous-agent workplace simulation engine. At Comcast, Dave is a Distinguished Architect leading the end-to-end ingest, distributed compute, and machine learning pipeline architectures for Big Data applications supporting Customer Experience and Systems Engineering in both public and private cloud environments.

Keynote

Flink-powered Customer Experience: Scaling from 5 Billion down to One

Kostas Tzoumas is a co-founder and CEO of Ververica, the company founded by the original creators of Apache Flink. Kostas is PMC member of Apache Flink and earned a PhD in Computer Science from Aalborg University with postdoctoral experience at TU Berlin. He is an author of a number of technical papers and blog articles on stream processing and other data science topics.

Keynote

Welcome session

Xiaowei Jiang is a Senior Director at Alibaba. He leads the StreamCompute Platform for AliCloud. The platform provides the real time data processing both internally and externally. Previously, he worked as Tech Lead at Facebook and Principal Engineer at Microsoft SQL Server.

Keynote

From Stream Processor to a Unified Data Processing System

Akshay is a Software Engineer on Lyft’s Adaptive Pricing team, where he works on ML-driven pricing models and the infrastructure behind them. The team prices rides in real-time, to maintain market balance and fairly compensate drivers in times of high demand. Previously, Akshay studied computer science and statistics at the University of Virginia. He’s passionate about building faster, more reactive models that make accurate predictions in a quickly changing market.

Use Case

Streaming your Lyft ride prices

I have worked at John Deere nearly 15 years and have over 20 years experience developing software across a range of platforms and technologies for a variety of consumers. Currently, as a Product Lead in Deere's Data Lake initiatives, I work with streaming and big data processing architectures to allow our customers to view, analyze and manipulate their data, to optimize precision agriculture. Using Apache Flink, along with AWS technologies, our team currently processes over 4 Billion complex, geospatial, records a day.

Use Case

How John Deere uses Flink to process millions of sensor measurements per second

Shuyi Chen is a software engineer at Uber and the TL of Uber’s stream processing platform. He is a committer of both Apache Flink and Apache Calcite. Shuyi has years of experience in storage infrastructure, data infrastructure, and mobile development at both Google and Uber.

Operations

Practical experience running Flink in Production

Elliot is a software engineer at Netflix on the Personalization Infrastructure team. He builds big data systems tailored for machine learning and analytics use cases with a variety of technologies including Scala, Spark, Flink, Kafka, and Cassandra.

Use Case

Real-time Processing with Flink for Machine Learning at Netflix

A data scientist and TensorFlow addict, Robert has a passion for helping developers quickly learn what they need to be productive.  He's used TensorFlow since the very early days and is excited about how it's evolving quickly to become even better than it already is.  Before moving to data science Robert led software engineering teams for both large and small companies, always focusing on clean, elegant solutions to well-defined needs.  In his spare time Robert sails, surfs occasionally, and raises a family.

Use Case

TensorFlow Extended: An end-to-end machine learning platform for TensorFlow

Matt Dailey is a backend software engineer with experience creating and maintaining large-scale distributed systems in both batch processing with Hadoop, and stream processing with Apache Flink. He has been using Apache Flink since Spring 2017.

Use Case

Using Flink to inspect live data as it flows through a data pipeline

Pablo is a Software Engineer from Mexico City. He lives in Seattle and works on Apache Beam and Google Cloud Dataflow. He's worked all across the Beam stack, mainly in the Python and Java SDKs. Before working at Google, he earned a Master's degree at Seoul National University, doing computational linguistics research. His favorite activities are traveling and drinking tea with the locals.

Ecosystem

Apache Beam: Portability in the times of Real Time Streaming

Greg Finch is a Senior Product Manager in the John Deere Intelligent Solutions Group. Working in the data platform team, he helps to define, design, and build systems that process sensor data from machines around the world. He enjoys using advanced technologies to help make powerful machines smarter, more reliable, and more efficient.

Use Case

How John Deere uses Flink to process millions of sensor measurements per second

Luca works as a Software Engineer on the User Location Intelligence team at Yelp. While part of his job involves data mining massive data sets at Yelp, he is also responsible for designing and scaling the backend for analyzing high-volume geospatial data from millions of Yelp’s mobile users. He was previously the main open source contributor to Yelp’s asynchronous client Fido.

