Data Engineer with Apache Kafka Salary in 2024

Where is Apache Kafka used?

Chewing Through Messages Like a Beaver in a Log Buffet

• Kafka sinks its teeth into monstrous streams of data, gnawing away at messaging queues with a ravenous appetite, much like a castor prepping for a hard winter.

Ninja Moves in the Event-Driven Dojo

• Like a stealthy ninja in a realm of shadows, Kafka executes event-driven acrobatics, slicing through complex workflows and somersaulting around real-time processing tasks.

Streamlining the Mosaic of Microservices

• Our Kafka conductor waves its baton to orchestrate a symphony of chatty microservices, preventing a cacophony more ear-piercing than a bagpipe solo.

The Pipeline Prowess on the IoT Catwalk

• Kafka struts down the IoT runway, flashing its pipeline prowess, connecting svelte sensors and voluptuous devices in a data-exchange fashion show.

Apache Kafka Alternatives

RabbitMQ

RabbitMQ is an open-source message broker that enables applications to communicate by sending and receiving messages. It uses AMQP protocol.

• Supports multiple messaging protocols


• Easy-to-use management UI


• Flexible routing features


• Limited message durability


• Can be complex to scale horizontally


• Performance degrades with high throughput

# Example of sending a message with RabbitMQ in Python using Pika
import pika

connection = pika.BlockingConnection(pika.ConnectionParameters('localhost'))
channel = connection.channel()

channel.queue_declare(queue='hello')

channel.basic_publish(exchange='', routing_key='hello', body='Hello World!')
print(" [x] Sent 'Hello World!'")

connection.close()

Amazon Kinesis

Amazon Kinesis is a managed streaming service on AWS for real-time data processing over large, distributed data streams.

• Fully managed and scales automatically


• Integrates with other AWS services


• Real-time processing capabilities


• Can get expensive at scale


• Vendor lock-in with AWS


• Limited to AWS ecosystem

# No code example due to requiring AWS setup

Pulsar

Pulsar is a distributed pub-sub messaging system originally created by Yahoo, which offers strong durability guarantees and low latency.

• Multi-tenancy and geo-replication


• Horizontally scalable


• Built-in tiered storage


• More complex setup compared to Kafka


• Smaller community and ecosystem


• Client library maturity varies

# Example of producing a message in Pulsar using Python client
from pulsar import Client

client = Client('pulsar://localhost:6650')

producer = client.create_producer('my-topic')

producer.send(('Hello Pulsar').encode('utf-8'))

client.close()

Quick Facts about Apache Kafka

Kafka's Debut on the Tech Stage

Imagine a world before real-time data streaming was a breeze. Enter Apache Kafka in 2011, strutting its way onto the scene. Crafted by the hands of suave LinkedIn engineers, led by the grand Jay Kreps, it was their answer to high-speed, scalable messaging woes. It's like they handed out Ferraris for data highways!

Rev Up Your Data with Kafka 0.8

In the year 2013, something epic happened – Kafka 0.8 burst through the doors! It brought along with it a feisty feature, the replicated Kafka, turning heads and ensuring that your precious data could survive even when disaster struck. Like a data guardian angel, it had your back.

Kafka Streams: The Data Whisperer

Fast forward to 2016, and Kafka decided it's not enough just to pass messages; it had to understand them. With version 0.10.0.0, Kafka Streams was born – a swanky tool to process and analyze data in real time. Imagine data flowing through Kafka's brain, being sifted, sorted, and spoken to, all at lightning speed.

// Here's a tiny glimpse into the magic of Kafka Streams
KStreamBuilder builder = new KStreamBuilder();
KStream<String, String> textLines = builder.stream("quickstart-events");
KTable<String, Long> wordCounts = textLines
    .flatMapValues(textLine -> Arrays.asList(textLine.toLowerCase().split("\\W+")))
    .groupBy((key, word) -> word)
    .count("Counts");

wordCounts.to(Serdes.String(), Serdes.Long(), "quickstart-events-wordcount");

What is the difference between Junior, Middle, Senior and Expert Apache Kafka developer?

Top 10 Apache Kafka Related Tech

1. 
   

   Java

   
   

   Ah, Java, the granddaddy of Kafka-compatible languages, sturdy and steadfast as a mountain, albeit a bit less exciting than a dental appointment. Kafka, conceived in Scala and Java bosoms, loves Java more than a kangaroo loves its pouch. It's like a love story more predictable than a Hallmark movie. So if you're waltzing into the Kafka ecosystem, Java is the tango you’ll want to dance.

