Hire the best Apache Kafka developers

Apache Kafka is an open-source stream-processing solution developed by LinkedIn and later donated to the Apache Software Foundation. The software platform aims to provide a low-latency, high-throughput solution for processing real-time data feeds.

Apache Kafka uses the publish/subscribe messaging pattern common in distributed systems. Kafka instances typically exist as clusters of nodes called brokers that can receive messages from multiple producers (any apps sending data to the cluster) and deliver them to multiple consumers (any apps receiving data from the cluster). Producers publish messages to Kafka topics (i.e., categories of messages), while consumers subscribe to Kafka topics. It is through this topic categorization that the brokers are able to determine where messages need to be delivered.

Apache Kafka is a popular choice among developers looking to build message brokers, website activity trackers, and analytics pipelines that must deal with large volumes of real-time data from disparate sources.

The first step to determining the cost to hire an Apache Kafka developer will be to define your needs. Rates can vary due to many factors, including expertise and experience, location, and market conditions.

Cost factor #1: project scope

The first variable to consider when determining scope is the nature of the work that needs to be completed. Not all Apache Kafka development projects are created equal. Creating a simple log aggregator to collect log files off different servers into a central place for processing will typically take less time than building out a multistage data streaming pipeline for your SaaS (software-as-a-service) product.

Tip: The more accurately your job description describes the scope of your project, the easier it will be for talent to give you accurate cost estimates and proposals.

Cost factor #2: Apache Kafka developer experience

Choosing the right level of expertise for the job is closely tied to how well you determined the scope of your project. You wouldn’t need an advanced Apache Kafka developer to create your own custom site analytics dashboard using Kafka. On the other hand, building a large-scale enterprise messaging system will require the skills of a seasoned Apache Kafka developer.

Beyond experience level, you need to consider the type of experience the talent possesses. The following table breaks down the rates of the typical types of Apache Kafka developers you can find on Upwork.

Rates charged by Apache Kafka developers on Upwork

Cost factor #3: location

Location is another variable that can impact an Apache Kafka developer’s cost. It’s no secret that you can leverage differences in purchasing power between countries to gain savings on talent. But it’s also important to factor in hidden costs such as language barriers, time zones, and the logistics of managing a remote team. The real advantage to sourcing talent remotely on Upwork is the ability to scan a global talent pool for the best possible person for the job. Location is no longer an obstacle.

Cost factor #4: independent contractor vs. agency

The final variable regarding talent cost is hiring an independent contractor vs. an agency. An agency is often a “one size fits all” model, so you’ll often have access to a designer, a project manager, an engineer, and more. When hiring individuals you have total autonomy regarding who is responsible for which part of the project, but you’ll need to source each of those skills separately.

The trade-off between hiring individuals vs. hiring an agency is the level of administrative overhead you incur personally in coordinating tasks among all members of the team. Project scope and personal preference will determine which style is a better fit for your needs.

Understand your partition data rate limitations

In Kafka, messages are organized into topics that can be divided into a number of smaller partitions. Partitions allow your Kafka cluster to process the data in a particular topic in parallel across multiple brokers. This capacity for parallel processing is what enables Kafka to deliver high-throughput messaging.

Of course, even high-throughput systems are going to have their limitations. Messages sent to a partition exist in a log for a configurable period of time or until a configurable size limit is reached. Exceed that retention limit prematurely, and it’s possible you can start losing messages before consumers can pull them from the topic partition.

That’s why it’s important to understand the data rate of your topic partitions. Simply multiply the average message size times the number of messages per second to calculate your average retention rate. This will enable you to figure out how much retention space is required to guarantee data is retained for the desired period of time.

Widen those consumer socket buffers for high-speed ingestion

The default settings for consumer socket buffers tend to be around 100 KB (Kafka 2.4.x), which is too small for high-throughput environments. For low-latency, high-bandwidth networks (10 Gbps or higher), it might be necessary to bump those values up to 8 or 16 MB. You can tune the socket buffer setting for consumers with the “socket.receive.buffer.bytes” parameter.

Tune your memory buffer and batch sizes for high-throughput producers

On the producer side of the equation, high-throughput environments will likely require a change to the default memory sizes for your “buffer.memory” and “batch.size” parameters. These values are trickier to set than your consumer socket buffers as they depend on a number of factors, including producer data rate, number of partitions, and the total memory you have available. Larger buffers aren’t necessarily always better, because having too much data buffered on-heap can lead to increased garbage collection—a process that will compete for resources and affect your importance. Best practices should be established based on the unique configuration and settings of your Kafka data streaming system.