Use Case

Realtime Store Visit Predictions at Scale

Sijie Guo is the PMC Chair of Apache BookKeeper and the PMC member of Apache Pulsar. He worked at Twitter before and led the messaging team. Prior to Twitter, he worked on Yahoo! Push notification infrastructure.

Ecosystem

Elastic Data Processing with Apache Flink and Apache Pulsar

Addison Higham a software engineer at Instructure, focused on building out a suite of infrastructure and tools for large scale stream processing to enable real-time learning analytics for student, teachers and administrators.

Technology Deep Dive

Becoming a Smooth Operator: A look at low-level Flink APIs and what they enable

Aljoscha Krettek is a PMC member at Apache Flink and co-founder and software engineer at "Ververica":https://www.ververica.com/. He studied Computer Science at TU Berlin, he has worked at IBM Germany and at the IBM Almaden Research Center in San Jose. In Flink, Aljoscha is mainly working on the Streaming API. The most recent additions to the windowing and state APIs where designed and implemented by him. Aljoscha has spoken at Hadoop Summit, Strata, Flink Forward and several meetups about stream processing and Apache Flink before.

Technology Deep Dive

Towards Flink 2.0: Rethinking the stack and APIs to unify Batch & Streaming

I'm a data scientist at AIStream and frequently work with a variety of big data technologies including Flink. My primary area of focus broadly speaking is making it easier to use deep learning models in real world applications. Part of this includes integrating current deep learning frameworks (in Python) with big data frameworks (in Java) to make it easier to serve models at scale with low-latency.

Ecosystem

Deploying ONNX models on Flink

Pala is an engineer in Data Platform at Airbnb, working on projects revolving around stream processing, analytics engines and storage. Prior to that he was a systems engineer at Trooly (acquired by Airbnb), working on a web crawl system (storage and indexing), and engineer at RocketFuel working on data and ML infrastructure. Before that he worked as an engineer in Microsoft SQL Server team on database manageability for SQL Azure and SQL Server. 

He holds a Bachelors and Masters degree in Computer Science from Cornell University.

Use Case

Building production Flink jobs with Airstream at Airbnb

Snehal Nagmote is Senior Software Engineer on Consolidated Logging Team at Netflix. She focuses on building batch and real-time data pipeline for processing user behavioral data using Flink, Spark, Kafka. Prior to Netflix, she worked on building real-time search indexing system at WalmartLabs using Spark and Cassandra . She has several years of experience of building distributed and large scale data processing systems.l.

Use Case

Massive Scale Data Processing at Netflix using Flink

Roshan is a technical lead at Uber's stream processing platform team (Athena) and looking into problems of stream processing at scale. He was previously at Hortonworks where he architected Storm 2.0's new high performance execution engine and authored Hive's transactional streaming ingest APIs. He is a committer on Flume, Streamline and Storm. He is also author of Castor, an open source C++ library that brings the Logic paradigm to C++.

Technology Deep Dive

Moving from Lambda and Kappa Architectures to Kappa+ at Uber

Piotr Nowojski is a Software Engineer at Ververica. Previously, he was a Software Engineer in Teradata working on Presto – distributed SQL query engine. Before that, he was developing streaming applications at huge scale in Gemius.

Technology Deep Dive

How to Join Two Data Streams?

Pallavi Phadnis is a Senior Software Engineer at Netflix working on Consolidated Logging team. At Netflix, she contributes to the backend systems that process user behavior data to enable analytics and personalization using technologies such as Flink, Spark, Kafka, Hadoop etc. Prior to Netflix, she has several years of experience building large scale, high-performance batch and real-time processing systems in domains such as e-commerce and online advertising at WalmartLabs, eBay and advertising.com. Pallavi has a Master's degree from Carnegie Mellon University, Pittsburgh.

Use Case

Massive Scale Data Processing at Netflix using Flink

Till is a PMC member of Apache Flink and software engineer at Ververica. His main work focuses on enhancing Flink’s scalability as a distributed system. Till studied computer science at TU Berlin, TU Munich and École Polytechnique where he specialized in machine learning and massively parallel dataflow systems.

Technology Deep Dive

Future of Apache Flink Deployments: Containers, Kubernetes and More

Rong Rong is a senior software engineer at Uber’s streaming processing team. He worked on Uber’s SQL-based stream analytics engine AthenaX which is currently powering over 1000+ production real-time data analytics and ML pipelines. Previously Rong held a software and machine learning engineer position in Qualcomm computer vision team.

