Apache Kafka Advantages and Disadvantages

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Kafka is useful in this case because it can transfer data from producers to data processors and then to data repositories. This makes Kafka useful for monitoring purposes, especially when it comes to real-time monitoring. Kafka is overkill when you need to process a small number of messages per day (up to a few thousand).

Avoid using Kafka for ETL jobs, especially where real-time processing is required. At the same time, Kafka should not be used for online data transformation, data storage, or when all you need is a simple task queue. A better solution would be to use Kafka to only store data for a short time and migrate the data to a relational or non-relational database, depending on your specific needs.

On-the-fly data manipulation is possible with Kafka, but the system used has limitations. Instead, it stores all messages for a set period of time, and consumers are charged for tracking their location in each log. Therefore, Kafka can support a huge number of consumers and store huge amounts of data with no overhead.

Kafka was designed to offer these distinct advantages over AMQP, JMS, etc. Kafka is a valuable tool in scenarios that require real-time data processing and application activity monitoring, as well as monitoring purposes. Due to its high efficiency, reliability and replication features, Kafka is applicable to systems such as service call monitoring (tracking each call), instant messaging or IoT sensor data monitoring where traditional technologies may not be considered. Compared to most messaging systems, Kafka has better throughput, built-in partitioning, replication, and fault tolerance, making it a good solution for both small and large-scale messaging applications.

The original use case for Kafka was to rebuild a user activity monitoring pipeline as a set of real-time publish and subscribe channels. In Kafka, messages are written to a topic (or multiple logs, one for each topic) that maintains that log, from which subscribers can read and get their own views of the data (such as materialized views). Distributed commit log means that the log is distributed among multiple Kafka brokers, which means it has all the characteristics of a distributed system, such as resource sharing, concurrency, fault tolerance, etc.

So far, we have touched on the capabilities of Kafka, its data structure, but we are still not sure how it differs from traditional messaging systems. To understand this, let’s dive a little into the world of messaging systems. This means that it is more reliable than similar messaging services available. It is designed to deliver huge message streams for analytics with extremely low latency on cloud platforms.

Kafka was originally developed by LinkedIn as a scalable messaging platform for social media’s central data pipeline to accommodate its growing membership. If you are not familiar with Kafka, it is a scalable, fault-tolerant and publish messaging system with subscribers that allows you to build distributed applications and serve Internet companies like LinkedIn, Twitter, AirBnB, etc. He combines Kafka with streaming stacks like Apache Spark and Apache Samza to route and load data into internal data stores like ElasticSearch and Cassandra, and directly into real-time scan engines. It can be deployed and scaled on any Kubernetes environment (such as AWS EKS) or an existing custom Kafka Connect cluster.

It provides simple publish/subscribe and topic-queue semantics, a simplified computing platform, automatic cursor handling for subscribers, and cross-datacenter replication. Meanwhile, a 2018 Apache Kafka report that polled over 600 users found data and messaging pipelines for two of the technology’s main uses.

After evaluating several options, Tuya chose Apache Pulsar as it proved to be the best for handling message accumulation and reuse. After comparing various messaging systems such as Kafka and LeviMQ, Tuya finally chose Apache Pulsar. Ease of operation (compared to Kafka); automation-oriented Apache Pulsar is a flexible public subscription messaging system with a segmented layered architecture.

Pulsar is a scalable, low-latency messaging platform that runs on basic hardware. If you want to learn more about Apache Pulsar, feel free to check out Pandio, a distributed messaging platform that outperforms Kafka in almost every possible use case and is positioned for future machine learning workloads. Pulsar is the latest Apache Software Foundation project to achieve Level 1 status, and it draws many comparisons to another ASF project, Kafka.

Apache Kafka is an open source software platform that uses stream processing to provide a low latency and throughput platform that processes data streams in real time. Apache Kafka is a fast, scalable, reliable, and fault-tolerant publish-subscribe messaging system used to manage large amounts of data.

Apache Kafka provides a distributed subscription and publish messaging system as well as a robust queue that can handle large amounts of data and allows us to pass on message consumption. Apache Kafka(r) is an open source distributed event streaming platform used by 80% of the Fortune 100 and thousands of small and medium enterprises (SMBs) to implement high-performance data pipelines, streaming analytics, data integration, and mission execution. critical applications. Apache Kafka event streaming architecture self-hosted approaches are the world’s most powerful data center solutions, providing real-time analytics capabilities for large multinational companies and Fortune 500 brands. Apache Kafka event streaming architecture self-service solutions can be managed in a public domain. , private, hybrid or multicloud cloud using VMware, OpenStack and Kubernetes platforms.

These products help SMBs get started with Apache Kafka event streams immediately, reducing implementation time and at an affordable price without the risk of building and managing it themselves. Invest in programming custom event streaming messaging software solutions with Kafka APIs instead of paying for staff and supporting 24/7 data center operations teams. Even with the Kafka Stream API, you have to spend days building complex pipeline data and interaction management. between data producers and data consumers. Although Apache Kafka comes with many messaging paradigms, some of them are still missing. This can become a real problem if you need to scale your infrastructure use cases. Limits Kafka’s ability to support building complex data pipelines.

All messages written in Kafka are backed up and replicated to peer brokers for fault tolerance, and these messages remain in circulation for a configurable time period (e.g. 7 days, 30 days, etc.). Kafka creates an abstraction of file details and provides a cleaner abstraction of log data or events as a message stream. Kafka supports huge archived log data, making it a great backend for an application built in this style.

Kafka is also suitable for large-scale messaging applications because of its better throughput, built-in partitioning, replication, and fault tolerance. Kafka looks and works like a publish and subscribe system that provides concise, durable, and scalable messages. It handles all the complexities of distributed systems, allowing developers to focus on the problem at hand. The platform is the best choice on the market for any business or industry looking for a scalable real-time data solution to create and manage data pipelines.

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