Community activity | Apache Flink meetup · Shenzhen station, locking in Flink best practices

Time:2020-11-24

Apache Flink meetup 2020 · Shenzhen station

Exactly! Style! Go! Line!

How to build enterprise data Lake based on Flink + iceberg?
What production environment practices does Hudi on Flink have?
How can the monitoring system based on Flink be more stereoscopic?
AI + Flink for privacy protection?

On September 26, four technical experts from Alibaba, Intel, SF, and Tencent shared with you the latest enterprise application practice of Flink, as well as the new progress in the combination with the popular data lake, digital warehouse and community ecology.

Highlights:

  • Exclusive dry goods sharing, 4 technical experts of first-line large factories share the popular enterprise level production environment application practice such as data lake, digital warehouse, AI, monitoring, etc;
  • The activity forms are diversified, and the online and offline activities are opened synchronously. You can participate in the face-to-face communication of offline meetup in the same city, and you can also watch the live broadcast online in different places, and you can not miss the wonderful contents;
  • Rich surrounding area waiting for you to take, sign up to have a chance to get more exquisite surroundings customized by Flink community!

Scan the code to sign up for more details

Community activity | Apache Flink meetup · Shenzhen station, locking in Flink best practices

Speech theme and guest introduction

Practical application of Hudi on Flink in SF
Cai Shize, head of Shunfeng big data platform

Community activity | Apache Flink meetup · Shenzhen station, locking in Flink best practices

Introduction to the speech:

This paper introduces the real-time data sharing experience of hunk and its products in Shunfeng.

Guest profile:

He is responsible for the construction and productization of Shunfeng big data platform. He has rich practical experience in the field of big data platform, Internet of things and edge computing, and is committed to greatly reducing the threshold of data development and application, and making big data technology a technology that can be used by everyone and can be applied quickly.

Cluster serving for privacy protection with analytics zoo and Flink
Gong Qiyuan, Intel machine learning Engineer

Community activity | Apache Flink meetup · Shenzhen station, locking in Flink best practices

Introduction to the speech:

How to protect user privacy in the process of data application has been a hot topic in the industry and academia in recent years. This lecture will introduce how to implement cluster serving for privacy protection on Intel SGX through analytics zoo and Flink, so as to protect user data, models and prediction results in the process of model serving.

Key points:

  1. Analytics-Zoo & Cluster Serving
  2. Intel SGX & Graphene-SGX
  3. Trusted Cluster Serving with Analytics-Zoo & Flink

Guest profile:

He graduated from Southeast University in 2016. During his doctorate period, he mainly engaged in data privacy related research. He joined Intel in 2017 and engaged in big data and machine learning related work. He is big data + AI open source project analytics zoo(https://github.com/intel-anal…)And big data storage management open source project SSM(https://github.com/Intel-bigd…)Key contributors to the project. At present, he is mainly responsible for analytics zoo privacy preserving machine learning, cluster serving, streaming, openvino, etc.

Application practice of Flink in Tencent video
Dong Lei, head of Tencent video monitoring system

Community activity | Apache Flink meetup · Shenzhen station, locking in Flink best practices

Introduction to the speech:

This sharing mainly introduces how to build a three-dimensional monitoring system through Flink, including dimension index monitoring, call chain and log. Based on the business requirements, it introduces the relevant technologies and solutions used.

Guest profile:

Joined Tencent in 2018 and has more than 8 years of big data processing experience. He has rich practical experience in big data processing, flow computing, machine learning, model operation, etc. After joining Tencent, I served as the director of video playback quality monitoring system, mainly focusing on distributed log system and other work.

Building enterprise real time data Lake based on Flink + iceberg
Hu Zheng (Ziyi) | Alibaba technical expert, Apache HBase PMC

Community activity | Apache Flink meetup · Shenzhen station, locking in Flink best practices

Introduction to the speech:

Apache Flink is a very popular streaming batch unified computing engine in the field of big data, and data lake is a new technology architecture conforming to the development trend of cloud services. So what kind of sparks will Apache Flink and data Lake collide? This sharing mainly includes the following core contents:

  1. Background of data Lake
  2. Introduction to classic business scenarios
  3. Why Apache iceberg
  4. How to realize flow into Lake by Flink + iceberg
  5. Community future planning

Guest profile:

At present, he is mainly responsible for the design and development of Flink data Lake solution, a long-term active contributor of Apache iceberg and Apache Flink project, and the author of HBase principle and practice.

Activity flow and registration

Community activity | Apache Flink meetup · Shenzhen station, locking in Flink best practices

  • Time: 13:00-17:30, September 26
  • Venue: onepiece work, 1st floor, block B, building 4, software industry base, Nanshan District, Shenzhen

If you are curious about Flink’s application in data lake, data warehouse, AI and community ecology, are interested in Flink geek challenge, and want to learn more about dry goods and application practices, please join us. Click the link below to sign up and see you in Shenzhen on September 26!

https://flinkchina.huodongxin…