Flink

Is Apache Flink the future of Real-time Streaming?

In our last blog, we had a discussion about the latest version of Spark i.e 2.4 and the new features that it has come up with. While trying to come up with various approaches to improve our performance, we got the chance to explore one of the major contenders in the race, Apache Flink. Apache Flink is an open source platform which is a streaming Continue Reading

Structured Streaming: What is it?

With the advent of streaming frameworks like Spark Streaming, Flink, Storm etc. developers stopped worrying about issues related to a streaming application, like – Fault Tolerance, i.e., zero data loss, Real-time processing of data, etc. and started focussing only on solving business challenges. The reason is, the frameworks (the ones mentioned above) provided inbuilt support for all of them. For example: In Spark Streaming, by just adding Continue Reading

Another Apache Flink tutorial, following Hortonworks’ Big Data series

Background A couple of weeks back, I was discussing with a friend of mine, on the topic of training materials on Apache Spark, available online. Of the couple of sites that I mentioned, the hadoop tutorial from Hortonworks, came up. This was primarily because I liked the way they organized the content: it was clearly meant for encouraging newcomers to try things hands-on, banishing the Continue Reading

Getting close to Apache Flink, albeit in a Träge manner – 2

From the preceding post in this series In the last blog , we had taken a look at Flink’s CountWindow feature. Here’s a quick recap: As a stream of events enter a Flink-based application, we can apply a transformation of CountWindow on it (there are many such transformations the Flink offers us, we will meet them as we go). CountWindow allows us to create a Continue Reading

Getting close to Apache Flink, albeit in a Träge manner – 1

Of late, I have begun to read about Apache Flink. Apache Flink (just Flink hereafter), is an ‘open source platform for distributed stream and batch data processing’, to quote from the homepage.  What has caught my interest is Flink’s idea that, the ability operate on unit of data streaming in gives one the flexibility to decide what constitutes a batch: count of events or events Continue Reading

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