Flink

Flink: Union operator on Multiple Streams

Reading Time: 3 minutes Apache Flink offers rich sources of API and operators which makes Flink application developers productive in terms of dealing with the multiple data streams. Flink provides many multi streams operations like Union, Join, and so on. In this blog, we will explore the Union operator in Flink that can combine two or more data streams together. We know in real-time we can have multiple data streams from different sources Continue Reading

Flink: Implementing the Session window.

Reading Time: 3 minutes In the previous blogs, we learned about Tumbling, Sliding, and Count windows in Flink. There is one another useful way to window the data which Flink offers i.e, Session window. So in this blog, we will explore the Session window in detail with an example. In the real world, all the work that we do online- Visiting a website, Clicking around the website, do online Continue Reading

Flink: Time Windows based on Processing Time

Reading Time: 4 minutes In the previous blog, we talked about Flink’s windows operator, a heart of processing infinite streams. Generally in Flink, after specifying that the stream is keyed or non keyed, the next step is to define a window assigner. The window assigner defines how elements are assigned to windows. Flink provides some useful predefined window assigners like Tumbling windows, Sliding windows, Session windows, Count windows, and Continue Reading

Basic Anatomy of a Flink Program

Reading Time: 3 minutes Hi Folks! Hope you all are safe in the COVID-19 pandemic and learning new tools and tech while staying at home. I also have just started learning a very prominent Big Data framework for stream processing which is  Flink. Flink is a distributed framework and based on the streaming first principle, means it is a real streaming processing engine and implements batch processing as a special case. In Continue Reading

Windows operator: Heart of processing infinite streams in Flink

Reading Time: 3 minutes Apache Flink is an open-source, distributed, Big Data framework for stream and batch data processing. Flink is based on the streaming first principle which means it is a real streaming processing engine and implements batching as a special case. Flink is considered to have a heart and it is the “Windows” operator. It makes Flink capable of processing infinite streams quickly and efficiently. Windows split Continue Reading

Reading Avro files using Apache Flink

Reading Time: 2 minutes In this blog, we will see how to read the Avro files using Flink. Before reading the files, let’s get an overview of Flink. There are two types of processing – batch and real-time. Batch Processing: Processing based on the data collected over time. Real-time Processing: Processing based on immediate data for an instant result. Real-time processing is in demand and Apache Flink is the 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

Streaming in Spark, Flink and Kafka

Reading Time: 7 minutes 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

Is Flink the shiny(err..) toy on the block?

Reading Time: 3 minutes If you are following the Big Data space especially from a Scala Space perspective then you would have noticed a troll of blogs, tweets and more blogs comparing the two. The two being Spark and Flink. That said, you would also find comparing these two with Samza and Storm. Incidentally all of them are top level Apache projects. For the purpose of this blog, let us stick Continue Reading