A Kafka service refers to a cloud service offering of Apache Kafka, a data streaming platform.
Apache Kafka is complex to deploy at scale, especially across a hybrid cloud environment. That’s why many streaming data users often opt for a Kafka service, in which infrastructure and system management is offloaded to a service provider.
Apache Kafka is an open source, distributed data streaming platform that can publish, subscribe to, store, and process streams of records in real time. It is designed to handle data streams from multiple sources and deliver them to multiple consumers.
Built to handle massive amounts of data, Apache Kafka is a suitable solution for enterprise applications. Apache Kafka is designed to manage streaming data while being fast, horizontally scalable, and fault-tolerant.
Apache Kafka is well suited for big data challenges. In many data processing use cases, such as the IoT and social media, data is increasing exponentially, and may quickly overwhelm an application you are building based on today's data volume.
For developers working with microservices, Apache Kafka is a great option when using asynchronous event-driven integration—which can augment the use of synchronous integration and application programming interfaces (APIs).
Streaming data is the continuous flow of real-time information, often represented as a running log of changes or events that have occurred in a data set.
Data streaming use cases can involve any situation that demands a real-time response to events—anything from financial transactions to Internet of Things (IoT) data to hospital patient monitoring.
Software that interacts with streaming data makes it possible to process data the moment it arrives, often using the event-driven architecture model.
With an event streaming model, event consumers can read from any part of the stream and can join the stream at any time. A basic data streaming event includes a key, a value, and a timestamp. A data streaming platform ingests events and processes, or transforms the event stream. And event stream processing can be used to find patterns in data streams.
For all its benefits, Apache Kafka can be challenging to deploy at scale in a hybrid cloud environment. Streaming data services can have more stringent requirements than other data applications.
Data streams must deliver sequenced information in real time, and must be consistent and highly available. The amount of raw data in a stream can surge rapidly. Streams need to prioritize proper data sequencing, data consistency, and availability, even during times of high activity. Streams also must be designed for durability in the event of a partial system failure.
Across a distributed hybrid cloud environment, a streaming data cluster demands special considerations. Apache Kafka data brokers are stateful and must be preserved in the event of a restart. Scaling requires careful orchestration to make sure messaging services behave as expected and no records are lost.
These challenges are why many Apache Kafka users opt for a managed cloud service, in which infrastructure and system management is offloaded to a service provider.
Some of the benefits realized from using a Kafka service include:
- Infrastructure management is taken care of, so teams can instead focus on app development and other core competencies.
- Faster application velocity, as teams can begin developing immediately and implement new technology quickly.
- A large ecosystem of additional cloud services, which can also simplify the delivery of stream-based applications.
- Connectors that link Kafka brokers to distributed services, making it easy to consume and share streaming data between applications and systems.
- Consumption-based pricing, allowing customers to pay for what they need when they need it.
And when run on a managed Kubernetes platform, Apache Kafka clusters can span across on-site and public, private, or hybrid clouds, and use different operating systems.
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