Getting Started with Stream Processing Using Apache Flink
Flink is a stateful, tolerant, and large scale system with excellent latency and throughput characteristics. It works with bounded and unbounded datasets using the same underlying stream-first architecture, focusing on streaming or unbounded data.
What you'll learn
Apache Flink is a distributed computing engine used to process large scale data. Flink is built on the concept of stream-first architecture where the stream is the source of truth. This course, Getting Started with Stream Processing Using Apache Flink, walks the users through exploratory data analysis and data munging with Flink. You'll start off learning about simple data transformations on streams such as map(), filter(), flatMap(), reduce(), sum(), min(), and max() on simple DataStreams and KeyedStreams. You'll then learn about window transformations in detail using tumbling, sliding, count, and session windows. You'll wrap up the course explore operations on multiple streams such as union and joins. All of this with hands on demos using Flink's Java API along with a real world project using Twitter's streaming API. After you've watched this course you'll have a strong foundation for stream processing concepts using Apache Flink.
Table of contents
- Introduction to Window Transformations 4m
- Tumbling Windows 3m
- Sliding Windows 2m
- Count, Session, and Global Windows 5m
- Event Time, Ingestion Time, and Processing Time 7m
- Implementing Tumbling and Sliding Windows 5m
- Implementing the Count Window 5m
- Implementing the Session Window 3m
- Getting the Twitter Consumer Keys and Access Tokens 3m
- Connecting to the Twitter Streaming API 7m