Apache Kafka: Architecture, deployment and ecosystem [2025 guide]

What is Apache Kafka?

Apache Kafka is an open-source stream-processing platform, managed by the Apache Foundation. It is used for building real-time data pipelines and streaming applications. Kafka handles tasks like tracking logs, metrics, readings from IoT sensors, financial transactions, and social media interactions. Kafka is adopted in various domains, such as banking, telecoms, and tech giants.

Kafka operates as a distributed system, running across multiple servers. This setup facilitates horizontal scalability by adding more servers to handle an increasing load. Its architecture consists of key components like producers that send data, consumers that read data, and brokers that mediate the data flow.

Kafka provides persistence and fault tolerance, ensuring data durability and reliability. This makes it suitable for critical applications where data loss or downtime is unacceptable.

You can get Kafka from the official project page.

What is Kafka used for?

Kafka is used across various industries for numerous applications. One of its primary uses is in building real-time data pipelines. These pipelines transport data across different systems quickly and reliably, which is fundamental for businesses requiring up-to-the-minute data insights. In retail, for instance, Kafka can track real-time inventory levels and sales data, allowing for instant adjustments and accurate forecasting.

Another use case is event sourcing, particularly in microservices architectures. Kafka helps in capturing all state changes as a sequence of events, which can later be analyzed. This enables applications to rebuild state from logs, ensuring data consistency and simplifying recovery processes. Additionally, Kafka is leveraged for log aggregation, monitoring, and fraud detection applications, providing a platform to handle high volumes of streaming data.

How does Kafka work?

Kafka’s operational model involves producers, brokers, and consumers. Producers send data records to brokers, which then store the data in an optimized format, organized into topics. Consumers then read from these topics.

A key feature of Kafka is its distributed commit log, ensuring that messages are stored safely and can be replayed or processed multiple times by different consumers at different rates. Kafka’s brokers use partitions to distribute data, enhancing both the scalability and reliability of the system.

Real-time processing at scale

Kafka excels in real-time data processing, allowing organizations to move and process data streams at scale. Its architecture supports high-throughput data ingestion, suitable for applications requiring rapid processing of vast amounts of data. For example, financial trading platforms use Kafka to track real-time market data and execute trades instantly based on the input received. Kafka’s low latency ensures minimal delay, crucial for time-sensitive operations.

Kafka can manage billions of data points daily, making it a reliable choice for data-heavy industries. It achieves this through efficient data partitioning and parallel processing techniques. Each topic in Kafka can be divided into multiple partitions, and each partition can be processed in parallel, enabling faster computations. This design ensures that Kafka can scale horizontally by adding more nodes or partitions to the system, distributing the workload evenly and maintaining performance.

Durable, persistent storage

Durability is a core attribute of Kafka, ensuring that data remains available even in the face of failures. Kafka stores messages on disk and replicates them across multiple brokers. This redundancy guarantees data persistence, even if some brokers fail. The replication factor can be configured based on the desired level of durability and availability, providing flexibility according to different use cases and data criticality.

Kafka’s log-based storage system is another key feature, allowing messages to be stored and retrieved efficiently. Each message has a unique offset, which serves as an identifier, making it easier to resume consumption from any point in the log. This design also supports time-based retention policies, enabling automatic deletion of old data and freeing up storage. These mechanisms ensure that Kafka’s storage remains both durable and manageable, maintaining the integrity and availability of data over time.

Publish/subscribe

Kafka employs a publish/subscribe model where producers publish messages to topics, and consumers subscribe to those topics to receive the messages. This model decouples producers from consumers, allowing them to operate independently. Producers send data to topics without needing to know who will consume it, and consumers read data without needing to know the source. This system offers flexibility and simplifies data pipeline management.

Furthermore, the publish/subscribe architecture supports multiple subscriber models. Consumers can read messages in real-time or in batch mode, depending on their requirements. Kafka ensures that each consumer group gets its dedicated set of messages, enhancing processing efficiency and data segregation. This model is ideal for implementing real-time analytics, monitoring systems, and other applications where timely data dissemination is crucial.

