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.
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.
A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.
Course Overview Hi, my name is Janani Ravi, and I'd like to welcome you to this course today. A little bit about myself, I have a masters degree in electrical engineering from Stanford, and have worked with companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on a real-time collaborative editing in Google Docs and I hold four patents for it under Line Technologies. I currently work on my own startup, Loony Corn, a studio for high-quality video content. This course focuses on Apache Flink, a distributed computing engine to process large-scale data. Flink is built on the concept of stream-first architecture where the stream of data is the source of true. This course walks users through exploratory data analysis and data managing with Flink from very first principles. Learn how to perform simple data transformations on streams such as map, filter, flat map, and reduce, understanding how Windows transformations work in great detail using tumbling, sliding, down, and session windows, all of this with hands-on demos in Java using Flink's run time live jury include a project to connect to the Twitter streaming API to pass and extract information from real-time tweets.