Operations

Practical experience running Flink in Production

Ray has been with Arctic Wolf Networks for 6 years, joining when the wolves were but pups. Over the years, Ray has worked on a number of services making up the distributed system that backs Arctic Wolf Networks’ CyberSOC service. He currently leads the Detection Automation team, where he’s focused on improving Arctic Wolf Networks’ threat detection capabilities. Prior to Arctic Wolf Networks, Ray worked at Blue Coat Systems (since acquired by Symantec) and Sybase (since acquired by SAP).

Use Case

Hunting for Attack Chains in Event Streams

Prem Santosh is a Software Engineer at Yelp who enjoys working on problems related to scaling applications and the theoretical side of distributed consensus. He spends his spare time cooking, skiing and playing league of legends. He loves to talk about raft, paxos, what seasonings go best with chicken and how to never get out of the bronze league.

Use Case

Creating millions of user sessions using Complex Event Processing

Ramesh Shanmugam is a Senior Data Platform Engineer at Branch Metrics. At Branch, currently, he is building streaming and batch pipelines at a huge scale using Apache Flink, Spark, and Airflow. He has been creating distributed applications for more than 15 years. Passionate about building data-intensive applications.

Use Case

Scaling a real-time streaming warehouse with Apache Flink, Parquet and Kubernetes

Ning Shi works with the engineering teams that focus on event processing, data storage, and analytics at Klaviyo. Previously he worked on building distributed in-memory database VoltDB. His passionate about building high performance distributed systems.

Use Case

High cardinality data stream processing with large states

Xiaogang is a committer of the Apache Flink project and a senior software engineer at Tencent. His current work is focused on building a platform to facilitate the development of real-time applications at scale. He holds a Ph.D. in computer science from Peking University.

Operations

Developing and operating real-time applications with Oceanus

Vipul works as a tech lead on the Data Streams Connectors team at Yelp, designing, building and maintaining stream processing infrastructure. His current focus is on building data connectors, enabling creation of a data lake from schematized streams, and developing next-gen stream processing infrastructure here at Yelp.

Use Case

Creating millions of user sessions using Complex Event Processing

Currently working as a Software Engineer at Lyft building infrastructure on large scale streaming and batch processing systems. Previously built real time replication systems for Lyft application tables. Also worked on DynamoDB (AWS), building DynamoDB streams, TTL, etc.

Operations

Managing Flink on Kubernetes - FlinkK8sOperator

Aslam Tajwala, VP of Engineering at Cogility Inc. His current work is focused on creating an analytics platform that makes it easy to capture human expertise and reasoning to allow analyzing high-volume, high-velocity data.

Use Case

Analytics for the masses

I am Principal Software Engineer at Intuit part of Identity and Profile Platform group. I am currently working with a team responsible for creating and maintaining streaming platform and applications. My current focus is on building real-time data ingestion and collection frameworks and building next generation graph processing platform here at Intuit.

Use Case

Building Financial Identity Platform using Apache Flink

Aditi is a senior software engineer at Branch, working on developing and scaling their data platform, that processes tens of billion events per day. Prior to Branch, she worked at Yahoo to develop data systems that provide actionable insights and audience targeting from petabytes of data. She has a wide range of experience in the data domain, from stream and batch processing, to resource management, scaling and monitoring.

Use Case

Scaling a real-time streaming warehouse with Apache Flink, Parquet and Kubernetes

Jonathan is an 8 year veteran of the cyber security industry, with a degree in Computational Mathematics and CISSP and GCIH security certifications. His interests lie in the intersection of mathematics and security.

Use Case

Hunting for Attack Chains in Event Streams

Hao is a software engineer on Airbnb’s Data Platform team. He has been leading the development and driving the adoption of real-time stream processing at Airbnb. Before that, he helped building the data and machine learning infrastructure for IBM Watson. He received his PhD from the University of Southern California.

