Visit the blog:

visit the blog: https://velog.io/@tpointtechblog/Understanding-Apache-Kafka-The-Backbone-of-Real-Time-Data

Visit more blog:

https://themediumblog.com/read-blog/167042https://tpointtechblog.blogspot.com/2025/05/what-is-mysql-and-why-should-you-learn.htmlhttps://sites.google.com/view/learnjavaprogramminglanguage/home

Apache Kafka is a powerful distributed platform used for building real-time data pipelines and streaming applications. This Kafka tutorial is designed for beginners who want to understand how Kafka works, including its core components like producers, consumers, brokers, and topics. Learn how data flows in real time, how to set up a basic Kafka environment, and how to avoid common mistakes. Whether you're a developer or data engineer, this guide will help you get started with confidence in event-driven architecture.

For more information and interview questions, you can also visit Tpoint Tech, where you can find many related topics.

Contact Information:

 Address : G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India

Mobile: +91-9599086977

 Email:  [email protected]

Avoid common pitfalls in Kafka setup with this beginner-friendly Kafka tutorial.

Microservices Communication With Apache Kafka

When developing a new product, the first thing that comes to mind is how to structure code. There is a battle going on in this regard: monolithic vs microservices. It has been going on for a while because software developers and company owners are still trying to figure out which architectural style is ideal for their applications. The scalability, efficiency, and competitiveness of the product…

While Kafka is incredible at what it does, it isn't intended to replace the database as a long time steady store. This is because of the determination in Kafka by gaining free apache Kafka online training is intended to deal with messages briefly while they are in transit and not to go about as a long haul persistent store liable for serving predictable peruses/composes from profoundly simultaneous user-facing web/mobile applications.

While solid SQL databases, for example, MySQL and PostgreSQL can carry out the responsibility of such a constant store, there is an impedance mismatch between their solid nature and Kafka's disseminated nature that everybody explored already. This is the place present-day appropriated SQL databases, for example, YugaByte DB comes in. These databases have a sharding and replication architecture that is very like that of Kafka and henceforth they intend to convey comparative advantages. Coming up next are the main advantages of an appropriated SQL database in a Kafka-driven informing stage.

1.      Horizontal Write Scalability

You basically make a focal bottleneck in your data structure. The arrangement lies in a dispersed database, preferably a circulated SQL database that can scale on a level plane like a NoSQL database. Dispersed SQL databases do as such using programmed sharding for each table like Kafka making various allotments for every theme. Furthermore, they do as such without giving up solid consistency, ACID transitions or more all SQL as an adaptable inquiry language. Need to deal with top traffic during Black Friday? Essentially include more Kafka brokers and disseminated SQL nodes. Also, scale in smoothly after Cyber Monday. Note that it is generally simple to accomplish horizontal read scalability in solid SQL database yet it is beyond the imagination to expect to accomplish local even compose adaptability.

2.      Native Failover & Repair

With the ISR model and f+1 replicas, a Kafka subject can endure f failures without losing submitted messages by learning it with free apache Kafka online training. Current disseminated SQL databases ordinarily utilize a larger part vote-based per-shard circulated agreement convention, which permits them to endure f failures given 2f+1 replicas. This resistance incorporates zero data loss just as local failover and fixes. The extra f replicas in the database permit it to perform low latency composes without hanging tight for the slowest replica to react. As portrayed beforehand, Kafka's replication convention doesn't offer this advantage yet rather expects a lower replication factor to be utilized if moderate replicas become an issue. It is common that the disseminated SQL databases give an increasingly stringent mix of data durability and low latency ensures than Kafka in light of their job as the long haul goal of the data.

These are the main reason why Kafka needs a distributed SQL database. You will need to understand the language properly by practicing it.