Building a data pipeline with Apache Kafka® & Kafka Connect: The basics and best practices

What is a Kafka data pipeline?

A Kafka data pipeline is a method to move data from one system to another in real time using Apache Kafka, usually with the help of Kafka Connect. It organizes the journey of data from producers to consumers while ensuring efficient and scalable data transfer. Kafka provides a distributed stream processing framework that can handle large volumes of data with minimal latency.

Kafka pipelines are crucial in environments where rapid data processing and analysis are critical. They enable real-time data flows by breaking down the process into ingesting, storing, and processing. This structured flow helps organizations react quickly to new data.

In this article:

• Benefits of Using Kafka for Data Pipelines
• Core Components of a Kafka Data Pipeline
• Common Use Cases for Kafka Data Pipelines
• Tutorial: Building a Simple Data Pipeline with Apache Kafka
• 5 Best Practices for Designing Kafka Data Pipelines

Benefits of using Kafka for data pipelines

Using Kafka for data pipelines provides several advantages, making it a preferred choice for real-time data movement and processing. It ensures high availability, fault tolerance, and scalability, which are necessary for data-driven applications. Here are the key benefits:

• High throughput and low latency: Kafka can handle millions of messages per second with minimal delay.
• Scalability: Kafka’s distributed architecture allows it to scale horizontally by adding more brokers.
• Fault tolerance: Kafka replicates data across multiple nodes, ensuring reliability and preventing data loss even if individual components fail.
• Durability: Messages are stored persistently in Kafka topics, allowing consumers to process them at their own pace without data loss.
• Decoupling of producers and consumers: Kafka enables a loosely coupled architecture where producers and consumers operate independently.
• Support for multiple consumers: Data in Kafka topics can be consumed by multiple applications simultaneously.
• Integration with big data and streaming frameworks: Kafka works with well with Big Data techs like Apache Cassandra, ClickHouse, and OpenSeaarch, as well as with streaming frameworks like Cadence. (Best of all, all the mentioned techs are provided as managed services on the NetApp Instaclustr platform – to get a taste of how they work together, sign up  for a free trial.)

Note, usually to stream data to or from Kafka, Kafka Connect running an appropriate connector is required. But, lately, more and more techs are starting to provide native / inbuilt functionality to allow streaming between themselves and Kafka (for example, OpenSearch , ClickHouse and Spark). 

Core components of a Kafka data pipeline

Producers

Producers are the components responsible for sending data into Kafka topics. They act as the entry point for data, publishing messages to topics that are then processed downstream. Producers push data in a fault-tolerant and scalable manner, ensuring that messages reach the appropriate Kafka brokers.

Producers can specify message keys, which help determine which partition a message is assigned to. This is crucial for maintaining order within a partition, as messages with the same key will always be sent to the same partition. Additionally, Kafka producers can operate in different acknowledgment modes:

• “At-most-once”: Messages are sent without confirmation, potentially leading to data loss.
• “At-least-once”: Messages are resent if acknowledgments are not received, ensuring delivery but allowing duplicates.
• “Exactly-once”: Guarantees that messages are processed only once, crucial for transactional applications.

Kafka producers are optimized for high throughput and low latency, enabling real-time data ingestion for various applications, including event streaming, log aggregation, and analytics.

Topics

Topics are the primary way Kafka organizes and categorizes data. A topic is a named stream of records that acts as a logical container for messages. Each topic can have multiple partitions, which divide the data and allow Kafka to scale horizontally.

Partitions enable parallel processing by distributing the load across multiple brokers. Each partition is assigned a leader broker responsible for handling all read and write operations, with follower replicas ensuring redundancy and fault tolerance.

Kafka topics retain messages for a configurable period, even after they have been consumed. This enables different consumers to process data at their own pace and allows replaying past messages if needed. Retention policies can be configured based on:

 

• Time-based retention: Messages are stored for a specified duration (e.g., 7 days).
• Size-based retention: Messages are deleted when the topic reaches a defined size limit.
• Compacted topics: Kafka retains only the latest value for each message key.

Brokers

Brokers are Kafka servers that store and manage topics. They handle incoming messages from producers, store them in partitions, and serve them to consumers. Kafka clusters typically consist of multiple brokers to ensure scalability, fault tolerance, and high availability.

