ClickHouse – Real-time Analytics with Kafka integration

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ClickHouse – Real-time Analytics with Kafka integration


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Objective

This guide explains how to integrate ClickHouse with Kafka using Aiven: setting up the connection, managing data formats, and enabling real-time ingestion for Analytics.

This guide explains how to connect ClickHouse® to Kafka® via Aiven integrations for real-time data exchange.

Requirements

Instructions

Preparing your Kafka cluster

Before connecting ClickHouse, ensure your Kafka cluster is operational:

  • Provision a Kafka cluster using Aiven or another managed provider;
  • Collect the required connection details (brokers, authentication method, SSL certificates if applicable);
  • Verify that the Kafka cluster is reachable from the ClickHouse network.

Creating the Apache Kafka integration in Aiven

Before configuring ClickHouse tables, create the Apache Kafka integration in Aiven. This establishes the managed connection for ClickHouse to consume Kafka data.

Follow the official Aiven documentation to create the integration.

During this step, you will:

  • Select or create an Apache Kafka data source;
  • Define Kafka topics, consumer groups, and data formats;
  • Create Kafka-backed tables exposed to ClickHouse;
  • Enable the integration.

Once enabled, the Kafka data source becomes available for use in ClickHouse.

Configuring ClickHouse for external connections

ClickHouse must accept connections from your Kafka cluster:

  • Access your ClickHouse instance configuration;
  • Enable external connections and whitelist Kafka cluster IPs or network ranges;
  • Confirm ClickHouse is running a stable version supported by Aiven.

Always use a secure connection (TLS) between ClickHouse and Kafka for production environments.

Creating Kafka engine tables in ClickHouse

ClickHouse uses the Kafka engine to consume topics directly:

CREATE TABLE kafka_events (
    event_time DateTime,
    user_id String,
    action String
) ENGINE = Kafka
SETTINGS
    kafka_broker_list = 'your-kafka-broker:9092',
    kafka_topic_list = 'events_topic',
    kafka_group_name = 'clickhouse_consumer_group',
    kafka_format = 'JSONEachRow';
  • Replace kafka_broker_list, kafka_topic_list, and kafka_group_name with your cluster-specific values.
  • kafka_format must match the data format used in your Kafka topics (JSON, Avro, etc.).

Persisting data with materialized views

To store incoming Kafka data permanently:

CREATE MATERIALIZED VIEW events_mv TO permanent_events AS
SELECT *
FROM kafka_events;

This ensures real-time ingestion into a permanent ClickHouse table for Analytics queries. Materialized views allow decoupling ingestion from querying, improving performance.

Validating the integration

Produce test messages in your Kafka topic and query the ClickHouse table or materialized view:

SELECT * FROM permanent_events LIMIT 10;

Confirm that data is ingested correctly and timestamps, formats, and encodings match expectations.

Monitoring and optimizing

  • Track Kafka consumer lag to ensure no data is missed.
  • Monitor ClickHouse ingestion metrics (rows/sec, disk usage).
  • Tune Kafka batch sizes and ClickHouse buffer settings for optimal throughput.
  • Implement alerting for failures, slow queries, or high lag.

Go further

ClickHouse service capabilities

Official Aiven documentation to integrate Kafka

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