Insights and perspectives to keep you updated.

Monitoring Kafka with Prometheus and Grafana

sakshigupta2312Apache Kafka, Monitoring, Streaming Solutions

Kafka monitoring is an operation which is used for the optimization of the Kafka deployment. This process is easy and efficient, by applying one of the existing monitoring solutions instead of building your own. Let’s say, we use Apache Kafka for message transfer and processing and we want to monitor it.But, before learning the steps for monitoring, let’s first understand the prerequisites. Kafka It is Continue Reading

Data processing using ML Supervised classification algorithm to find accuracy

Shubham DangareML, AI and Data Engineering, python, , ,

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. Type of machine learning Supervised learning Unsupervised Learning Reinforcement Learning In Supervised Learning, algorithms learn from labeled data. After understanding Continue Reading

Getting Started with Akka Remoting

Prabhat KashyapAkka, Scala, ,

When we start with Akka we generally start with one actor system on our local machine but when we talk about the business application we can have multiple parts of an application and those part can run on different machine or node. Akka Remoting is a communication module to connect the actor system in peer to peer fashion. It also serves as the foundation for Continue Reading

Tired off creating Grafana dashboards manually? Let’s automate it.

Rishi KhandelwalAPI's, DevOps, Monitoring

We have already discussed the creation of Grafana dashboards and alerts in my previous blogs. We were doing that manually. But think of, if we need to do that in more than 10 environments then we need to repeat that manual process again and again and sometimes we get frustrated by doing all these repetitive stuff. We should have some automated process for doing this. So Continue Reading

Why programming community should be serious about RUST

Ayush MishraRust,

I have been exploring Rust from the last few weeks, as it has been declared the most loving language in 2018. As a Scala/Java developer, I worked on many applications, which were related to the banking domain, web applications, mobile applications, e-commerce, social media, etc. But I was not able to find what kind of applications should be built in Rust? I have searched Rust’s Continue Reading

MachineX: The alphabets of Artificial Neural Network – (Part 2)

shubhamgoyal01Artificial intelligence, Future, ML, AI and Data Engineering

If you are reading this blog, it is supposed that you have already done with Part 1 No???? Then visit to the previous blog The alphabets of Artificial Neural Network first and comeback here for an awesome knowledge about Neural network working. We got the basic understanding of neural network so let’s get into deep. Let’s understand how neural networks work. Once you got the Continue Reading

Flinkathon: Guide to setting up a Local Flink Custer

Himanshu GuptaApache Flink, Apache Kafka, cluster, Flink, Scala, Streaming

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

MachineX: The inevitable Principal Component Analysis

Rahul KhannaML, AI and Data Engineering, ,

In this blog post, we will look at an interesting feature extraction technique of Machine Learning known as Principal Component Analysis (PCA). PCA is one of the powerful techniques in dimensionality reduction, in fact, the de facto standard for human face recognition. Let’s first understand what is dimensionality reduction Dimensionality Reduction As an example let’s say we have a data set with many-many features(which is Continue Reading

Different ways for `Error Propagation` in Rust

Ayush MishraRust, ,

Error Propagation means the code, that detects the problem, must propagate error information back to the caller function so that it can handle the problem. In the real world, the practice of Error Propagation is necessary. One of the benefits is that your code will look cleaner by simply propagating error information back to the caller that can handle the error and another benefit is Continue Reading

Determine Kafka broker health using Kafka stream application’s JMX metrics and setup Grafana alert

Rishi KhandelwalApache Kafka, DevOps, Monitoring, Scala, Streaming

As we all know, Kafka exposes the JMX metrics whether it is Kafka broker, connectors or Kafka applications. A few days ago, I got the scenario where I needed to determine Kafka broker health with the help of Kafka stream application’s JMX metrics. It looks bit awkward, right? I should use the broker’s JMX metrics to do this, why am I looking to application JMX Continue Reading

Realm: What and Why?

Pallavi SinghScala

In this blog, I will be introducing the Realm along with its ecosystem and components in detail. Introduction The realm is a NoSQL based server and a client component. The components connect via a fast and efficient sync protocol. It enables real-time, connected apps and services that are responsive and performant irrespective of network status. The design of the realm is based on the principle Continue Reading

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