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Introduction
Apache Kafka is an open-source platform created by the Apache Software Foundation in 2011. According to the survey report, over 60% of Fortune 100 firms currently use Apache Kafka as an event streaming platform. The fact that Kafka has many features makes it the most common choice for an event streaming platform.
In this article, we'll go through the key features and benefits of Kafka that any Kafka developer should know.
Why use Kafka?
Before discussing the key features and benefits of Kafka, let’s discuss why to use Kafka.
Real-time data streaming applications and pipelines that adapt to the data streams are typically built using Kafka. In this process, an event variable with a record, date(timestamp), key-value for data shared facilities, and header for metadata code are created by Apache Kafka. Every event is assigned a unique key value that is never utilised, and it is then saved in data archives, where it may be processed for use in real-time and historical analytics.
It mixes interactions, storage, and stream processing to enable storing and analysing historical and real-time data. Events can track platform activity from mobile and online applications and return results from IoT devices used in industrial and environmental research.
Since it is more durable, dependable, and fault-tolerant than conventional message queues, it is an excellent fit for large-scale message processing applications.
Kafka Architecture
To handle enormous volumes of data, data is distributed across brokers or processed in parallel across brokers. Due to its distributed architecture, Kafka can broadcast massive amounts of data with incredibly low latency. Let’s understand the architecture of Kafka using the following diagram to understand the key features and benefits of Kafka quickly.
The working of Kafka architecture is as follows:
In the beginning, Kafka accepts event data streams by producers.
A Kafka cluster includes multiple brokers, where Kafka stores records chronologically in partitions among brokers (servers).
Each record contains a key-value combination describing an event; the timestamp and header metadata are optional details.
Kafka groups records into topics.
Data consumers obtain their data by subscribing to the subjects they choose.
Key Features of Kafka
The key features of Kafka are as follows:
Scalability
Kafka may be upgraded without downtime by adding more nodes on demand. We can add hundreds of servers with Kafka. The Kafka system does not go offline when additional servers are being added. Also, message handling occurs seamlessly and transparently within the Kafka cluster.
Durability
Messages are also never lost because of message replication, another durability factor. Data that is persistent throughout time without being lost is said to be durable. The replication method is used to achieve durability in Kafka. Since there are numerous copies of the data, the data in a given broker is recovered after the broker failure.
Low Latency
It can process data stream signals with extremely little latency in the millisecond range. As a result, it takes the consumer relatively little time to fetch a record that is produced in Kafka. Kafka uses data batching, compression, and partitioning to deliver exceptionally low latency.
High Throughput
Kafka can handle high-volume data. Kafka supports high velocity of message throughput, i.e., hundreds of messages per second. Millions of concurrent messages can be processed at extremely high throughput on an adequately built Kafka cluster.
Real-time Processing
With Kafka's distributed messaging mechanism, data is published by producers and received in real-time by consumers. For any application, a real-time data pipeline is essential. Real-time data pipelines, including those for storage, processing, and analytics, are simple to construct using Kafka.
Fault Tolerant
Kafka is designed from the ground up to be resilient to node/machine loss within a cluster. Kafka Streams will automatically restart the process that was operating on a failing device in one of the application instances that are still up and running.
Integration
More than 120 already prepared connectors are available from open source creators, partners, and ecosystem businesses through Kafka Connect. It enables us to integrate with third-party apps. Examples include integration with Google BigQuery, MongoDB, ElasticSearch, Redis, Azure Cosmos DB, AEP, SA etc.
Benefits of Kafka
The benefits of kafka are as follows:
Real time Analytics
Providing organisations with real-time analytics for commercial logistics is one of the most well-liked uses of data streaming technologies.Apache Kafka has been widely adopted across a variety of projects with a variety of objectives of real-time analytics. It also provides various third party integration to provide efficient real-time analytics.
User Friendly
It offers a simple user interface that makes it simple to view both the messages that are stored in the cluster's topics and the objects that are part of a Kafka cluster. There can be numerous consumers queuing to handle messages. When integrating with many consumers, just one integration needs to be established making the Kafka system more user friendly.
High Speed Performance
In multi-node clusters that can be distributed across different data centres, Kafka creates a data processing system. When providing real-time data through streaming data architecture, Kafka performs better than other technologies with lower latency.
Reliability
Kafka is regarded as being more reliable when compared to other messaging services. It offers a simple messaging system that makes it simple to view both the messages that are stored in the cluster's topics and the objects that are part of a Kafka cluster. Kafka offers resistance by replicating data in the event of a machine failure.
Storage Buffer
The clusters that are a unique group of servers that come with Apache Kafka guaranteeing the system won't crash when a real-time data transfer is taking place. Kafka serves as a buffer by removing data from source systems and switching it to the target systems.
Extreme Concurrency
Kafka has the capacity to process thousands of messages per second. It handles the data streaming in low latency and high throughput situations. Furthermore, it allows for high concurrent reading messages by consumers and sending of messages into it by producers.
Security
The Apache Software Foundation, which oversees Kafka and provides the framework for peer-reviewed security. With the help of Confluent Server Authorizer, audit logs offer a mechanism to record, safeguard, and retain authorisation activities into topics in Kafka clusters. It enables the tracing of cloud occurrences to implement security policies that shield networks.
Frequently Asked Questions
Can we use Kafka with a small amount of data?
Traditional message queues, like RabbitMQ, can be used for relatively smaller data collections. Kafka is overbearing if you only need to process a limited number of messages per day.
What cannot be done with Kafka?
When performing ETL tasks, avoiding utilising Kafka as the processing engine is advisable. However, you can also utilise third-party tools that integrate with Kafka to get more powerful features for real-time analytics.
What is the primary goal of Kafka?
Real-time data streaming applications and pipelines that adapt to the data streams are typically built using Kafka. It mixes interactions, storage, and stream processing to enable storing and analysing historical and real-time data.
What types of data are supported by Kafka?
We need tools to manage enormous volumes of data as it continues to rise daily. Modern data streams require a vital tool called Kafka. It has a million message processing capacity per second, or billions of messages each day.
Conclusion
In this article, we extensively discussed the Key Features and Benefits of Kafka. Apache Kafka is an open-source distributed event-streaming platform for processing data in real-time.
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