Apache Kafka and Real-Time Data Stream Processing

Apache Kafka is a high-performance server-based message broker capable of processing enormous volumes of events, measured in millions per second. Kafka's distinctive features include exceptional fault tolerance, the ability to store data for extended periods, and ease of infrastructure expansion through the simple addition of new nodes. The project's development began within LinkedIn, and in 2011, it was transferred to the Apache Software Foundation. Today, Kafka is widely used by leading global companies to build scalable, reliable data transmission infrastructure and has become the de facto industry standard for stream processing.

Kafka solves a key problem: ensuring stable transmission and processing of streaming data between services in real time. As a distributed broker, it operates on a cluster of servers that simultaneously receive, store, and process messages. This architecture allows Kafka to achieve high throughput, maintain operability during failures, and ensure minimal latency even with many connected data sources. It also supports data replication and load distribution across partitions, making the system extremely resilient and scalable.

Kafka is written in Scala and Java but supports clients in numerous languages, including Python, Go, C#, JavaScript, and others, allowing integration into virtually any modern infrastructure and use in projects of varying complexity and focus.

How the Technology Works

To work effectively with Kafka, you first need to understand its structure and core concepts. The system's main logic relies on the following components:

• Messages: Information enters Kafka as individual events, each representing a message.
• Topics: All messages are grouped by topics. A topic is a logical category or queue that unites data by a specific characteristic.
• Producers: These are programs or services that send messages to a specific topic. Producers are responsible for generating and transmitting data into the Kafka system.
• Consumers: Components that connect to a specific topic and extract published messages. To improve efficiency, consumers are often organized into consumer groups, thereby distributing the load among different instances and allowing better management of parallel processing of large data volumes. This division significantly improves overall system performance and reliability.
• Partitions: Any topic can be divided into partitions, enabling horizontal system scaling and increased performance.
• Brokers: Servers united in a Kafka cluster perform functions of storing, processing, and managing messages.

The component interaction process looks as follows:

1. The producer sends a message to a specified topic.

2. The message is added to the end of one of the topic's partitions and receives its sequential number (offset).

3. A consumer belonging to a specific group subscribes to the topic and reads messages from partitions assigned to it, starting from the required offset. Each consumer independently manages its offset, allowing messages to be re-read when necessary.

Thus, Kafka acts as a powerful message delivery mechanism, ensuring high throughput, reliability, and fault tolerance.

Since Kafka stores data as a distributed log, messages remain available for re-reading, unlike many queue-oriented systems.

Key Principles

Append-only log: messages are not modified/deleted (by default), they are simply added. This simplifies storage and replay.

• Partition division for speed: one topic is split into parts, and Kafka can process them in parallel. Thanks to this, it scales easily.
• Guaranteed order within partition: consumers read messages in the order they were written to the partition. However, there is no complete global ordering across the entire topic if there are multiple partitions.
• Messages can be re-read: a consumer can "rewind" at any time and re-read needed data if it's still stored in Kafka.
• Stable cluster operation: Kafka functions as a collection of servers capable of automatically redirecting load to backup nodes in case of broker failure.

Why Major Companies Choose Apache Kafka

There are several key reasons why large organizations choose Kafka:

Scalability

Kafka easily handles large data streams without losing performance. Thanks to the distributed architecture and message replication support, the system can be expanded simply by adding new brokers to the cluster.

High Performance

The system can process millions of messages per second even under high load. This level of performance is achieved through asynchronous data sending by producers and efficient reading mechanisms by consumers.

Reliability and Resilience

Message replication among multiple brokers ensures data safety even when part of the infrastructure fails. Messages are stored sequentially on disk for extended periods, minimizing the risk of their loss.

Log Model and Data Replay Capability

Unlike standard message queues where data disappears after reading, Kafka stores messages for the required period and allows their repeated reading.

Ecosystem Support and Maturity

Kafka has a broad ecosystem: it supports connectors (Kafka Connect), stream processing (Kafka Streams), and integrations with analytical and Big Data systems.

