Flink Streaming

Using Apache Flink for Kinesis to Kafka Connect

Reading Time: 3 minutes In this blog, we are going to use kinesis as a source and kafka as a consumer. Let’s get started. Step 1: Apache Flink provides the kinesis and kafka connector dependencies. Let’s add them in our build.sbt: Step 2: The next step is to create a pointer to the environment on which this program runs. Step 3: Setting parallelism of x here will cause all Continue Reading

Comparison between different streaming engines

Reading Time: 5 minutes Distributed stream processing engines have been on the rise in the last few years, first Hadoop became popular as a batch processing engine, then focus shifted towards stream processing engines. Stream processing engines can make the job of processing data that comes in via a stream easier than ever before and by using clustering can enable processing data in larger sets in a timely manner. Continue Reading

Flinkathon: What makes Flink better than Kafka Streams?

Reading Time: 2 minutes Initially, I would like you all to focus on a few questions before comparing the frameworks:1. Is there any comparison or similarity between Flink and the Kafka?2. What could be better in Flink over the Kafka?3. Is it the problem or system requirement to use one over the other? Before talking about the Flink betterment and use cases over the Kafka, let’s first understand their Continue Reading

Is Apache Flink the future of Real-time Streaming?

Reading Time: 5 minutes 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?

Reading Time: 3 minutes 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