Companies and systems inevitably generate progressively more data which allows them to gain valuable insights into customer behavior, market trends, financial predictions, and more. Analyzing Big Data, leveraging data engineering, and tapping into the Internet of Things (IoT) gives businesses unlimited intelligence about industry prospects. However, a new problem emerges: how do you handle and process such incredible amounts of data? The market needed a better system to deal with increasing data volumes without jeopardizing app performance and customer experience. Apache Kafka might be the solution to this predicament. In this article, we’ll discuss the main functionalities of the system, learn Apache Kafka use cases and benefits, and try to identify whether this tool is suitable for you.
Apache Kafka is an open source streaming data platform created by LinkedIn in 2011. In the abundance of data and event records being generated by people, apps, and gadgets every day, storing and processing such data has become increasingly difficult. An event occurs when an action is triggered by another action such as order placement or temperature scan. These streams of data and events allow companies to build applications that utilize real-time data to gain insights and offer various services. A streaming data platform like Kafka is a tool for developers to take advantage of streams in high volumes and at fast speeds.
Before the Kafka streaming platform emerged, data was captured and processed at random time intervals which jeopardized the accuracy and precision of data and event records. This LinkedInoriginated tool collects data in real time and stores it as an immutable commit log which guarantees the safety and accuracy of the information. To put it simply, Kafka has three main tasks: subscribing to data streams, processing information in real time, and storing said data in chronological order.
In order to understand how the system operates, we need to introduce you to the main concepts of Apache Kafka as well as basic terminology.
The elements that constitute a Kafka cluster include Brokers, ZooKeeper, Producers, and Consumers. Kafka brokers are servers that can handle many data volumes per second without impacting performance in a negative way. Working together, Kafka brokers provide load balancing and reliable redundancy to ensure the system operates properly. To manage and coordinate a cluster, brokers utilize ZooKeeper. This element monitors the health of the broker system and decides which server performs as a lead. ZooKeeper also notifies the entire cluster whenever a change occurs. Also, Kafka has producers that publish events to brokers as well as consumers that read and process such events.
Additional components of the Kafka machine include topics, partitions, consumer groups, and replication. Kafka topics are channels for streaming data that producers can use to publish messages and consumers to receive them. Topics are then separated into partitions that capture data in an immutable sequence. Consumer groups are collectives of consumers who share the task of reading and processing partitions. Finally, topic replication is a safety measurement system that allows developers to make reliable deployments. In case of a broker failure, you still have access to topic replicas and can continue with development.
At the core of the operation, there are five functions: publishing, consuming, processing, connecting, and storing. First of all, Kafka allows data sources to publish a data stream into the corresponding topic. For example, an IoT device publishes the monitored temperature in the warehouse to ensure appropriate storage conditions. The second function allows applications to subscribe to a Kafka topic in order to consume the data stream and process the gathered information. Then, the system processes the incoming data from multiple topics and directs the renewed streams to other topics. Next, you can wire the producer and consumer data streams to existing applications. Lastly, Kafka can serve as durable long-term storage as well as a reliable source of truth.
Sometimes, Apache Kafka can be confused with an Extract, Transform, Load (ETL) tool which is not fully accurate. Although the platform does have some features of such a solution, it’s a lot more than just that. ETL tools can transport typically transactional data from one system to another and are not capable of handling large amounts of information. Unlike ETL platforms, Kafka can deal with multiple data types, including IoT, gaming, and mobile apps. Most importantly, Apache Kafka is designed to work with real-time data. In other words, even though the system can perform some ETL duties, it was not built for this task.
Another generalization argues that Kafka is a database. Although it does have some qualities of a database and provides the main properties of one, you cannot refer to it as a full-fledged database. The Apache Kafka streaming platform does offer long-term storage solutions, but they are meant to enhance the main functionality of the system.
The main goal of the Kafka solution is to streamline software development for event-driven applications. It achieves this by allowing for data collection, distribution, and long-term storage as well as offering real-time data stream access and processing. In this part, we will investigate the most significant advantages of Apache Kafka.
Thanks to Apache Kafka Streams, you can capture and process data with minimal delay and immediately react to a change or customer inquiry. Not only do you get access to real-time data relating to your business such as orders, cancellations, inventory, and more, but also additional information including social media mentions, search queries and recommendations. All these features allow companies to be more responsive to their client’s needs and win over new audiences.
Through Kafka’s event-driven microservice architecture, companies gain access to a scalable platform that allows them to seamlessly grow and shrink. This benefits both the developmental side as well as the business perspective.
Kafka stores and separates data streams in immutable partitions, thus eliminating any human error or oversight. No matter how much you move your data between applications, you will always have a well-preserved and chronologically-structured single source of truth.
When collecting data from multiple sources, developers conventionally had to write code for these integrations separately, which is time-consuming and tedious. Working with Kafka allows you to create a single integration with the system for producers and consumers.
Your employees can access data they require to complete their tasks through a single source as opposed to logging into multiple applications. Moreover, they will have a full overview of the information in conjunction with other important pieces of data such as website interactions or financial insights.
Being open source and available for modifications and extensions, Kafka has a limitless influx of enthusiastic contributors who continuously come up with new features, libraries, plugins, and other tools. You can benefit from this growing community and be sure that the platform is not going to decay any time soon.
The Apache Kafka solution provides a myriad of helpful use cases that can automate and accelerate your development cycle as well as ensure a stable and reliable application. Let’s dive into the most vital use cases that the platform has to offer. So, what is Kafka used for?
