Kafka

Real-time data streams require scalable services

One thing that can’t be denied is that your company depends on data. Not only do you use it to make crucial decisions for marketing, planning, and product development, so many of your applications and services depend on that data to function.

Making that data available to your software means you can extend the functionality such that it can serve many needs. From stock trading, fraud detection, data integration, and real-time analytics. In fact, the sky’s the limit when you have the right glue to join together your applications and your data.

And with a platform like Apache Kafka, you can create continuous streams of data between apps such as:

Web apps
Mobile apps
Desktop apps
Microservices
Monitoring
Analytics

with the likes of:

Apps
Social network feeds
NoSQL databases
Relational databases
Data warehouses
Analytics

Apache Kafka is capable of handling tasks like publishing, subscribing, storing, and processing data.

At its heart, Kafka is a distributed streaming system that is used to both publish and subscribe to data streams. With fault-tolerant storage, Kafka replicates topic log partitions across multiple servers and allows applications and services to process records as they occur. And because Kafka batches and compresses records, it enjoys an incredibly fast I/O, so it can stream data into data lakes, applications, and even real-time stream analytic systems.

To achieve this level of speed, Kafka enables in-memory microservices, which makes it possible to build real-time streaming applications, replicate data between nodes, re-sync nodes, and even restore data states.

So, if you’re looking to enable your business for the “always-on” consumer, where constant data delivery and automation are key, Kafka might be your answer.

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Kafka Use Cases

At this point, you’re probably thinking, “What can I use Kafka for?” The answer is, “Plenty.” With a skilled team of developers (who can work with the likes of Java, Scala, Python, .NET, Node.js, PHP, and Ruby), Kafka can be put to use for tasks like:

Real-time payment processing and other financial transactions.
Real-time shipment tracking and logistics.
Capture and analyze sensor data from IoT and embedded devices.
Real-time customer interactions, such as ordering and booking.
Real-time hospital patient monitoring and prediction.
Connecting departments, divisions, and warehouses for a single company.

Think of Kafka this way: If you need real-time interaction between data sources and applications or services, this open-source layer is the best on the market.

Kafka uses 3 important features for event streaming:

Ability to read and write streams of events (which includes the import and export of data from other systems.
Ability to store streams durably and reliably.
Ability to process streams as they occur.

Benefits of using Kafka

There are several very important benefits to employing Kafka, each of which should have considerable appeal to your business.

Scalable storage

Kafka is one of the best systems on the market for storage and retrieving records and messages. One feature that benefits enterprise businesses is Kafka's ability to scale. Kafka replicates all records to servers for fault tolerance. And because Kafka Producers (which serialize, partition, compress, and load balance data across brokers based on partitions) can wait on the acknowledgment of a record stored, the system is not only scalable but reliable.

In this instance, the producer doesn’t complete the write of data until the message replicates. This structure scales incredibly well, especially when combined with modern disks that have very high IO throughput with large batches of streaming data.
Record retention

Another feature that has a high appeal for businesses is Kafka's ability to retain all published records. Unless your admins/developers set limits, Kafka will keep every record until it runs out of storage. Limits can be set based on time, size, or compaction, which means your Kafka developers and admins can set flexible record retention policies.

How does Kafka work?

Kafka is deployed (via bare metal, virtual machines, or containers and either on-premises or to a cloud hose) as a distributed system, consisting of servers and clients. The servers are a cluster of machines that can span multiple data centers or cloud regions and act as either the storage layer (brokers) or run Kafka Connect for the importing and exporting of data as event streams.

Kafka Clients allow your developers to create applications and microservices capable of reading, writing, and processing streams in parallel. Out of the box, Kafka ships with a limited number of clients, but there are plenty of community-created clients for Java, Scala, Go, Python, C/C++, and REST APIs.

It’s also important to understand what an event (also called a record or a message) is. Kafka records events when something happens. Each event has a key, value, timestamp, and an optional metadata header, which might look like:

Event key: "Client X"
Event value: "Purchased Item A"
Event timestamp: "May 28, 2021 at 12:32 p.m."

Events are stored in topics, which is like a folder in a standard computer filesystem. You could have folders for payments, clients, customers, divisions, warehouses, products, or services. Events within these topics can be read as often as necessary and are never deleted (unless your admins have configured retention policies and an event meets the requirements of deletion in a given policy).

Topics are partitioned, so they are spread out in buckets on different Kafka brokers. This partition scheme makes Kafka incredibly scalable as clients can read and write data to and from multiple brokers simultaneously.

Conclusion

Should you be using Kafka? The answer is simple: If you need scalable, real-time streaming data for applications and services, Kafka should probably be the first platform you look at for this purpose. But given its complexity, you should seriously consider turning to a nearshore or offshore development firm if you don’t have a highly skilled development team on staff. Those companies can put together the perfect team to implement this service and help you leverage all of Kafka’s benefits.