Understanding Kafka architecture

Producer and consumer APIs

The Producer API allows applications to send streams of data to Kafka topics. Producers send records, which are key-value pairs, to specified topics, and Kafka brokers handle the data distribution across partitions for scalability. Producers can also define message keys to control which partition a record goes to, ensuring that records with the same key are always delivered to the same partition.

The Consumer API is used by applications to read data from Kafka topics. Consumers subscribe to one or more topics and read messages from partitions. Kafka’s consumer groups allow for distributed consumption, where multiple consumers can share the load of reading messages from a topic, with each consumer handling messages from different partitions.

Kafka Connect

Kafka Connect simplifies integrating Kafka with external systems, such as databases, file systems, and other services. It provides a scalable and fault-tolerant way to stream data between Kafka and these systems, supporting source connectors (which pull data from external systems into Kafka) and sink connectors (which push data from Kafka into external systems).

Kafka Connect is highly configurable and supports distributed or standalone modes. In a distributed mode, multiple workers can share the task of running connectors, offering higher availability and scalability. The system also handles data transformations, allowing users to modify records as they are streamed between systems.

Kafka Streams

Kafka Streams is a client library for building real-time applications that process data streams directly within Kafka. It enables developers to transform, aggregate, filter, or join data streams. Kafka Streams leverages the same fault-tolerant architecture as Kafka, ensuring that stream processing applications can scale and handle failures automatically.

With Kafka Streams, users can write applications that process data as it flows through Kafka topics. The library abstracts much of the complexity of distributed stream processing, offering simple APIs for common operations, such as windowed computations, aggregations, and joins.

ksqlDB

ksqlDB extends Kafka’s capabilities by providing a SQL-like interface for working with data streams. With ksqlDB, users can query Kafka topics in real time using familiar SQL syntax, enabling complex transformations and aggregations without needing to write custom code. This reduces the barrier to entry for building stream processing applications.

ksqlDB also supports creating materialized views from continuous queries, which can be used for real-time monitoring or analytics. The system is designed to handle high-throughput, low-latency workloads, making it suitable for scenarios like fraud detection, operational monitoring, and event-driven applications.

Kafka cluster components

A Kafka cluster is a distributed system consisting of multiple Kafka brokers that work together to provide high-throughput, fault-tolerant messaging and data streaming services.

Kafka broker

A Kafka broker is a server that handles data storage and retrieval in a Kafka cluster. Brokers receive data from producers and write it to disk, partitioning it for access. Each broker manages multiple partitions and ensures data replication for fault tolerance. Brokers can serve both read and write requests, distributing the load across the cluster. This distributed approach helps maintain throughput and low latency.

Brokers coordinate with KRaft (or ZooKeeper in older systems) to monitor the state of partitions and manage consumer offsets. Kafka automatically balances partitions across brokers, ensuring even load distribution. This automatic balancing is critical for maintaining performance as new brokers are added to the cluster. Brokers also handle data retention policies, deleting old data based on configured time or size limits, ensuring efficient use of storage space.

KRaft

KRaft (Kafka Raft) is a new consensus protocol introduced in Apache Kafka to eventually replace ZooKeeper, simplifying the management of Kafka clusters. ZooKeeper has traditionally been used for storing metadata and coordinating cluster management tasks, but with KRaft, Kafka can operate independently, reducing operational complexity and making Kafka self-contained.

KRaft uses the Raft consensus algorithm to manage metadata, ensuring that cluster changes, such as partition leadership and topic management, are coordinated safely across brokers. This increases fault tolerance by removing the dependency on an external system like ZooKeeper. KRaft also improves scalability, as the metadata is distributed across multiple brokers.

Kafka producers

Kafka producers are responsible for sending data to the Kafka cluster. Producers can send messages to specific topics, partitioning them based on keys or round-robin strategies. This partitioning is essential for distributing the load and ensuring parallel processing. Producers can handle both synchronous and asynchronous data sends, optimizing for either performance or reliability based on the application requirements.

Producers also have configurable settings for durability, such as acknowledgment levels from brokers. By adjusting these settings, producers can balance between performance and data reliability. In high-throughput environments, asynchronous sends with batch processing can enhance performance. Producers’ ability to compress messages before sending further optimizes data transmission, reducing network load and improving efficiency.

