Mar

Apache Kafka Use Cases: Tips on When to Use It & When Not to Use It

Apache Kafka is a leader in real-time data streaming, securing the top position in the industry with a 38.49% market share. But, as thousands of customers rely on Apache Kafka’s event-driven architecture, the question that arises isn’t whether to embrace Kafka. But instead, the question is when to harness its capabilities and when to find the alternatives.
Even when Kafka can handle enormous amounts of data efficiently, it is not a one-fit-size-for-all solution. This blog highlights the reasons why you should use Apache Kafka and when you should not.
But, first, let’s understand the concept and components of Apache Kafka

What are the key Components and Concepts of Apache Kafka?

Kafka receives streams of events from data producers and organises them chronologically in partitions across multiple servers called brokers, forming a cluster. Each event record includes a key-value pair, with timestamp (optional) and header information. Records are grouped into topics, and data consumers retrieve the data they need by subscribing to specific topics. Let’s understand each one in detail:
Key Components and Concepts of Apache Kafka

Event: An event is a message containing information about an occurrence. For instance, when a new user signs up on a website, a registration event is generated, containing details like the user’s name, email, password, location, etc.
Topics and Partitions: In Kafka, data streams are organised into topics, which act as channels for publishing and subscribing to data. Each topic can accommodate several producers and consumers. Topics are divided into various partitions for parallel processing and data distribution, with these partitions replicated across multiple brokers to ensure fault tolerance.
Producers and Consumers: Producers send data to Kafka topics, originating from different sources like applications or systems. Consumers, which can be applications or services, read and process data from Kafka topics, subscribing to one or more topics for real-time data updates.
Brokers: Kafka brokers are essential in the Kafka cluster, storing and managing data records. They act as the central hub for communication between producers and consumers. Kafka clusters can have several brokers to enhance scalability and fault tolerance.
Streams and Connect: Kafka provides Kafka Streams for stream processing and Kafka Connect for building connectors to link with external data sources and sinks. It is widely used for Kafka real-time streaming, log aggregation, event sourcing, data integration, complex event processing (CEP), change data capture (CDC), and other applications.
Expand the cluster: To scale the cluster, simply add entries like server.3=server3IPAddress to each server zookeeper properties file.
Start Zookeeper: After modifying the configuration files, initialise Zookeeper on each server with the appropriate command.

What is the main use case of Apache Kafka?

Hackers often target APIs for several reasons:

Abundant Attack Surface: APIs typically expose a large attack surface, providing hackers with numerous entry points to exploit vulnerabilities and gain unauthorised access to sensitive data or system resources.
APIs are Rich Data Sources: APIs often handle valuable data, such as personal information, financial data, or business-critical information. APIs are the favourite thing of hackers because hacking them can give them access to valuable information for malicious purposes.
Ease of Automation: APIs are designed to facilitate automated interactions between systems, making them attractive targets for automated attacks. Hackers can leverage automated tools and scripts to probe, exploit, and manipulate APIs, increasing the efficiency and impact of their attacks.
Third-party Integrations: Many applications rely on third-party APIs for functionality or data access. Hackers may target these APIs as a means to compromise the security of multiple interconnected systems or to exploit weaknesses in third-party services.
Inadequate Security Measures: Most of the APIs are not secure due to poor authentication mechanisms, insufficient access controls, or lack of encryption. Hackers exploit these security weaknesses to bypass defences and gain unauthorised access to sensitive data or system functionalities.

Apache is used for Activity Tracking:

Originally, Kafka was for LinkedIn to reconstruct its user activity tracking system into real-time publish-subscribe feeds. This is crucial for handling high volumes of activity messages, including user clicks, registrations, likes, time spent on pages, orders, environmental changes, and more. These events are for dedicated Kafka topics and you can use it for various purposes, such as loading into a data lake or warehouse for offline processing and reporting.

Example: Imagine an e-commerce platform using Kafka to track user activities instantly. Each activity, like product views, cart additions, purchases, reviews, and search queries, becomes an event published to specific Kafka topics. Kafka microservices use these events or store them for recommendations, personalised offers, reporting, and fraud detection.
Use case of Apache Kafka

Real-time Data Processing: Kafka facilitates the swift transmission of data, achieving very low latency (e.g., 5 milliseconds), ideal for various applications:

Financial organisations: Process payments, detect and block fraudulent transactions instantly, and update market prices on dashboards in real time.
Predictive maintenance (IoT): Models analyse field equipment metrics, triggering alarms immediately upon detecting deviations that may indicate imminent failure.
Autonomous mobile devices: Enable real-time data processing for navigating physical environments.
Logistical and supply chain businesses: Monitor and update tracking applications. This includes apps such as keeping real-time tabs on cargo vessels for precise delivery estimates.

Example: Imagine a bank using Kafka to handle transactions instantly. Each customer-initiated transaction will be an event and is live on the Kafka topic. An application then consumes these events, validates and processes the transactions, blocks any suspicious ones, and updates customer balances in real time.

Messaging: Kafka serves as a strong messaging system for real-time communication between applications, useful in chat, notifications, and handling the large amount of data produced by IoT systems.

Example: Imagine a taxi app with microservices using Kafka to exchange messages between services. When a rider books a ride, the ride-booking service sends a message to the driver-matching service through Kafka. In near-real time, the driver-matching service finds a nearby driver and sends a message back.

Log Aggregation: Kafka acts as a central hub for storing logs from various services and applications. This simplifies log analysis, debugging, and troubleshooting, making it popular for DevOps and system monitoring.

Example: Imagine a big company with many systems using Kafka to gather logs. A log analysis tool or security system can then use these logs for troubleshooting, security monitoring, and compliance reporting.

When not to use Apache Kafka?

It is a powerful tool for handling real-time Kafka data streaming and processing, but in some situations, it lacks its capabilities.

Simple or low-volume data processing: If your data processing needs are simple or your data volume is low, using Kafka might be overkill. Simpler tools or traditional messaging systems could be more appropriate and easier to manage.
Highly synchronous or transactional applications: You can use Kafka in for high-throughput, low-latency, and asynchronous data processing. If your application needs strong transactional guarantees, technologies like traditional message queues or databases will be more suitable.
Limited resources: Running and managing Kafka clusters require significant resources in terms of hardware, infrastructure, and operational expertise. If you have limited resources or budget constraints, Kafka might not be the most cost-effective option.
Small-scale deployments: Setting up and maintaining Kafka clusters can be complex, especially for small-scale deployments. However, you can choose simple alternatives if scalability is not the issue.
Real-time analytics with complex processing: You can use Kafka for real-time data streaming. However, for complex analytics, you need additional technology of frameworks such as Apache Spark or Apache Flink.

Conclusion

Apache Kafka is like a strong force that handles real-time data streaming—it’s powerful. Utilise it for substantial data transfers, log streamlining, message processing, and operational metric tracking. However, carefully consider alternatives for less demanding tasks. Understanding these details helps businesses use Kafka in the best way possible for their data streaming plans.
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