Apache Kafka is a relatively easy system for your organization to learn and use. Even with the basics, a user can do so much. After they start, there is much to learn about Kafka in the open-source community. You can quickly go from a beginner to an advanced user.

As a powerful event-streaming platform, Kafka has dozens of real-world applications and the capacity to meet any demand for speed and availability. As mentioned, it is an open-source framework initially developed over a decade ago by LinkedIn. It was designed to ingest and process streaming data in real time.

Apache Kafka is used for more today. It can be implemented into high-performance data pipelines, stream analytics applications, and data integration.

Here is your beginner’s guide to Apache Kafka and how to use it.

How to Define Kafka

It is a system that processes real-time data from multiple producers and applications. This data is then available in a consumer system or application at low latency.

In Kafka, brokers are servers that store data and serve clients. Producers are client applications that write events to topics. Consumers are applications that read and process events from topics.

Topics are categories in which records are published. Two types of topics exist. Compacted topics have no time or space limits. Regular topics, however, can be configured to expire.

How to Start Kafka

To start with Kafka, download the latest release and extract it. Apache Kafka opens with either ZooKeeper or KRaft. Run the commands you need to open your environment.

Create a Topic to Store Events

Kafka lets you read, write, store, and process ‘events,’ sometimes called records or messages. Events can take many forms, from a payment transaction to a smartphone’s geolocation update. They can also be a shipping order, a sensor measurement from an IoT device or medical equipment, or anything similar.

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Every event is organized and stored in ‘topics.’ A topic is to a file system what a folder is to a folder. A topic must be created before an event is written.

Write Events into the Topic

Run your console producer client to write about events related to your created topic. Once received, these events will be stored for as long as needed or even in perpetuity.

After writing some events, open another session, run the console consumer client, and read the events you created. See how the events show up in your consumer terminal. An event can be read as often as possible without affecting the file or storage quality.

Import or Export Data as Events Streams

Many individuals who come to Kafka have immense amounts of data in existing systems – i.e. relational databases, messaging systems, and applications that they use. Through Kafka Connect, you can automatically import data from external systems into Kafka. You can also export Kafka data into external systems in perpetuity.

This tool runs connectors, implementing custom logic for these interactions. You should add connect-file-3.7.0.jar to the plugin. Path property in the Kafka Connect worker’s configuration. For production deployments, absolute paths are preferred.

Run a single, local, dedicated process in standalone mode with the connectors you intend to use and the configuration files as parameters. This will test your connection and verify everything works as intended.

Process Events with Kafka Streams for Java/Scala

After storing data as events in Kafka, they can be processed using Kafka Streams for Java/Scala. This client library allows you to implement real-time applications and microservices, storing input and output data in Kafka topics.

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Write and deploy standard Java and Scala applications on the client side but with Kafka’s server-side cluster technology. This makes these apps highly scalable, fault-tolerant, and distributed.

Why Developers Love Kafka

Developers can scale and maintain Kafka systems from simple actions to more advanced functions without anything else. It is an all-in-one system that combines a message broker, event store, and stream processing framework. It can simultaneously connect hundreds of data sources and process massive continuous data streams. Apache Kafka has endless benefits.

Its intelligently designed architecture includes data partitioning, batch processing, zero-copy techniques, and append-only logs. Kafka can achieve significant throughput and can handle high-velocity and high-volume data scenarios.

Dividing a topic into multiple partitions allows load balancing across servers. It distributes production clusters in different geographical areas and availability zones.

Data stream storage is distributed in a fault-tolerant cluster that guards against server failure. Even during a server outage, Kafka will remain operational, routing queries to different brokers when a server experiences issues.

Maximizing with Apache Kafka

You can quickly scale up Kafka by creating large numbers of partitions. Messages are delivered at network-limited throughput through a cluster of servers with latency as low as two milliseconds. Messages are saved to a disc, and intra-cluster replication is offered.