Kafka consumers

Kafka consumers read messages from Kafka topics. They can operate individually or as part of consumer groups, where Kafka distributes messages across the members of the group. Consumers manage message offsets, which indicate the position of the next message to be read. This offset management ensures that consumers process messages reliably, supporting both at-least-once and exactly-once delivery semantics.

Consumers can leverage Kafka’s support for automatic offset management, wherein offsets are committed at regular intervals. This automatic mechanism simplifies the consumption process, though manual offset control can provide finer granularity for critical applications. Consumer rebalancing ensures that messages are redistributed if new consumers join or existing consumers leave, maintaining load balance and ensuring that all messages are processed.

Related content: Read our guide to Kafka management

Options for deploying Apache Kafka

Standalone installation

A standalone installation of Kafka is a straightforward setup ideal for development and testing. This installation can run on a single server or a personal computer. It involves installing Kafka and KRaft (or ZooKeeper for legacy clusters) on the same machine, providing an easy way to experiment with Kafka’s capabilities without requiring a complex setup. Standalone installations are also useful for learning and prototyping applications before moving to more robust environments.

One key advantage of standalone installations is their simplicity and ease of management. With fewer components, it is easier to configure and troubleshoot. However, this simplicity comes at the cost of limited scalability and fault tolerance. Standalone setups are not suitable for production environments but provide a starting point for individuals and small teams to get acquainted with Kafka.

Clustered deployment

Clustered deployment is the preferred approach for production environments. Multiple Kafka brokers and KRaft nodes are deployed across several servers, ensuring high availability and load balancing. This setup facilitates handling large volumes of data and provides built-in failover mechanisms. In case a broker fails, others take over its partitions, maintaining the continuity of data flow.

Clustered deployments allow horizontal scaling. As data traffic grows, more brokers can be added to the cluster, distributing the load. This flexibility ensures that Kafka can handle increasing demands without compromising performance. Clustered deployments also support replication across brokers, ensuring data durability and fault tolerance. These features make clustered deployments suitable for enterprise-level applications requiring scalable data streaming services.

Containerized deployment

Containerized deployment leverages Docker and Kubernetes to run Kafka, offering isolated and consistent environments. Containers encapsulate Kafka and its dependencies, making deployment easier and more predictable. Docker allows for rapid deployment and efficient resource utilization, as containers share the host system’s kernel and run in isolated user spaces. This approach simplifies the installation and setup of Kafka.

Kubernetes enhances containerized deployment by providing orchestration capabilities. It manages container deployment, scaling, and load balancing. Kubernetes can automatically restart failed containers, ensuring high availability and reliability. With containerized deployments, organizations can achieve faster development cycles, easier scaling, and consistent environments, making it easier to manage Kafka across different stages of the development lifecycle.

Apache Kafka in the cloud

Deploying Kafka in the cloud is easy, and can be a preferred option for organizations based on their infrastructure requirements.

Managed Kafka services offered by providers like Instaclustr, AWS (via Amazon MSK), Azure, and Google Cloud simplify Kafka deployment and management within the cloud. These services handle the underlying infrastructure, including hardware provisioning, software installation, and maintenance. Users can focus on developing and deploying applications rather than managing Kafka’s operations. Managed services offer automatic scaling, backup, and recovery, ensuring robust performance and reliability.

Like on-prem deployments, managed Kafka services provide advanced monitoring and security features within the cloud. They integrate with other cloud services, enabling data pipelines and analytics solutions. Users can leverage these integrations for real-time data processing, data lake formation, and machine learning model training.

Apache Kafka vs RabbitMQ: What are the differences?

Apache Kafka and RabbitMQ are both messaging systems, but they serve different purposes and have distinct architectures.

Kafka is designed for high-throughput, low-latency data streaming, making it suitable for real-time data pipelines and event sourcing. It uses a distributed log-based storage system, ensuring data durability and efficient processing. Kafka’s publish/subscribe model supports large-scale data distribution and real-time analytics applications, catering to industries like finance, telecommunications, and e-commerce.

RabbitMQ is a message broker focusing on reliable message delivery and complex routing. It excels in scenarios requiring guaranteed message delivery, transactional operations, and complex message routing patterns. RabbitMQ’s architecture is based on messaging queues and exchanges, allowing fine-grained control over message flow and consumption. This makes RabbitMQ suitable for enterprise messaging, microservices communication, and task scheduling.

