What is Apache Flink®?

Learn the basics of Apache Flink® and how to get started with simple, serverless Flink!
Flink is a powerful, battle-hardened stream processor that has rapidly grown in popularity, becoming the de facto standard for stream processing and a top-five Apache project. Kai Waehner, Field CTO at Confluent, explains how Flink fits into your data streaming architecture, why stream processing is needed for real-time data, and how Flink’s underlying architecture provides a number of advantages.
From building event-driven applications to performing real-time analytics, teams that put stream processing into practice can unlock real-time use cases and save on redundant downstream processing costs. Developers choose Flink because of its performant runtime engine, resiliency for failure recovery, support for streaming and batch modes, and APIs and language flexibility. Getting started with Flink is easy and seamless using Confluent Cloud, where you can experience both Flink and Apache Kafka® on a unified, cloud-native data streaming platform.
00:00 Overview of data streaming
1:07 Why stream processing?
2:58 Introduction to Flink
5:12 A look at Flink’s architecture
7:36 Flink as a fully managed service
LEARN MORE
►Apache Flink® 101 course: developer.confluent.io/course...
►Get started on Confluent Cloud for free: www.confluent.io/get-started/
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ABOUT CONFLUENT
Confluent is pioneering a fundamentally new category of data infrastructure focused on data in motion. Confluent’s cloud-native offering is the foundational platform for data in motion - designed to be the intelligent connective tissue enabling real-time data, from multiple sources, to constantly stream across the organization. With Confluent, organizations can meet the new business imperative of delivering rich, digital front-end customer experiences and transitioning to sophisticated, real-time, software-driven backend operations. To learn more, please visit www.confluent.io.
#confluent #apacheflink #flink #apachekafka #kafka #cloud

[@em-dashman4404]

Very engaging video, with just the right amount of information. Top effort!

[@sabaokangan]

More lightboard videos of Flint would be so helpful :D

[@saravmca]

I am a beginner I understood the Concepts with in 10 mins . Very good explanation

[@PradeepKumar-wm8lh]

Thanks for the video it’s really insightful...Can you just explain in a video or here…How kafka and flink can fit in a realtime scenario with their duties for understanding with more clarity.

[@neshkeev]

Why would one need to use Flink when there is already Kafka Streams and KSQLDB?

[@tuanongquoc5647]

Same question like you

[@mayankjuneja1354]

The answer starts at 3:10

[@kaiwaehner5702]

Flink is a separate compute engine that is might more scalable and efficient than Kafka Streams / KSQL (because it does not rely directly on Kafka Topics). Other benefits: Support for multiply APIs (SQL, Java, Python), Unified API for streaming and batch, support for CEP (complex event processing, i.e. pattern matching), connectivity to multiple Kafka clusters in one query, etc. Kafka Streams on the other side is a very lightweight library that can be embedded into microservices (e.g., Spring Boot applications operated in its own Docker container). Very different sweet spot than Flink.

[@user-gz9jo6oy8h]

Kafka Streams and KSQLDB doesn't support analytical job like Flink does

[@KaiWahner]

@@user-gz9jo6oy8h What exactly do you mean with „analytical job“?

[@jdog8019]

Brilliant...

[@shankarsg1405]

Awesome video.. just suggestion is look straight at screen a feel like you are explaining to viewers

[@mrsabbih]

Why not Apache Spark streaming from kafka

[@KaiWahner]

You can also use Spark Streaming together with Kafka. The fundamental difference is that Spark was built for batch and added streaming capabilities while Flink was designed for streaming from the beginning. This fact is combination with some other benefits and mature features for transactional workloads, complex event processing (CEP) capabilities, much better open source community adoption and growth (for streaming data, not for batch data) make Flink the better choice for most data streaming projects.