Hire a Kafka Developer
The Roles and Responsibilities of a Kafka Developer
It isn’t enough to process data volumes faster – you should also be able to render insights in real-time to react and adapt to changing business conditions.Typically, a Kafka engineer helps with the architecture, implementation, and overall design of the Kafka clusters. Kafka engineers also provide continuous support to ensure daily operational maintenance goes smoothly.
Mostly, Kafka developers manage Kafka-based cluster environments and massive scale multiple nodes on AWS. Kafka developers also take care of Kafka environment builds that involve capacity planning, performance tuning, cluster setup, and continuous monitoring.
Kafka engineers offer high-level operational maintenance and make upgrades for the Kafka clusters on a day-to-day basis. In addition, they create key performance metrics and keep an eye on the entire cluster’s health. It is their responsibility to plan and perform new software and hardware upgrade releases for the main storage infrastructure.
Advantages of Hiring a Kafka Developer
Kafka developers help organizations maintain data flow in real-time between target and source. One major perk of hiring a Kafka engineer is that organizations can ensure persistent data in defined configurations.
FAQs About Hiring Kafka Developers
When it comes to traditional recruitment practices, the High5 platform cuts out the friction. It is ideal for helping organizations join forces with some of the most gifted big data developers. Even better, High5’s platform also screens and vets Kafka engineers to ensure a smooth transition.
Gone are the days when businesses had no choice but to spend a lot of money to hire the right candidate at the right time. With the High5 platform, organizations can speed things up and streamline the entire developer recruitment process.
Kafka refers to a framework implementation of a dedicated software bus through stream processing. The open-source program is written in Java and Scala with the development of Apache Software. The objective of Kafka is to generate real-time unified data feeds. Essentially, Kafka is a fault-tolerant, scalable, message scribe system that makes it possible to develop distributed applications.
Kafka involves a producer and a topic to work around data. Spark, on the other hand, offers platforms to pull, process, and hold data straight from the source. Furthermore, Kafka offers a real-time window and stream process in contrast to spark that provides real-time batch and stream process.
While you cannot roll out a transformation in Kafka, Spark allows you to perform ETL. Unlike Kafka, Spark also supports different libraries and programming languages. Practically, the use of Kafka makes sense for real-time streaming between the target and the source.