Source: Google Help
Real-time data processing is a critical element for many organizations. It allows them to better serve clients and make critical business decisions instantaneously.
Kafka also doubles as a message broker that facilitates communication between different applications. It receives and stores event messages in a queue. The queue links the messages to consumer applications, similar to other message brokers like RabbitMQ. However, unlike RabbitMQ, Kafka segregates its messages into topics based on a message key, which consumers can use for filtering relevant messages.
Kafka collects operational metrics from different applications in a microservices architecture. These metrics generate key performance indicators (KPIs) for application monitoring.
Kafka can collect log files from multiple systems and place them in centralized storage. Applications can also be configured to stream logs directly via Kafka as messages.
These messages can then be stored in a file on disk. Moreover, the multiple log files can be transformed into a more straightforward form for cleaner interpretation.
Best Use Cases for Kafka in Different Niches
Real-time processing has opened up several new opportunities in different industries. Business leaders leverage Kafka for revenue generation, customer satisfaction, and business growth. Let’s discuss a few niches that are using Apache Kafka well.
The financial sector generates data in the count of millions daily. The sheer amount of financial transactions and the volume of customers is too much for conventional systems to handle. Apache Kafka handles all business-critical and high-volume workloads, ensuring customers get a seamless experience. Moreover, banks and other financial services use it for generating real-time analytics and powering machine learning models for applications like fraud detection.
Some popular financial services using Kafka include
ING — Began with powering a fraud-detection system and soon expanded to multiple customer-centric use cases.
Paypal — Handling about 1 trillion messages per day.
JPMorgan Chase — Powers monitoring and administrative tools, allowing real-time customer handling and decision-making.
Forming aggregated analytics can be cumbersome when running marketing campaigns across multiple platforms. Kafka can build connections to multiple platforms like Google, Facebook, Twitter, or LinkedIn. It can gather marketing data as the user interactions are active and use this real-time information to form analytics. The low latency system can help business leaders and marketing experts plan their future campaigns without delay.
Similar to this Apache Kafka use case is our advertising analytics solution. It aggregates your data from multiple advertising platforms. With built-in connectors for Google, Facebook, and many other platforms, DoubleCloud offers instantaneous analysis for all your marketing needs.
Start-ups or growing e-commerce businesses face thousands of orders every hour and are challenging to handle. Swift response and efficient customer management are key to running an online shop. However, this becomes difficult when your tech infrastructure needs to keep up with the website traffic.
Kafka streamlines the communication between the customer and the shop owner and the robust pipelines ensure that all events, including orders, inquiries, and cancellations, reach the user within a minimum time. This allows the business owner to respond in near real-time and maintain customer satisfaction. Kafka also helps gather real-time analytics regarding business performance.
The telecommunications industry uses Kafka for various purposes. It’s used for real-time data stream processing to detect anomalies and monitor network performance and it facilitates information integration from various data sources throughout the organization, such as call records, customer data, etc. However, the mainstream use case is supporting text messaging over a network and delivering it to your phone, tablet, or computer.
The healthcare industry benefits greatly from Kafka’s data streaming capabilities. It creates a seamless network of hospitals and clinics by building an uninterrupted communication and data transfer channel.
This universal network allows users to construct healthcare-related analytics using data from various sources. It also assists knowledge sharing across institutes that impact research quality and reduces the time for medical breakthroughs.
Internet of Things (IoT)
A typical IoT infrastructure includes several electronic devices, a backend engine for processing and storage, and a network web for communication. Each device in this infrastructure is in constant communication with the other, sharing data that is vital for operation.
Imagine an agriculture field with several sensors spread across it. Some measure temperature, some humidity, while others keep track of the constituents of the soil. Each of these transmits this data back to a back-end server every second. The back-end server might generate analytics or use this data for machine-learning forecasts.
Kafka supports this back-and-forth communication between the devices by building a persistent channel. It gathers data from all the various sources and transports it to a centralized database. Kafkas message queue ensures the messages remain in the order sent for sequential processing.
The gaming industry has experienced exponential growth in recent years, generating $300 Billion in revenue in 2022. The gaming industry accommodates millions of players worldwide, allowing them to play against each other in real-time.
Apache Kafka allows fast communication between different servers and users, offering players a low-latency experience during gaming. The real-time event streaming capabilities benefit analytics and machine learning applications like cheater detection. The stream data can be used with platforms like our gaming analytics. DoubleCloud helps gaming companies collect telemetry data and efficiently store it in a data warehouse. Additionally, with DoubleCloud visualizations, users get an overview of performance metrics and business analytics.
The streaming pipelines ensure any events, such as player position changes, are instantaneously transmitted to the entire player base. Kafka scalability also allows for accommodating a growing number of users which is crucial considering the growth of the gaming industry.
When Not To Use Kafka…
Despite its numerous benefits, Kafka isn’t a one-size-fits-all solution. There are many scenarios where Kafka’s capabilities might be overkill, and the configuration efforts might just be a needless overhead. Below are some cases where Kafka might not be needed, however if you’re not sure, our Solution Architects will always be happy to discuss your individual requirements.
Small-Scale Data Processing
Kafka’s charms might fool some people into believing it is the ultimate data processing solution; however, Kafka is best for companies facing millions of requests and messages per day. For anything less, it is better to revert to other broker services like RabbitMQ.
We’ve discussed Kafka’s quick message transmission system but it has its limitations for hard real-time situations. While its scalable system is great for gaming experiences, where occasional latency spikes are not a deal-breaker, Kafka is not advised in mission-critical scenarios requiring a strictly zero latency system.
Tight Integration With Legacy Systems
Integrating Kafka with a large-scale legacy system can be quite a hassle. The setup requires several Kafka experts, and building the end-to-end architecture can take up to months. It is better to operate with conventional methods and data pipelines or look for a managed Kafka solution.
How DoubleCloud Helps Manage Apache Kafka
Setting up an Apache Kafka cluster is a challenging task and requires seasoned experts. DoubleCloud takes this hassle away from users with our Managed Kafka service. With Managed Kafka, users enjoy a fully-managed Kafka environment with a user-friendly UI.
The service manages Zookeeper brokers and clusters, configuring AWS clusters and versioning. Moreover, all communications are TLS-secured, and data can be directly dumped into ClickHouse for real-time analytics.
Apache Kafka is a fantastic tool that allows event producers and consumers to communicate seamlessly via a message queue. It offers several benefits to modern-day systems, such as
Gathering log details across distributed systems
Real-time tracking of user activity on the website
Gathering data for real-time analytics
Help applications communicate in a microservices architecture
Kafka offers a fault-tolerant and scalable system that can accommodate several thousand users and process millions of messages daily. However, it is not necessarily the right fit for every situation.
Kafka should be avoided when
Handling a small user base (only a few thousand messages per day)
There is no flexibility for communication delays or latency spikes
Working with large-scale legacy systems