Author: Himanshu Gupta

Flinkathon: Guide to setting up a Local Flink Custer

In our previous blog post, Flinkathon: First Step towards Flink’s DataStream API, we created our first streaming application using Apache Flink. It was easy, clean, and concise. However, the real power of Apache Flink is seen on a cluster, where data is processed in a distributed manner, with the advantage of multi-core/multi-memory systems. So, in this blog post, we will see how to set up Continue Reading

Flinkathon: First Step towards Flink’s DataStream API

In our previous blog posts: Flinkathon: Why Flink is better for Stateful Streaming applications? Flinkathon: What makes Flink better than Kafka Streams? We saw why Apache Flink is a better choice for streaming applications. In this blog post, we will explore how easy it is to express a streaming application using Apache Flink’s DataStream API. DataStream API DataStream API is used to develop regular programs Continue Reading

Flinkathon: Why Flink is better for Stateful Streaming applications?

Stream processing is a way to query a continuous stream of data and draw conclusions from it within the boundaries of a real-time scenario. For example, receiving an alert as soon as a fraudulent transaction is done via a credit/debit card. The 2 main types of stream processing done are: Stateless: Where every event is handled completely independent from the preceding events. Stateful: Where a Continue Reading

A Beginner’s Guide to Deploying a Lagom Microservice on Kubernetes

Both Lagom and Kubernetes are gaining popularity quite fast. Lagom is an open source framework for building reactive microservice systems in Java/Scala. And, Kubernetes (or K8s in short) is an open-source system for automating deployment, scaling, and management of containerized applications. Together they make an excellent stack for developing Reactive microservices of production grade. We have already seen a lot of blogs on Lagom on this Continue Reading

Structured Streaming: Philosophy behind it

In our previous blogs: Structured Streaming: What is it? & Structured Streaming: How it works? We got to know 2 major points about Structured Streaming – It is a fast, scalable, fault-tolerant, end-to-end, exactly-once stream processing API that helps users in building streaming applications. It treats the live data stream as a table that is being continuously appended/updated which allows us to express our streaming computation as Continue Reading

Structured Streaming: How it works?

In our previous blog post – Structured Streaming: What is it? we got to know that Structured Streaming is a fast, scalable, fault-tolerant, end-to-end, exactly-once stream processing API that helps users in building streaming applications. Now it’s time to learn  – How it works? So, in this blog post, we will look at the working of a structured stream via an example. So, let’s take a 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

A Beginner’s Guide to Deploying a Lagom Service Without ConductR

How to deploy a Lagom Service without ConductR? This question has been asked and answered by many, on different forums. For example, take a look at this question on StackOverflow – Lagom without ConductR? Here the user is trying to know whether it is possible to use Lagom in production without ConductR or not. To which the best answer that came up was – “Yes, it is Continue Reading

KnolX: Learning Kafka Streams with Scala

Hello everyone, Knoldus organized a session on 22nd September 2017. The topic was “Learning Kafka Streams with Scala”. Many people attended and enjoyed the session. In this blog post, I am going to share the slides & video of the session. Slides: Video: If you have any query, then please feel free to comment below.  

Self-Learning Kafka Streams with Scala – #1

A few days ago, I came across a situation where I wanted to do a stateful operation on the streaming data. So, I started finding possible solutions for it. I came across many solutions which were using different technologies like Spark Structured Streaming, Apache Flink, Kafka Streams, etc. All the solutions solved my problem, but I selected Kafka Streams because it met most of my Continue Reading

Spark Structured Streaming: A Simple Definition

“Structured Streaming”, nowadays we are hearing this term in Apache Spark ecosystem quite a lot, as it is being preached as next big thing in scalable big data world. Although, we all know that Structured Streaming means a stream having structured data in it, but very few of us knows what exactly it is and where we can use it. So, in this blog post Continue Reading

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