Apache 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

Streaming in Spark, Flink and Kafka

There is a lot of buzz going on between when to use use spark, when to use flink, and when to use Kafka. Both spark streaming and flink provides exactly once guarantee that every record will be processed exactly once thereby eliminating any duplicates that might be available. Both provide very high throughput compared to any other processing system like storm, and the overhead of Continue Reading

Introduction To HADOOP !

Here I am to going to  write a blog on Hadoop! “Bigdata is not about data! The value in Bigdata [is in] the analytics. ” -Harvard Prof. Gary King So the Hadoop came into Introduction! Hadoop is an open source, Java-based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment. It is part of the Apache 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 – 3

In the last two blogs on Flink, I hope to have been able to underline the primacy of Windows in the scheme of things of Apache Flink’s streaming. I have shared my understanding of two types of Windows that can be attached to a stream of Events, namely (a) CountWindow and (b) TimeWindow. Variations of these types are offered too; for example, one can put Continue Reading

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