Flink Forward 是由 Apache 官方授权，Apache Flink China社区支持，有来自阿里巴巴，Ververica（Apache Flink 商业母公司）、腾讯、Google、Airbnb以及 Uber 等公司参加的国际型会议。旨在汇集大数据领域一流人才共同探讨新一代大数据计算引擎技术。通过参会不仅可以了解到Flink社区的最新动态和发展计划，还可以了解到国内外一线大厂围绕Flink生态的生产实践经验，是Flink开发者和使用者不可错过的盛会。2019年04月的 Flink Forward 在美国旧金山进行，本次会议议题涵盖 Flink 使用案例、内部原理、Flink 生态系统的增长以及流处理和实时分析等相关主题。详细的 Schedule 可以参见 https://sf-2019.flink-forward.org/。

本文收集到本次会议的 35 个高清视频以及 12 个对应 ppt，关注本博客微信公众号 iteblog_hadoop，并回复 flink201904 获取本次会议的视频和 PPT 下载地址。

如果想及时了解Spark、Hadoop或者Hbase相关的文章，欢迎关注微信公众号：iteblog_hadoop

视频和 PPT 列表

下面标题能够点开的说明有对应的 PPT，其他说明有超清视频。

• From Stream Processor to a Unified Data Processing System
• Flink Powered Customer Experience: Scaling from 5 Billion down to One
• The Trade Desk's Year in Flink
• Managing Flink on Kubernetes - FlinkK8sOperator
• How John Deere uses Flink to process millions of sensor measurements per second
• Building a Streaming Analytics Stack with Open Source Technologies
• Streaming for Enterprises
• High cardinality data stream processing with large states
• Future of Apache Flink Deployments: Containers, Kubernetes and More
• How to Join Two Data Streams?
• TensorFlow Extended: An end-to-end machine learning platform for TensorFlow
• Build a Table-centric Apache Flink Ecosystem
• Building Financial Identity Platform using Apache Flink
• Elastic Data Processing with Apache Flink and Apache Pulsar
• High performance ML library based on Flink
• Building production Flink jobs with Airstream at Airbnb
• Analytics for the masses
• Creating millions of user sessions using Complex Event Processing
• Hunting for Attack Chains in Event Streams
• Deploying ONNX models on Flink
• Integrate Flink with Hive Ecosystem
• Developing and operating real-time applications with Oceanus
• Apache Beam: Portability in the times of Real Time Streaming
• Adventures in Scaling from Zero to 5 Billion Data Points per Day
• Streaming your Lyft Ride Prices
• Practical Experience running Flink in Production
• Scaling a real-time streaming warehouse with Apache Flink, Parquet and Kubernetes
• Massive Scale Data Processing at Netflix using Flink
• Using Flink to inspect live data as it flows through a data pipeline
• Towards Flink 2.0: Rethinking the stack and APIs to unify Batch & Stream
• Moving from Lambda and Kappa Architectures to Kappa+ at Uber
• Build a Table-centric Apache Flink Ecosystem
• Realtime Store Visit Predictions at Scale
• Real-time Processing with Flink for Machine Learning at Netflix
• Becoming a Smooth Operator: A look at low-level Flink APIs and what they enable