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Introduction of Apache Kafka

Apache Kafka is a distributed publish-subscribe messaging system. It was originally developed at LinkedIn Corporation and later on became a part of the Apache project. Kafka is a fast, scalable, distributed in nature by its design, partitioned and replicated commit log service.

Apache Kafka differs from the traditional messaging system in:

It is designed as a distributed system which is very easy to scale out.

It offers high throughput for both publishing and subscribing.

It supports multi-subscribers and automatically balances the consumers during failure.

It persists messages on disk and thus can be used for batched consumption such as ETL, in addition to real-time applications.

In the context of Apache Kafka, a streaming data pipeline means ingesting the data from sources into Kafka as it's created and then streaming that data from Kafka to one or more targets. we're offloading transactional data from a database to an object store, perhaps for analytical purposes.Because Kafka is a distributed system, it's highly scalable and resilient. By decoupling the source from the target, and by using Kafka to do this, we gain some great benefits.

If the target system goes offline, there's no impact to the pipeline; when the target comes back online, it just resumes from where it got to before, because Kafka stores the data. If the source system goes offline, the pipeline also is unimpacted. The target doesn't even realize that the source is down; it just sees that there's no data. When the source comes back online, data will start to flow again.If the target can't keep up with the rate of data being sent to it, Kafka will take the backpressure.

Pipelines built around Kafka can evolve gracefully. Because Kafka stores data, we can send the same data to multiple targets independently. We can also replay the data, either to back-populate new copies of a target system or to recover a target system after a failure.Pipelines aren't just about streaming the same data from one place to another.When you want to run Kafka, you need to start its broker: a simple instance of Kafka running on a machine, just like any other server. The broker is responsible to send, receive, and store messages into the disk.

A single broker is not enough to ensure Kafka can handle a high-throughput of messages. That goal is achieved through many brokers working together at the same time, communicating and coordinating with each other.A Kafka cluster groups together one or more brokers. Instead of connecting to a single node, your application connects to a cluster that manages all the distributed details for us..

Apache Kafka Online Training Content

OurKafka Admin coursecontent is prepared by domain experts and is aligned with the current industry requirements. It is a complete practical and project-based learning path that provides you with the foundation you need to build your career!

Objectives Of The Course:

Get an overall understanding of fundamentals and internals of Kafka.
Get understanding on Kafka Architecture, Setup, and Configuration.
Get knowledge on Spark, Hadoop, and Storm integration with Kafka.
Get a feel of some of the technologies in action with hands-on and real time use cases.
Troubleshoot and fine tune Kafka components and learn usage patterns and best practices

Recommended Skills Prior To Taking Course

Basic understanding of Apache Hadoop and Big Data.
Basic Linux Operating System knowledge.
Basic understanding of the Scala, Python, R, or Java programming languages.