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Presenting our Streaming Concepts & Introduction to Flink Video Series


Transitioning from the batch data processing world into the world of stream processing and real time analytics can be challenging. Throughout this process, there are many new concepts you need to grasp, technologies and stream processing frameworks to explore, and finally, an underlying mental shift in how you treat data from static, long-lived records in a database to a continuous stream of events constantly flowing through your data processing system. To support developers, data engineers, software architects and engineering leads starting with real time event streaming we are excited to present Ververica’s brand new series of videos called “Streaming Concepts & Introduction to Flink”. 

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Our video series will cover both basic stream processing concepts as well as Apache Flink internals. Over the course of the following months, we will give an introduction to stateful stream processing and how it relates to batch processing as well as cover more Flink-related concepts, such as Flink’s runtime architecture, event time and windowing with Apache Flink, savepoints, checkpoints and many more Flink features that make it a unique framework for any stream processing scenario. Whether you are new to stream processing or want to freshen up your Flink knowledge, our video series is a great source to get you started with the Apache Flink framework. Make sure to subscribe to the Ververica YouTube channel to get the latest videos and news about stream processing and Apache Flink. 

The first video of the series includes an introduction to stateful stream processing as a novel data processing paradigm. It explains how stream processing relates to and differs from batch processing while highlighting where the need for stream processing frameworks comes from. In this first video, we will also introduce Apache Flink as a unified framework for processing large amounts of data. The following videos, coming out soon, will explore Flink’s approach to processing both unbounded and bounded data streams and will reason about some of Flink’s unique characteristics, such as its low-latency, high-throughput architecture, that make it unique for real time data processing scenarios. We will also dive deeper into the mechanics with which Flink handles state (the data of your application) by persisting intermediate results internally — without the need for external storage or a distributed file system — and how that applies to stateful computations over data streams. 


We are excited to be taking you through the streaming processing journey with Apache Flink and we hope you find this video, as well as the future videos of the series, helpful and insightful. The next videos are coming up soon so stay tuned and subscribe to the Ververica YouTube channel to get notified about new videos in the coming weeks and months. If you have questions about stream processing with Apache Flink, you can also reach out through our contact forms below! 

Happy (Video) Streaming!   📽️📽️📽

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Alexander Fedulov
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Alexander Fedulov

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