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The data Artisans Blog

Apache Flink, stream processing, event-driven applications, and more.

Category: Flink Features

Apache Flink® User Survey 2016 Results, Part 1

You can find part 2 of survey results here.At the end of 2016, data Artisans organized the first-ever Apache Flink® user survey in order to better understand Flink usage in the community, asking for...

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Savepoints, Part 2: Streaming Applications in the Pit Lane

By Fabian Hueske (@fhueske) and Mike Winters (@wints) Last month, we gave a high-level overview of Apache Flink® savepoints and touched on why and how you’d reprocess data in a streaming applicatio...

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Savepoints: Turning Back Time

This post is the first in a series where the data Artisans team will highlight some of Apache Flink’s® core features. By Fabian Hueske (@fhueske) and Mike Winters (@wints) Stream processing is comm...

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Robust Stream Processing with Apache Flink®: A Simple Walkthrough

Jamie Grier, Director of Applications Engineering at data Artisans, gave an in-depth Apache Flink® demonstration at OSCON 2016 in Austin, TX. A recording is available on YouTube if you’d like to se...

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Extending the Yahoo! Streaming Benchmark

Update December 18, 2017: Nearly 2 years after this initial post, we discussed the Yahoo streaming benchmark in another blog post where we cover some of the issues we see with modern benchmarking met...

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How Apache Flink™ Enables New Streaming Applications, Part 1

(For the rest of this series, see part 2 here and part 3 here)Stream data processing is booming in popularity, as it promises better insights from fresher data, as well as a radically simplified pipe...

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Kafka + Flink: A Practical, How-To Guide

A very common use case for Apache Flink™ is stream data movement and analytics. More often than not, the data streams are ingested from Apache Kafka, a system that provides durability and pub/sub fu...

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How Apache Flink™ handles backpressure

People often ask us how Flink deals with backpressure effects. The answer is simple: Flink does not use any sophisticated mechanism, because it does not need one. It gracefully responds to backpressur...

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High-throughput, low-latency, and exactly-once stream processing with Apache Flink™

The popularity of stream data platforms is skyrocketing. Several companies are transitioning parts of their data infrastructure to a streaming paradigm as a solution to increasing demands for real-tim...

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Real-time stream processing: The next step for Apache Flink™

This post also appears as a guest post at the Confluent blog. Stream processing is becoming very popular with open source projects like Apache Kafka, Apache Samza, Apache Storm, Apache Spark’s Stre...

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