   
   

   
   // Java producer example
   Properties props = new Properties();
   props.put("bootstrap.servers", "localhost:9092");
   props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
   props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
   
   Producer<String, String> producer = new KafkaProducer<>(props);
   producer.send(new ProducerRecord<String, String>("test", "key", "value"));
   producer.close();
   

   
   


2. 
   

   Scala

   
   

   Scala is like the cool, hip cousin of Java who shows up at family reunions on a motorcycle. It blends object-oriented and functional programming more smoothly than a bartender mixes a mojito. Kafka itself is written in Scala, so using Scala is akin to speaking the native tongue, and it provides concise, powerful code, which can reduce the chance of developing carpal tunnel from typing too much.

   
   

   
   // Scala producer example
   val props = new Properties()
   props.put("bootstrap.servers", "localhost:9092")
   props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer")
   props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer")
   
   val producer = new KafkaProducer[String, String](props)
   producer.send(new ProducerRecord[String, String]("test", "key", "value"))
   producer.close()
   

   
   


3. 
   

   Apache Kafka Streams

   
   

   Crafting streaming applications with Kafka Streams is like being a wizard in the enchanted world of real-time processing, except the spells are lines of code and the dragons are rogue unprocessed data streams. It does for stream processing what microwaves did for cooking: making it quick, efficient, and far less likely to set your kitchen—er, data pipeline—on fire.

   
   

   
   // Stream processing example
   StreamsBuilder builder = new StreamsBuilder();
   KStream<String, String> textLines = builder.stream("quickstart-events");
   KTable<String, Long> wordCounts = textLines
       .flatMapValues(textLine -> Arrays.asList(textLine.toLowerCase().split("\\W+")))
       .groupBy((key, word) -> word)
       .count(Materialized.as("counts-store"));
   wordCounts.toStream().to("word-count-output", Produced.with(Serdes.String(), Serdes.Long()));
   

   
   


4. 
   

   Spring for Apache Kafka

   
   

   Imagine Spring dancing around the Kafka ecosystem like Snow White in the woods, harmonizing message passing as effectively as Snow White summons the forest creatures. It simplifies the Kafka integration with its magical framework. Using it is like having a fairy godmother who codes, making Kafka integration bibbidi-bobbidi-boo simple.

   
   

   
   // Spring Kafka listener example
   @Service
   public class KafkaConsumerService {
   
     @KafkaListener(topics = "test", groupId = "group_id")
     public void consume(String message) {
       System.out.println("Consumed message: " + message);
     }
   }
   

   
   


5. 
   

   Docker

   
   

   Embarking on Kafka development without understanding Docker is like going on a voyage without knowing what a boat is. Docker containers make deploying Kafka as easy as shopping online in your pajamas. It's the difference between painstakingly assembling a jigsaw puzzle and having it delivered as a gorgeous pre-made picture.

   
   

   
   // Docker Compose snippet for Kafka
   services:
     zookeeper:
       image: wurstmeister/zookeeper
       ports:
         - "2181:2181"
     kafka:
       image: wurstmeister/kafka
       ports:
         - "9092:9092"
       environment:
         KAFKA_ADVERTISED_HOST_NAME: localhost
         KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
   

   
   


6. 
   

   Confluent Platform

   
   

   Using the Confluent Platform with Kafka is like having a Swiss Army knife in the jungle of data streaming. It's packed with tools and enhancements that make Kafka look like it went through an extreme home makeover. It's Kafka on steroids, vitamins, and a splash of elixir of life.

   
   


7. 
   

   Kafka Connect

   
   

   Like a matchmaker for data, Kafka Connect pairs up Kafka with various data sources and sinks, making sure everyone gets along nicely and data flows like conversations at a speed-dating event. It ensures that your Kafka cluster can read from and write to databases, making integration less of a hassle than getting a toddler to eat their greens.

   
   


8. 
   

   Prometheus & Grafana

   
   

   Monitoring Kafka without Prometheus and Grafana is like exploring a cave without a flashlight. It's possible but not recommended unless you fancy stumbling around in the dark. These tools shine a light on Kafka's performance, giving you dashboards so informative they could double as an informational pamphlet on how awesome your Kafka setup is doing.

   
   


9. 
   

   Kafka Manager

   
   

   If Kafka were a circus, Kafka Manager would be the ringmaster, keeping all the acts in check and making sure the lions don't eat the acrobats. It provides a UI that helps wrangle the Kafka clusters, giving you insight into the performance and making configuration changes as easy as an “abracadabra”.

   
   


10. 
    

    Ansible

    
    

    Provisioning Kafka clusters manually is a task so tedious it could be an ancient form of punishment. Enter Ansible, the IT automation magician, which orchestrates cluster provisioning like a conductor with a symphony, ensuring each node comes to life playing the sweet, sweet symphony of a fully automated deployment process.