Each broker can host multiple partitions, and Kafka distributes partitions across brokers to balance the load. To maintain resilience, Kafka replicates partitions across brokers, with one broker acting as the leader and others as followers. If a leader broker fails, one of the followers is automatically promoted to maintain system stability.

Brokers also manage client connections and coordinate with ZooKeeper to keep track of cluster metadata. With Kafka’s newer versions (KRaft mode), ZooKeeper is being phased out, and brokers handle metadata management natively. A Kafka cluster can dynamically scale by adding or removing brokers.

Consumers

Consumers are applications that read messages from Kafka topics. They subscribe to topics and process messages in real time. Kafka consumers pull data from brokers at their own pace, enabling flexible and efficient data processing.

Consumers are typically organized into consumer groups, where multiple consumer instances share the workload of processing messages from a topic. Kafka ensures that each message in a topic is consumed by only one consumer in a group, achieving parallel processing while maintaining order.

Consumers use offsets to track their progress in reading messages. Kafka provides two main offset management strategies:

• Automatic offset management: Kafka commits offsets periodically.
• Manual offset management: Consumers control when offsets are committed.

Apache ZooKeeper™  and KRaft

ZooKeeper has historically been a core component of Apache Kafka’s architecture, responsible for managing cluster metadata, broker coordination, and controller elections. Before the introduction of KRaft (Kafka Raft Metadata mode), every Kafka cluster required an external ZooKeeper ensemble to function. KRaft simplifies Kafka’s architecture by integrating metadata management directly into Kafka brokers, removing the dependency on an external system.

Common use cases for Kafka data pipelines

Fraud detection in financial services

Kafka data pipelines are widely used in financial institutions to detect fraudulent activities in real time. By streaming transaction data from multiple sources, Kafka enables the rapid identification of suspicious patterns.

Fraud detection systems leverage machine learning models and rule-based engines to analyze incoming transactions. Kafka enables this by integrating with processing frameworks like Apache Flink or Spark. If an anomaly is detected—such as an unusually large transaction or transactions from different locations within a short period—an alert is triggered for further investigation.

Real-time analytics in eCommerce

eCommerce platforms use Kafka pipelines to analyze customer behavior, track inventory, and personalize recommendations in real time. Data from website interactions, purchase histories, and customer reviews are streamed into Kafka topics, enabling analytics systems to process them immediately.

For example, when a user browses a product, Kafka streams this event data to an analytics engine, which updates recommendation algorithms dynamically. Similarly, stock levels can be monitored and adjusted automatically.

Monitoring and logging in microservices

Microservices architectures generate vast amounts of log data from different services, making centralized monitoring critical for system health and debugging. Kafka acts as a log aggregator, collecting logs from various services and forwarding them to monitoring tools like OpenSearch or Prometheus.

With Kafka, logs can be processed in real time to detect application failures, performance issues, or security threats. Alerts can be generated when anomalies occur.

IoT data processing and analytics

IoT devices generate continuous data streams that require efficient ingestion, processing, and storage. Kafka serves as the backbone for IoT data pipelines by enabling real-time data flow from sensors, smart devices, and industrial machines.

For example, in a smart factory, Kafka can collect sensor readings from machines to detect anomalies, optimize production efficiency, and trigger predictive maintenance alerts. The data can then be processed using stream processing frameworks or stored for long-term analysis.

Related content: Read our guide to Apache Kafka use cases (coming soon)

5 best practices for designing Kafka data pipelines

Here are some useful practices to consider when building data pipelines in Kafka.

1. Ensuring data integrity and exactly-once processing

Maintaining data integrity in a Kafka data pipeline requires careful handling of message delivery semantics. Kafka provides three delivery guarantees:

• At-most-once: Messages may be lost but are never duplicated.
• At-least-once: Messages are never lost but may be duplicated.
• Exactly-once: Each message is processed only once.

To achieve exactly-once processing, use Kafka transactions. Producers can batch multiple messages into a single atomic unit, ensuring that either all messages are written or none at all. Consumers should use idempotent processing—ensuring the same message does not cause unintended side effects if reprocessed.

 

Kafka Streams and connectors support exactly-once processing via EOS (Exactly Once Semantics) mode. When integrating with external systems like databases, use idempotent writes and distributed transactions (e.g., two-phase commit protocol) to maintain consistency.

2. Optimizing partitioning and replication

Proper partitioning and replication strategies improve Kafka’s performance, scalability, and fault tolerance.