Open Source

Kafka is distributed under the free Apache license. This provides numerous advantages: a huge amount of official and unofficial documentation, tutorials, and reviews; a large number of third-party extensions and patches improving basic functionality; and the ability to flexibly adapt the system to specific project needs.

Why Use Apache Kafka?

Kafka is used where real-time data processing is necessary. The platform enables development of resilient and easily scalable architectures that efficiently process large volumes of information and maintain stable operation even under significant loads.

Stream Data Processing

When an application produces a large volume of messages in real time, Kafka ensures optimal management of such streams. The platform guarantees strict message delivery sequence and the ability to reprocess them, which is a key factor for implementing complex business processes.

System Integration

For connecting multiple heterogeneous services and applications, Kafka serves as a universal intermediary, allowing data transmission between them. This simplifies building microservice architecture, where each component can independently work with event streams while remaining synchronized with others.

Data Collection and Transmission for Monitoring

Kafka enables centralized collection of logs, metrics, and events from various sources, which are then analyzed by monitoring and visualization tools. This facilitates problem detection, system state control, and real-time reporting.

Real-Time Data Processing

Through integration with stream analytics systems (such as Spark, Flink, Kafka Streams), Kafka enables creation of solutions for operational analysis and rapid response to incoming data. This allows for timely informed decision-making, formation of interactive monitoring dashboards, and instant response to emerging events, which is critically important for applications in finance, marketing, and Internet of Things (IoT).

Real-Time Data Analysis

Through interaction with stream analytics tools (for example, Spark, Flink, Kafka Streams), Kafka becomes the foundation for developing solutions ensuring fast processing and analysis of incoming data. This functionality enables timely important management decisions, visualization of indicators in convenient interactive dashboards, and instant response to changing situations, which is extremely relevant for financial sector companies, marketers, and IoT solution developers.

Use Case Examples

Here are several possible application scenarios:

• Web platforms: any user action (view, click, like) is sent to Kafka, and then these events are processed by analytics, recommendation system, or notification service.
• Fintech: a transaction creates a "payment completed" event, which the anti-fraud service immediately receives. If suspicious, it can initiate a block and pass data further.
• IoT devices: thousands of sensors send readings (temperature, humidity) to Kafka, where they are processed by streaming algorithms (for example, for anomaly detection), and then notifications are sent to operators.
• Microservices: services exchange events ("order created," "item packed," etc.) through Kafka without calling each other directly.
• Log aggregation: multiple services send logs to Kafka, from where analytics systems, SIEM, or centralized processing systems retrieve them.
• Logistics: tracking delivery statuses or real-time route distribution.
• Advertising: collection and analysis of user events for personalization and marketing analytics.

These examples demonstrate Kafka's flexibility and its application in various areas.

When Kafka Is Not Suitable

It's important to understand the limitations and situations when Kafka is not the optimal choice. Several points:

• If the data volume is small (for example, several thousand messages per day) and the system is simple, implementing Kafka may be excessive. For low traffic, simple queues like RabbitMQ are better.
• If you need to make complex queries with table joins, aggregations, or store data for very long periods with arbitrary access, it's better to use a regular database.
• If full ACID transactions are important (for example, for banking operations with guaranteed integrity and relationships between tables), Kafka doesn't replace a regular database.
• If data hardly changes and doesn't need to be quickly transmitted between systems, Kafka will be excessive. Simple storage in a database or file may be sufficient.

Kafka's Differences from Traditional Databases

Traditional databases (SQL and NoSQL) are oriented toward storing structured information and performing fast retrieval operations. Their architecture is optimized for reliable data storage and efficient extraction of specific records on demand.

In turn, Kafka is designed to solve different tasks:

• Working with streaming data: Kafka focuses on managing continuous data streams, while traditional database management systems are designed primarily for processing static information arrays.
• Parallelism and scaling: Kafka scales horizontally through partitions and brokers, and is designed for very large stream data volumes. Databases (especially relational) often scale vertically or with horizontal scaling limitations.
• Ordering and stream: Kafka guarantees order within a partition and allows subscribers to read from different positions, jump back, and replay.
• Latency and throughput: Kafka is designed to provide minimal delays while simultaneously processing enormous volumes of events.