The original purpose of the platform, user tracking features had to be later reconstructed to fit into the publish-subscribe data streams. Each user-related event such as clicks, likes, orders, etc., is recorded and published in the respective Kafka topic for further processing, analysis, and distribution. The data can be stored in data lakes or warehouses for offline, in-depth analytics and reporting. User activity tracking is also referred to as website activity tracking.
Kafka can also be utilized to garner a comprehensive overview of operational metrics across the organization. The system accumulates data from distributed applications to generate a consolidated feed of operational KPIs and statistics.
Log management becomes progressively more difficult with time, a problem that can be solved by Kafka’s centralized logging system. Acting as an external commit log, Kafka allows for data replication which preserves information and eliminates detrimental data losses. The system can also be adopted as an external data repository for companies that are unsatisfied with their current log aggregation solution.
Companies whose products depend on real-time data processing will largely benefit from Kafka implementation. Using this platform, your data will be processed the moment the event occurs, which allows businesses to instantaneously react to changes. For example, an online payment application will collect and process payments in real-time with almost no delays as well as immediately halt a fraudulent transaction before it hurts their business or customers.
Thanks to Kafka’s fast throughput, replication features, and fault tolerance, the platform can serve as a reliable message broker that guarantees message exchange between applications and systems. Especially for products that rely on low latency, Kafka can be used as a great alternative to traditional message brokers.
Another prominent example of Apache Kafka use cases is the Internet of Things. The platform can act as the central hub for data collection and distribution from multiple IoT devices. On top of that, Kafka will automatically scale up or down if your device count changes without jeopardizing any systems.
Apache Kafka is a great solution for microservices as it can facilitate flawless and real-time communication between the containers. The adoption of Kafka for the microservice architecture application will allow you to build a robust and scalable product.
RabbitMQ is a message broker that deals with message delivery. Often compared to each other, Kafka and RabbitMQ do have quite a few substantial functional differences that we will explore in this part.
While RabbitMQ is exceptional in handling transactional data that includes order placement or user requests, Kafka is best for operational data streams such as auditing and system activity.
In RabbitMQ, messages are removed immediately after a client acknowledges them, whereas in Kafka, messages remain in the queue for a predefined time period that cannot be changed by the consumer.
RabbitMQ offers the so-called push-based model that incorporates a “smart” producer who decides when to push data forward. This approach distributes messages evenly among all consumers and allows for a moderate workload. In turn, messages are not always processed in order, which can create some delays. Kafka uses an alternative approach called the pull-based model which offers a smart consumer as opposed to a producer. This model forces the consumer to request messages at certain intervals. Using the pull-based approach, the message log always remains in order.
As mentioned above, prominent Apache Kafka use cases include real-time data processing, user activity tracking and log aggregation. However, RabbitMQ can be a perfect platform for complex routing scenarios that require transferring data across multiple applications. Another use case is file scanning, which RabbitMQ makes easier due to the microservice architecture feature. You can submit the file to the platform to identify viruses and file information before sending it out to other users. Image scaling can also be achieved by using RabbitMQ. For example, real estate agencies can utilize this feature to automatically upload images in the right size.
Being able to handle up to a million messages per second, Apache Kafka shows better performance results compared to RabbitMQ whose throughput lies around ten thousand messages per second.
Apache Kafka has been growing in popularity to the point that more than 80% of 100 Fortune companies have entrusted their event streaming needs to this platform. The biggest businesses in the market like Tinder, Uber, and Netflix prefer Kafka to its competitors.
Tinder is a dating app that generates nearly 90 billion events on a daily basis and requires a robust solution to handle such load. Tinder processes such as notifications, user activation, content management, analytics, and many others are backed up by Apache Kafka Streams.
Another platform with a huge audience and an immense number of daily events such as pins, shares, likes, etc., Pinterest also utilizes Kafka to manage various processes including recommendations, content indexing, and even ad budgeting.
An app that handles a whopping trillion events every day, Uber calculates data volume in petabytes! Subsequently, the platform requires a reliable system to manage the processes and keep data intact.
Netflix is another giant that finds Kafka an extremely useful tool to handle billions of events on a daily basis. In fact, the system has become such an integral part of the company that it is referred to as Nextflix’s Keystone data pipeline.
No matter how great a tool or service may be, you will only derive benefits if you find the use for it for your company and product. We have already touched on what Apache Kafka is used for, and in this section, we’ll identify the cases in which you should opt out of using Kafka.
Unless you are handling substantial amounts of data, Kafka might be an overkill for your business. For smaller products that process a few thousand events every day, consider using RabbitMQ.
It’s not recommended to use Kafka for processing ETL tasks as you will be forced to create an additional pipeline of interactions between producers and consumers.
Although Kafka topics do resemble some blockchain features like log aggregation and immutability, it does fail to provide cryptographic data verification to constitute a secure blockchain.
To sum up, the Apache Kafka streaming tool offers great scalability and high throughput, supports multiple use cases, and lowers latency to milliseconds. As a result, companies that require real-time data processing can immensely benefit from the platform and respond to customers’ requests almost immediately. Even large applications that handle billions and trillions of events every day leverage Kafka to process their data streams. But smaller companies will also find the tool advantageous, especially the ones that might experience a sudden jump in activity like ecommerce or IoT.
All in all, if your company is currently handling or will handle large amounts of data, the Kafka streaming platform could be a helpful addition to your arsenal. Especially if you’re still growing, you can largely benefit from the scalability of the platform that will swiftly adjust to the increasing data volumes. To learn more about Apache Kafka and its advantages, reach out to NIX United, a team of professionals who will help you navigate the platform and unlock its benefits.
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