The data Artisans Blog
Apache Flink, stream processing, event-driven applications, and more.
Category: Flink Features
Apache Flink® User Survey 2016 Results, Part 1January 19, 2017
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...
Savepoints, Part 2: Streaming Applications in the Pit LaneNovember 16, 2016
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...
Savepoints: Turning Back TimeOctober 14, 2016
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...
Robust Stream Processing with Apache Flink®: A Simple WalkthroughAugust 18, 2016
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...
Extending the Yahoo! Streaming BenchmarkFebruary 2, 2016
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...
How Apache Flink™ Enables New Streaming Applications, Part 1December 9, 2015
(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...
Kafka + Flink: A Practical, How-To GuideSeptember 2, 2015
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...
How Apache Flink™ handles backpressureAugust 31, 2015
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...
High-throughput, low-latency, and exactly-once stream processing with Apache Flink™August 5, 2015
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...