Can we compare Kafka Vs Axon?

Introduction

This is a commonly asked question what is the exact difference between the working of Axon and Kafka, at what place we are good to use Kafka, and at what place we are good to use the Axon framework, and how their combination works efficiently.
This blog will wrap up most of the misunderstood points on these topics.
Since we know very well from our previous blogs that Axon mainly supports event sourcing, this blog will revolve around event sourcing concepts only and by the end of the blog, you would be able to differentiate well between axon and Kafka in terms of event sourcing.

Event Sourcing

It mainly originates from Domain Driven Design patterns, It helps in designing the applications with the help of a series of events and instead of a series of states.
Here, the databases are not used for the particular state rather the changing events are kept in mind for designing purposes.
 

Usage of Event sourcing

• Temporal Reporting
• Data Analytics
• Flexibility in designing

Event sourcing makes use of CQRS for their implementations, CQRS mainly works with 2 categories, the initial category is a command, and 2nd is a query, CQRS makes use of both these mentioned aspects to make a productive working model, command changes the particular state of the function and query helps to fetch out the details of the particular function.

So basically event sourcing makes usage of the command model from the CQRS building pattern

Kafka- Event Sourcing OR Event Streaming?

So, since we are good with the concepts of event sourcing from the above discussions, therefore now we can proceed to decide whether Kafka is basically using event sourcing concepts or event streaming concepts.
Let’s start!

Kafka works on event streaming patterns which mainly focus on 3 main concepts:

• Publish
• Store
• Process

Kafka event streaming revolves around these three major values. Although if we want to make use of event sourcing with Kafka then we would require some special add-on with the process.
Therefore from this, we can say that Kafka can’t do event sourcing on its own fully, it must be combined with other platforms to make use of it, in some cases, this is efficient to work but in most cases, the reading from the topics becomes extremely slow as well.
So, usage of Kafka only for event sourcing does not make much impact.
Kafka is one of the message brokers which integrates with Axon well.

Axon- Event Sourcing

Since we have already learned much about Axon from our previous blogs. Therefore we already know that axon basically works with 2 major values:

• Axon Server -> Storage of events are made and used for event sourcing, as per the requirement they are delivered for event streaming purposes.
• Axon Framework -> It mainly focuses on the non-functional needs of architecture.

Conclusion – Difference between Axon and Kafka

• Kafka can’t make event sourcing design on its own, requires other patterns to be combined to work efficiently, it integrates with Axon and then can perform event sourcing well.
• Kafka is basically an event streaming design instead of event sourcing.
• Axon can work with event sourcing and much more in addition to that.

Original article source at: https://blog.knoldus.com

#kafka 

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How native is React Native? | React Native vs Native App Development

If you are undertaking a mobile app development for your start-up or enterprise, you are likely wondering whether to use React Native. As a popular development framework, React Native helps you to develop near-native mobile apps. However, you are probably also wondering how close you can get to a native app by using React Native. How native is React Native?

In the article, we discuss the similarities between native mobile development and development using React Native. We also touch upon where they differ and how to bridge the gaps. Read on.

A brief introduction to React Native

Let’s briefly set the context first. We will briefly touch upon what React Native is and how it differs from earlier hybrid frameworks.

React Native is a popular JavaScript framework that Facebook has created. You can use this open-source framework to code natively rendering Android and iOS mobile apps. You can use it to develop web apps too.

Facebook has developed React Native based on React, its JavaScript library. The first release of React Native came in March 2015. At the time of writing this article, the latest stable release of React Native is 0.62.0, and it was released in March 2020.

Although relatively new, React Native has acquired a high degree of popularity. The “Stack Overflow Developer Survey 2019” report identifies it as the 8th most loved framework. Facebook, Walmart, and Bloomberg are some of the top companies that use React Native.

The popularity of React Native comes from its advantages. Some of its advantages are as follows:

• Performance: It delivers optimal performance.
• Cross-platform development: You can develop both Android and iOS apps with it. The reuse of code expedites development and reduces costs.
• UI design: React Native enables you to design simple and responsive UI for your mobile app.
• 3rd party plugins: This framework supports 3rd party plugins.
• Developer community: A vibrant community of developers support React Native.

Why React Native is fundamentally different from earlier hybrid frameworks

Are you wondering whether React Native is just another of those hybrid frameworks like Ionic or Cordova? It’s not! React Native is fundamentally different from these earlier hybrid frameworks.

React Native is very close to native. Consider the following aspects as described on the React Native website:

• Access to many native platforms features: The primitives of React Native render to native platform UI. This means that your React Native app will use many native platform APIs as native apps would do.
• Near-native user experience: React Native provides several native components, and these are platform agnostic.
• The ease of accessing native APIs: React Native uses a declarative UI paradigm. This enables React Native to interact easily with native platform APIs since React Native wraps existing native code.

Due to these factors, React Native offers many more advantages compared to those earlier hybrid frameworks. We now review them.

