Introduction to Stream Processing with Apache Flink

Explore the fundamentals of stream processing and Apache Flink in this 56-minute conference talk by Marta Paes Moreira. Dive into the evolution of stream processing from real-time applications to a unified paradigm for distributed data processing. Learn about Flink's flexible APIs, powerful execution model, and its adoption by major companies like Alibaba, Netflix, Uber, and Yelp. Discover key concepts such as state, time, and the building blocks that make Flink a resilient and versatile option for stateful stream processing. Examine Flink's competitive edge over similar frameworks, its API stack, and how it handles distributed systems, fault tolerance, and checkpointing. Gain insights into Flink's applications in streaming analytics, stateful serverless applications, and its deployment on Kubernetes. Engage with questions about Flink's advantages, performance, and integration with other technologies, including Python support.

Introduction
Welcome
About For Very Good
Recap
Quest for Data
Everything is a Stream
String Processing
Apache Flink
Flink Users
String Processing Use Cases
Streaming Analytics Use Cases
Stateful Serverless Applications
Companies using Flink
Alibaba
You Hopper
What makes Flink different
Flink API Stack
Your Code
Distributed Systems
State Fault Tolerance
Checkpointing
Safe Points
Time
Event Time
Processing Time
Summary
Flink on Kubernetes
Questions
Experience with Flink
What does Flink help with
What other events or adapters do you have
Are there any advantages performance or otherwise
Mix and match
Python