Related technologies used with Kafka

Apache Hadoop

Apache Hadoop is a framework for distributed storage and processing of large datasets. It consists of two components: the Hadoop Distributed File System (HDFS) and the MapReduce processing engine. HDFS provides a scalable and fault-tolerant storage solution, while MapReduce allows for parallel processing of large data sets across a cluster of machines.

Hadoop integrates with Kafka, providing a solution for batch processing of data ingested via Kafka. Data can be streamed from Kafka topics to HDFS, where it can be processed using MapReduce or other Hadoop ecosystem tools like Apache Hive, Apache Pig, and Apache HBase. This integration allows organizations to leverage the strengths of both Kafka for real-time data ingestion and Hadoop for batch processing and storage.

Apache Spark

Apache Spark is an analytics engine used for big data processing. It supports both batch and real-time data processing, making it a tool in a data pipeline. Spark integrates with Kafka, allowing for data ingestion and processing. Spark Streaming, a component of Spark, can consume data from Kafka topics, perform transformations, aggregations, and machine learning tasks in real-time.

Spark provides a set of APIs in Java, Scala, Python, and R, making it accessible to a range of developers. It offers fault tolerance mechanisms, where data can be recomputed from lineage information in case of failures. This reliability combined with its processing power makes Spark a preferred choice for building real-time analytics solutions on top of Kafka. By leveraging Spark’s capabilities, organizations can derive insights from their data streams.

Apache Flink

Apache Flink is a stream processing framework that excels at real-time data processing and event-driven applications. It provides abstractions for defining complex data processing pipelines, allowing developers to create streaming applications with ease. Flink integrates with Kafka, enabling the consumption and production of data streams directly from Kafka topics.

Flink’s features include event time processing, stateful computations, and exactly-once processing semantics. Event time processing ensures that events are processed based on their timestamps, allowing for accurate handling of out-of-order events. Stateful computations enable Flink to maintain state information across records, which is crucial for operations like aggregations, joins, and windowing. Exactly-once semantics guarantee that each event is processed exactly once, even in the face of failures, ensuring data consistency.

Best practices for using and managing Apache Kafka

1. Plan your data schema

Planning your data schema is essential for maintaining data quality and facilitating efficient data processing in Kafka. Utilize schema registry tools, such as Confluent Schema Registry, to manage and version your schemas. These tools help ensure that data producers and consumers adhere to the same schema, preventing data inconsistencies. Define clear data contracts, specifying the structure and type of data fields, to avoid errors during data ingestion and processing.

When designing your schema, consider future-proofing it by allowing for schema evolution. This means planning for potential changes in the schema while ensuring backward and forward compatibility. Use schema validation to enforce these rules and avoid schema-related errors. This practice enhances data quality and simplifies the integration of new data sources and the modification of existing ones.

2. Use Kafka topics efficiently

Efficient use of Kafka topics is crucial for maintaining an organized and scalable data pipeline. Start by designing your Kafka topics to align with your data flow requirements.

Use a separate topic for each distinct data stream to ensure data isolation and easier management. Implement partitioning strategies based on keys that allow for even distribution of data across partitions, which helps in balancing the load and improving performance.

Adopt a clear and consistent naming convention for your topics. Descriptive and standardized names make it easier for developers and administrators to understand the purpose of each

3. Optimize your Kafka consumers

Optimizing your Kafka consumers is vital for ensuring efficient and reliable data processing. Leverage consumer groups to distribute the load across multiple consumers, enabling parallel processing and improving throughput. Each consumer in a group processes data from a unique subset of partitions, allowing for scalable and fault-tolerant consumption.

Fine-tune consumer configuration parameters to match your workload and performance requirements. Adjust fetch size, session timeouts, and offset commit intervals to optimize data retrieval and processing efficiency. Implement error handling and retry mechanisms to manage transient failures gracefully. This ensures that your consumers can recover from errors without losing data or causing significant delays.

