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:
with the likes of:
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.
Kafka Developers Hiring Guide
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:
There are several very important benefits to employing Kafka, each of which should have considerable appeal to your business.
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:
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.
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 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.
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