Partitioning best practices:

• Use a consistent partitioning key to ensure related messages are processed together.
• Balance the number of partitions with available consumer instances to maximize parallelism.
• Avoid too few partitions, which can create processing bottlenecks, or too many, which can increase metadata overhead.

Replication best practices:

• Set a replication factor of at least 3 in production to prevent data loss.
• Use min.insync.replicas to enforce stricter durability guarantees.
• Distribute partitions evenly across brokers to avoid hot spots.

3. Securing Kafka clusters

Kafka clusters must be secured to protect against unauthorized access, data leaks, and attacks. Key security measures include:

• Authentication: Use SASL (Simple Authentication and Security Layer) or mutual TLS for client and broker authentication.
• Authorization: Implement ACLs (Access Control Lists) to restrict user access to topics and operations.
• Encryption: Use TLS (Transport Layer Security) for encrypting data in transit and disk encryption for data at rest.
• Audit logging: Enable logging to track authentication failures and unauthorized access attempts.
• Network segmentation: Place Kafka brokers in a private network or behind a firewall to limit exposure.

4. Handling backpressure and resource management

Kafka pipelines must handle backpressure to prevent system overload and ensure stable performance under high data loads.

Techniques to manage backpressure:

• Consumer throttling: Use consumer pause() and resume() methods to control message processing rates dynamically.
• Rate limiting: Apply quotas on producer and consumer throughput to avoid overwhelming brokers.
• Batch processing: Adjust fetch.min.bytes and fetch.max.wait.ms to optimize batch sizes for efficiency.
• Scaling consumers: Increase the number of consumers in a consumer group to distribute load more effectively.
• Monitoring and alerts: Use Prometheus or Grafana to monitor lag, CPU, and memory usage.

5. Integrating with other data systems

Kafka integrates with databases, data warehouses, and analytics platforms using Kafka Connect and stream processing frameworks.

Best practices for integration:

• Use Kafka Connect with connectors for databases (like PostgreSQL, MySQL), data lakes (S3, HDFSAzure Blob Storage, etc), and search engines (OpenSearch, for example).
• Employ schema evolution with Avro or Protobuf to handle changing data structures while maintaining compatibility.
• Implement change data capture (CDC) for real-time replication of database changes into Kafka topics.
• Use Kafka Streams to process and transform data before storing it in downstream systems.

Ensure exactly-once processing when writing to external storage to prevent duplicate or missing records.

Instaclustr for Apache Kafka and its role in data pipelines

Apache Kafka has become a foundational technology for building real-time data pipelines, enabling businesses to process and analyze massive amounts of data efficiently. Instaclustr for Apache Kafka takes this powerful open source tool to the next level by offering a fully managed, scalable, and secure Kafka environment. For organizations looking to optimize their data pipelines without the complexity of managing infrastructure, Instaclustr provides a tailored solution that lets you focus on innovation rather than maintenance.

A data pipeline is the lifeblood of modern enterprises, serving as the mechanism that transfers, processes, and enriches data between various systems, applications, and data stores. With Instaclustr’s fully managed solution, Kafka seamlessly integrates into these pipelines, ensuring robust data streaming, event sourcing, and real-time processing capabilities. 

Whether you’re building customer activity trackers, machine learning pipelines, or processing IoT data, Apache Kafka’s ability to handle high-throughput and low-latency data streams is unparalleled. Instaclustr makes it even easier by managing everything from provisioning and monitoring to scaling and fault tolerance.

What sets Instaclustr apart is its dedication to providing a secure and reliable environment for Kafka-based data pipelines. The platform ensures consistent performance with zero-downtime upgrades, 24/7 expert support, and enterprise-grade security features, including encryption and fine-grained access controls. For businesses operating in data-intensive environments, this level of operational reliability is key to maintaining the agility needed to stay ahead in a competitive landscape.

By leveraging Instaclustr for Apache Kafka, businesses can unlock the full potential of their data pipelines with confidence. The combination of Apache Kafka’s capabilities and Instaclustr’s expertise creates a winning formula for handling today’s real-time data challenges.

For more information:

• How to size Apache Kafka® clusters for Tiered Storage: Part 1–A Kafka performance model for SSDs, network, and I/O
• 12 Kafka Best Practices: Run Kafka Like the Pros
• Apache Kafka cluster: Key components and building your first cluster
• Apache Kafka® versions: Managing upgrades and tips for success