#android app #frontend #ios app #mobile app development #benefits of react native #is react native good for mobile app development #native vs #pros and cons of react native #react mobile development #react native development #react native experience #react native framework #react native ios vs android #react native pros and cons #react native vs android #react native vs native #react native vs native performance #react vs native #why react native #why use react native

Can we compare Kafka Vs Axon?

Introduction

This is a commonly asked question what is the exact difference between the working of Axon and Kafka, at what place we are good to use Kafka, and at what place we are good to use the Axon framework, and how their combination works efficiently.
This blog will wrap up most of the misunderstood points on these topics.
Since we know very well from our previous blogs that Axon mainly supports event sourcing, this blog will revolve around event sourcing concepts only and by the end of the blog, you would be able to differentiate well between axon and Kafka in terms of event sourcing.

Event Sourcing

It mainly originates from Domain Driven Design patterns, It helps in designing the applications with the help of a series of events and instead of a series of states.
Here, the databases are not used for the particular state rather the changing events are kept in mind for designing purposes.
 

Usage of Event sourcing

• Temporal Reporting
• Data Analytics
• Flexibility in designing

Event sourcing makes use of CQRS for their implementations, CQRS mainly works with 2 categories, the initial category is a command, and 2nd is a query, CQRS makes use of both these mentioned aspects to make a productive working model, command changes the particular state of the function and query helps to fetch out the details of the particular function.

So basically event sourcing makes usage of the command model from the CQRS building pattern

Kafka- Event Sourcing OR Event Streaming?

So, since we are good with the concepts of event sourcing from the above discussions, therefore now we can proceed to decide whether Kafka is basically using event sourcing concepts or event streaming concepts.
Let’s start!

Kafka works on event streaming patterns which mainly focus on 3 main concepts:

• Publish
• Store
• Process

Kafka event streaming revolves around these three major values. Although if we want to make use of event sourcing with Kafka then we would require some special add-on with the process.
Therefore from this, we can say that Kafka can’t do event sourcing on its own fully, it must be combined with other platforms to make use of it, in some cases, this is efficient to work but in most cases, the reading from the topics becomes extremely slow as well.
So, usage of Kafka only for event sourcing does not make much impact.
Kafka is one of the message brokers which integrates with Axon well.

Axon- Event Sourcing

Since we have already learned much about Axon from our previous blogs. Therefore we already know that axon basically works with 2 major values:

• Axon Server -> Storage of events are made and used for event sourcing, as per the requirement they are delivered for event streaming purposes.
• Axon Framework -> It mainly focuses on the non-functional needs of architecture.

Conclusion – Difference between Axon and Kafka

• Kafka can’t make event sourcing design on its own, requires other patterns to be combined to work efficiently, it integrates with Axon and then can perform event sourcing well.
• Kafka is basically an event streaming design instead of event sourcing.
• Axon can work with event sourcing and much more in addition to that.

Original article source at: https://blog.knoldus.com

#kafka 

Kafka Spark Streaming | Kafka Tutorial

In this kafka spark streaming tutorial you will learn what is apache kafka, architecture of apache kafka & how to setup a kafka cluster, what is spark & it’s features, components of spark and hands on demo on integrating spark streaming with apache kafka and integrating spark flume with apache kafka.

# Kafka Spark Streaming #Kafka Tutorial #Kafka Training #Kafka Course #Intellipaat

PWA vs Native App: Which Is Better Option In 2021?

Every year, the world is expanding with the launch of new smartphones and other gadgets available in the market. According to Statista, more than 50% of the population will be using smartphones by the end of 2021.

Hence, businesses worldwide have understood the importance of smartphones and are joining the mobile industry by launching native apps.

Apart from native apps, progressive web apps is another technology that is gaining a lot of attention among businesses. Moreover, various leading companies worldwide have openly accepted PWA and built progressive web apps.

Now, the question arises, how is PWA different from the native apps? Read More

#pwa vs native #pwa vs native app #progressive web app vs native #progressive web app vs native app #pwa vs native app performance

Diving Deep into Kafka

The objective of this blog is to build some more understanding of Apache Kafka concepts such as Topics, Partitions, Consumer, and Consumer Groups. Kafka’s basic concepts have been covered in my previous article.

Kafka Topic & Partitions

As we know, messages in Kafka are categorized or stored inside Topics. In simple terms, Topic can be construed as a Database table. Kafka Topics inside is broken down into partitions. Partitions allow us to parallelize a topic by splitting the data of a topic across multiple brokers, thus adding an essence of parallelism to the ecosystem.

Behind the scenes

Messages are written to a partition in an append-only manner, and messages are read from a partition from beginning to end, FIFO mannerism. Each message within a partition is identified by an integer value called _offset. _An offset is an immutable sequential ordering of messages, maintained by Kafka. Anatomy of a Topic with multiple partitions:

Partitioned Topic

Sequential number in array fashion is the offset value maintained by Kafka

Some key points:

1. Ordering of messages is maintained at the partition level, not across the topic.
2. Data written to partition is immutable and can’t be updated.
3. Each message in the Kafka broker is a collection of message topics, partition, offset, key, and value.
4. Each partition will have a leader that will take care of Read/Write operations in the partition.

#kafka-python #kafka #streaming #apache-kafka