Expanded

Exploring the Apache Flink API for Processing Streaming Data

Flink is a stateful, tolerant, and large scale system which works with bounded and unbounded datasets using the same underlying stream-first architecture.
Course info
Level
Beginner
Updated
Dec 7, 2020
Duration
3h 31m
Table of contents
Course Overview
Applying Transforms on Input Streams
Performing Custom Transforms on Streams
Working with Windowing Operations on Streams
Exploring the Table API and Running SQL Queries
Description
Course info
Level
Beginner
Updated
Dec 7, 2020
Duration
3h 31m
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Description

Apache Flink is built on the concept of stream-first architecture where the stream is the source of truth. In this course, Exploring the Apache Flink API for Processing Streaming Data, you will perform custom transformations and windowing operations on streaming data.

First, you will explore different stateless and stateful transformations that Flink supports for data streams such as map, flat map, and filter transformations.

Next, you will learn the use of the process function and the keyed process function which allows you to perform very granular operations on input streams, get access to operator state, and access timer services.

Finally, you will round off your knowledge of the Flink APIs by performing transformations using the table API as well as SQL queries.

When you are finished with this course you will have the skills and knowledge to design Flink pipelines, access state and timers in Flink, perform windowing and join operations, and run SQL queries on input streams.

About the author
About the author

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.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, my name is Janani Ravi, and welcome to this course on Exploring the Apache Flink API for Processing Streaming Data. A little about myself. I have a masters in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google. I was one of the first engineers working on real‑time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup Loonycorn, a studio for high‑quality video content. Apache Flink is built on the concept of stream‑first architecture, where the stream is the source of truth. In this course, you will perform custom transformations, and when doing operations on streaming data, using Apache Flink APIs. First, you'll explore different stateless and stateful transformations that Flink supports for data streams, such as map, flatMap, and filter transformations, which allow you to perform very granular operations on input streams, get access to operator state, and access timer services. You will also see how multiple output streams can be generated using side output and how state can be broadcast to downstream operators using broadcast state. Next, you'll explore the different kinds of windowing operations that Flink supports and perform aggregations using tumbling windows, sliding windows, and session windows. You will also see how different streams can be joined together using window joins and interval joins. Finally, you will round off your knowledge of the Flink APIs by performing transformations using the Table API, as well as using SQL queries. When you're finished with this course, you will have the skills and knowledge to design Flink pipelines, access state and timers in Flink, perform windowing and join operations, and run SQL queries on input streams.