Use Case

Building production Flink jobs with Airstream at Airbnb

Shaoxuan is a senior staff engineer and director of engineering at Alibaba, working on Flink SQL and AI platform. Alibaba runs Blink, a fork of Flink, at very large scale and offers it as a service to internal and cloud customers. Prior to Alibaba, Shaoxuan was a senior software engineer working on social graph and core infrastructure at Facebook. Shaoxuan received his Ph.D degree from UC San Diego. He is an Apache Flink® committer. Shaoxuan and his colleagues are heavily contributing to Apache Flink®.

Technology Deep Dive

Build a Table-centric Apache Flink Ecosystem

Thomas is Software Engineer, Streaming Platform at Lyft. He is also a committer for Apache Flink, PMC member of Apache Apex and Apache Beam and contributor to several more of the ASF ecosystem projects. Thomas has presented stream processing topics at international big data conferences and is the author of the book “Learning Apache Apex”.

Use Case

Streaming your Lyft ride prices

Engineer passionate about making data accessible to everyone. Lead for Flyte (kubernetes native orchestration platform for machine learning and large-scale data analysis) and passionate about Data on kubernetes. Past lead for ETA at Lyft responsible for extracting realtime traffic signals from all Lyft location data, which makes Lyft more efficient. Lead for Block storage at Oracle Baremetal cloud. Lead for Maps and dynamic route planning for logistics @Amazon

Operations

Managing Flink on Kubernetes - FlinkK8sOperator

Fangjin is a co-author of the open source Druid project and a co-founder of Imply, a San Francisco based technology company. Fangjin previously held senior engineering positions at Metamarkets and Cisco. He holds a BASc in Electrical Engineering and a MASc in Computer Engineering from the University of Waterloo, Canada.

Technology Deep Dive

Building a Streaming Analytics Stack with Open Source Technologies

Xuefu Zhang is a long-time open source veteran, worked or working on many projects under Apache Foundation, of which he is also an honored member. About 10 years ago he worked in the Hadoop team at Yahoo where the projects just got started. Later he worked at Cloudera, initiating and leading the development of Hive on Spark project in the communities and across many organizations. Prior to joining Alibaba, he worked at Uber where he promoted Hive on Spark for all Uber's SQL on Hadoop workload and also significantly improved Uber's cluster efficiency.

Ecosystem

Integrate Flink with Hive Ecosystem

David is responsible for training at Ververica, and enjoys teaching users about Apache Flink. He is also a teacher and mentor at Data Science Retreat, where he established the curriculum in big data engineering. In recent years, David has lead teams building analytics pipelines, recommender systems, and visualization products. Earlier, as a research scientist, he applied machine learning techniques to create better user experiences — for toys, learning environments, and logistics optimization.

Training Program

Alexander Fedulov is a Solutions Architect at Ververica. He enjoys helping clients to build robust solutions based on distributed open source technologies. He is passionate about scalability and elegant systems design. When he has time, he likes to dig into the source code of Apache Flink to see how exactly the "magic" happens beyond the API level.

Training Program

Fabian Hueske is a committer and PMC member of the Apache Flink® project and has been contributing to Flink since its earliest days. Fabian is a co-founder of Ververica, a Berlin-based startup devoted to fostering Flink, where he works as a software engineer and contributes to Apache Flink®. He holds a PhD in computer science from TU Berlin and is currently writing a book about “Stream Processing with Apache Flink®”.

Training Program

As a Solution Architect at Ververica, Konstantin helps our clients to solve their business problems with Apache Flink and Ververica Platform. In this role he is also one of the first people our customers turn to if a streaming applications is not performing as expected. Before joining Ververica he worked as a Senior Consultant with TNG Technology Consulting, where he supported their clients mainly in the areas of Distributed Systems and Automation. Konstantin has studied Mathematics and Computer Science at TU Darmstadt specializing in Stochastics and Algorithmics.

Training Program

Nico Kruber is an Apache Flink contributor and works as a Solutions Architect at Ververica. He is very passionate about open source projects and likes to dig deeper into Flink’s core to further improve performance and extend Flink’s capabilities where possible. Before joining Ververica, Nico was working on his PhD in parallel and distributed systems at Zuse-Institute Berlin.

Training Program

Timo Walther is a committer and PMC member of the Apache Flink project. He studied Computer Science at TU Berlin. Alongside his studies, he participated in the Database Systems and Information Management Group there and worked at IBM Germany. Timo works as a software engineer at Ververica. In Flink, he is mainly working on the Table & SQL API.

Training Program