Example Simple Python Application for Working with Kafka

If Kafka is not yet installed, the easiest way to "experiment" with it is to install it via Docker. For this, it's sufficient to create a docker-compose.yml file with minimal configuration:

version: "3"
services:
  broker:
    image: apache/kafka:latest
    container_name: broker
    ports:
      - "9092:9092"
    environment:
      KAFKA_NODE_ID: 1
      KAFKA_PROCESS_ROLES: broker,controller
      KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9092,CONTROLLER://0.0.0.0:9093
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://localhost:9092
      KAFKA_CONTROLLER_LISTENER_NAMES: CONTROLLER
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: CONTROLLER:PLAINTEXT,PLAINTEXT:PLAINTEXT
      KAFKA_CONTROLLER_QUORUM_VOTERS: 1@localhost:9093
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
      KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1
      KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
      KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0
      KAFKA_NUM_PARTITIONS: 3

Run:

docker compose up -d

Running Kafka in the Cloud

In addition to local deployment via Docker, Kafka can be run in the cloud. This eliminates unnecessary complexity and saves time.

In Hostman, you can create a ready Kafka instance in just a few minutes: simply choose the region and configuration, and the installation and setup happen automatically.

The cloud platform provides high performance, stability, and technical support, so you can focus on development and growth of your project without being distracted by infrastructure.

Try Hostman and experience the convenience of working with reliable and fast cloud hosting.

Python Scripts for Demonstration

Below are examples of Producer and Consumer in Python (using the kafka-python library), the first script writes messages to a topic and the other reads.

First, install the Python library:

pip install kafka-python

producer.py

This code sends five messages to the test-topic theme.

from kafka import KafkaProducer
import json
import time

# Create Kafka producer and specify broker address
# value_serializer converts Python objects to JSON bytes
producer = KafkaProducer(
    bootstrap_servers="localhost:9092",
    value_serializer=lambda v: json.dumps(v).encode("utf-8"),
)

# Send 5 messages in succession
for i in range(5):
    data = {"Message": i}     # Form data
    producer.send("test-topic", data)  # Asynchronous send to Kafka
    print(f"Sent: {data}")       # Log to console
    time.sleep(1)                      # Pause 1 second between sends

# Wait for all messages to be sent
producer.flush()

consumer.py

This Consumer reads messages from the theme, starting from the beginning.

from kafka import KafkaConsumer
import json

# Create Kafka Consumer and subscribe to "test-topic"
consumer = KafkaConsumer(
    "test-topic",                         # Topic we're listening to
    bootstrap_servers="localhost:9092",   # Kafka broker address
    auto_offset_reset="earliest",         # Read messages from the very beginning if no saved offset
    group_id="test-group",                # Consumer group (for balancing)
    value_deserializer=lambda v: json.loads(v.decode("utf-8")),  # Convert bytes back to JSON
)

print("Waiting for messages...")

# Infinite loop—listen to topic and process messages
for message in consumer:
    print("Received:", message.value)     # Output message content

These two small scripts demonstrate basic operations with Kafka: publishing and receiving messages.

Conclusion

Apache Kafka is an effective tool for building architectures where key factors are event processing, streaming data, high performance, fault tolerance, and latency minimization. It is not a universal replacement for databases but excellently complements them in scenarios where classic solutions cannot cope. With proper architecture, Kafka enables building flexible, responsive systems.

When choosing Kafka, it's important to evaluate requirements: data volume, speed, architecture, integrations, ability to manage the cluster. If the system is simple and loads are small—perhaps it's easier to choose a simpler tool. But if the load is large, events flow continuously, and a scalable solution is required, Kafka can become the foundation.

Despite certain complexity in setup and maintenance, Kafka has proven its effectiveness in numerous large projects where high speed, reliability, and working with event streams are important.