While the Internet of Things (IoT) is getting a lot of attention, the challenges that prevent widespread adoption still exist. The biggest hindrance to IoT being relevant in more industries is the ability to integrate devices and machines for processing data in real-time at scale. Apache Kafka addresses this issue by introducing scaled data streams for integrating and processing datasets.
If you are new to Apache Kafka, it would be interesting to know that it is a real-time streaming platform widely adopted in small and large organizations and multi-national corporations. Kafka’s architecture and publish/subscribe protocol makes it possible to move data between applications and enterprise systems in real-time.
Use Cases of Scaling Streaming Data using Apache Kafka
With the use of connected infrastructure, cars can communicate with each other via a remote datacenter. Audi has a simple implementation of the technology with its Audi Connect service. Drivers can get real-time traffic recommendations, updates on maintenance, and other personalized services.
Smart Home Automation
Smart cities and smart homes require efficient data management and control, which is made possible by Apache Kafka. Solar energy solutions can also be integrated to offer a more comfortable and sustainable lifestyle.
Apache Kafka can offer real-time integration between CRM, backend systems, loyalty systems, and customers. It enables the retail industry to offer curated promotions, better services to customers, and cross-selling. Target is one of the biggest brands in the retail sector, and they make use of smart retail solutions to offer targeted promotions to customers.
Industrial manufacturing is simplified by using robots and automated machines. But data management can be challenging in such industries without the right applications. Apache Kafka enables the use of predictive maintenance to ensure there is minimal downtime. In addition to machinery, modern-age manufacturers are also offering IoT-enabled digital subscriptions for repairs and maintenance.
How does Apache Kafka addresses common IoT challenges?
Apache Kafka’s development focuses on addressing common challenges faced during the implementation of the Internet of Things. One of the most significant advantages of Apache Kafka is its ability to scale data movement and processing. Kafka is capable of handling backpressure and processing high levels of throughput.
The development process via Apache Kafka is very agile, as all sources and sinks are put into decoupled domains. Apache Kafka makes data management easy even with different teams developing, maintaining, and changing integration between services and machines. The platform is designed to promote innovative development. Apache Kafka is compatible with all new and innovative technologies that require IoT implementation.
Some of the complications that still need to be addressed by businesses that want to adopt Apache Kafka include:
Complex operations and infrastructure
Sometimes, the hardware on existing systems cannot be integrated into an IoT ecosystem. It leaves businesses with either the option of replacing hardware or opting out of IoT altogether for a specific process.
Integration with multiple technologies
Businesses often struggle with adhering to proprietary and legacy standards while also adopting MQTT or OPC architecture. Better planning and updating of older technologies to make room for Apache Kafka’s implementation helps greatly. It is also important to upgrade network hardware as IoT networks require solid networking infrastructure.
Once these challenges are met, businesses integrate machines and tools with datacenters for better integration and data management.
Should you use Apache Kafka for scaling streaming data?
If you want a dynamic platform that combines data processing, messaging, and storage using reliable and secure infrastructure, Apache Kafka is for you. Most Kafka users also make use of Kafka Connect for better integration with the sink or source in an ecosystem.
Kafka Streams is a library for developing streaming applications; it allows continuous streaming. The streams are focused solely on the problem that it intends to solve. It balances the processing load whenever a new instance of the app is added or existing instance crashes. It recovers failure instances while maintaining local states for tables.
By using the library in your program, you can start multiple instances of the app at a single point in time. Kafka will automatically partition and balance the processing load over these instances.
Here are the top benefits of using Kafka for scaling streaming data:
- Event reprocessing
- High integration with enterprise tools
- Multi-cloud platform
- Long-term data storage and buffering
- Large scale
- High throughput
- Capable stream processing
As long as you can provide a stable network backed up by solid hardware for your IoT infrastructure, Apache Kafka is a great way to scale streaming data. By combining Kafka and MQTT, enterprises can have the perfect match in terms of scalability, reliability, and security in IoT.
Since the capability of Apache Kafka to act as high-performance data absorbing layer is remarkably significant, every skilled IoT developer must utilize it. It can assist the developers in multiple data-centric tasks and is highly useful beyond IoT endpoints. Kafka is efficiently used by business giants such as Twitter and Netflix for data ingestion, event processing, and real-time monitoring. Even at Softobiz, we are using Apache Kafka and other integrations for enterprise development. So, we can help you in making the best possible use of the technology.