Consider using idempotent consumers or implementing exactly-once semantics if your application requires precise data processing guarantees. This involves ensuring that each message is processed exactly once, even in the event of failures, which is critical for applications that cannot tolerate duplicate data processing.

4. Monitor Kafka performance metrics

Regular monitoring of Kafka performance metrics is essential for maintaining the health and efficiency of your Kafka cluster. Key metrics to track include broker and topic throughput, partition offsets, consumer lag, and producer latency. These metrics provide insights into the performance and reliability of your Kafka deployment.

Use monitoring tools like Kafka Manager, Burrow, or Confluent Control Center to visualize these metrics and set up alerts for abnormal conditions. Proactive monitoring helps in identifying and resolving issues before they impact your applications. For instance, monitoring consumer lag can help you detect slow consumers that might be causing delays in data processing.

Implement a logging and alerting strategy to ensure timely detection and resolution of issues. Set up dashboards to visualize key metrics and trends over time, helping you make informed decisions about scaling and optimizing your Kafka deployment. Regularly review and adjust your monitoring strategy to address evolving requirements and challenges.

Learn more in our detailed guide to Kafka performance (coming soon)

5. Secure Kafka

Implementing security measures is critical to protect your Kafka cluster from unauthorized access and data breaches. Start by using SSL/TLS encryption for data in transit to ensure that data exchanged between Kafka brokers, producers, and consumers is secure. This prevents eavesdropping and tampering by malicious actors.

Enable authentication mechanisms, such as SASL (Simple Authentication and Security Layer), to verify the identities of clients connecting to the Kafka cluster. This ensures that only authorized users and applications can produce and consume data. Configure access control lists (ACLs) to enforce fine-grained permissions on topics and operations, allowing you to control who can read, write, and manage topics.

Regularly update your Kafka and KRaft versions to patch known vulnerabilities and address security issues. Migrate away from ZooKeeper, which is deprecated and will be discontinued in Kafka 4.0. Conduct thorough security audits to ensure compliance with your organization’s security policies and industry best practices. Implement network segmentation and firewall rules to restrict access to Kafka brokers and KRaft or ZooKeeper nodes, further enhancing security.

Finally, consider using Kafka’s built-in encryption for data at rest to protect data stored on disk. This adds an extra layer of security, ensuring that sensitive data remains protected even if the physical storage is compromised.

Harnessing the power of Apache Kafka on the Instaclustr managed platform

Instaclustr, a leading provider of managed open source data platforms, offers a powerful and comprehensive solution for organizations seeking to leverage the capabilities of Apache Kafka. With its managed platform for Apache Kafka, Instaclustr simplifies the deployment, management, and optimization of this popular distributed streaming platform, providing numerous advantages and benefits for businesses looking to build scalable and real-time data pipelines.

Instaclustr takes care of the infrastructure setup, configuration, and ongoing maintenance, allowing organizations to quickly get up and running with Apache Kafka without the complexities of managing the underlying infrastructure themselves. This streamlines the adoption process, reduces time-to-market, and enables organizations to focus on developing their data pipelines and applications.

Instaclustr’s platform is designed to handle large-scale data streaming workloads, allowing organizations to seamlessly scale their Kafka clusters as their data needs grow. With automated scaling capabilities, Instaclustr ensures that the Kafka infrastructure can handle increasing data volumes and spikes in traffic, providing a reliable and performant streaming platform. Additionally, Instaclustr’s platform is built with redundancy and fault tolerance in mind, enabling high availability and minimizing the risk of data loss or service disruptions.

Organizations can leverage the expertise of Instaclustr’s engineers, who have deep knowledge and experience with Kafka, to optimize their Kafka clusters for performance, reliability, and efficiency. Instaclustr provides proactive monitoring, troubleshooting, and performance tuning, ensuring that organizations can effectively utilize Kafka’s capabilities and identify and resolve any issues promptly.
Instaclustr follows industry best practices and implements robust security measures to protect sensitive data and ensure compliance with data privacy regulations. Features such as encryption at rest and in transit, authentication and authorization mechanisms, and network isolation help organizations safeguard their data and maintain a secure Kafka environment.

For more information:

• Apache Kafka tutorial: Get started with Kafka in 5 simple steps
• Apache Kafka® Use Cases and Real-Life Examples