Integrating Kafka with ClickHouse for Real-time Stream Processing in Fintech


A financial technology (FinTech) company may integrate Kafka with ClickHouse to enable real-time stream processing of financial data, across a variety of use cases such as merchant settlements, fraud analytics, etc.

Runbook to integrate Kafka with ClickHouse for Fintech

Here is an example of how this might work:

  1. Data collection: The FinTech company collects financial data, such as stock prices, trade data, and customer transactions, from various sources, such as stock exchanges and banking systems, and sends it to a Kafka cluster.
  2. Data ingestion: The Kafka cluster ingests the data and makes it available for real-time stream processing.
  3. Data processing: A stream processing application, such as Apache Flink or Apache Storm, subscribes to the Kafka topic and processes the data in real-time. The application performs tasks such as validating the data, applying business logic, and aggregating the data.
  4. Data loading: The processed data is then loaded into a ClickHouse cluster for real-time analytics and reporting. The ClickHouse cluster is optimized for high-performance analytical queries and can handle millions of rows per second.
  5. Data visualization: The FinTech company uses a data visualization tool, such as Grafana or Tableau, to create real-time dashboards and reports based on the data in ClickHouse. These dashboards provide insights into key metrics, such as stock prices, trade volumes, and customer behavior.
  6. Data archiving: The data in ClickHouse is also archived to a more cost-effective storage solution for long-term retention, such as Amazon S3 or Google Cloud Storage.
  7. Alerts and notifications: The FinTech company sets up alerts and notifications based on the data in ClickHouse to notify relevant parties of important events, such as abnormal stock price fluctuations or suspicious customer transactions.


By integrating Kafka with ClickHouse, the FinTech company can process financial data in real-time and make data-driven decisions that can help improve their business. The real-time analytics and reporting provide a competitive edge over other companies who rely on batch processing and delayed insights.

ChistaDATA’s ClickHouse Consultative Support(24*7) and Managed Services

  • ChistaDATA provides full-stack ClickHouse Optimization. We deliver elite-class Consultative Support (24*7) and Managed Services for both on-premises ClickHouse infrastructure and Serverless/Cloud/ClickHouse DBaaS operations.
  • ChistaDATA Server for ClickHouse (and all tools essential for Data Ops. @ Scale) will be Open Source (100% GPL forever) and free. We are committed to helping corporations in building Open Source ColumnStore for high-performance Data Analytics.
  • Global Team available 24*7 for ClickHouse Consultative Support and Managed Services.
  • Our team has built and managed Data Ops. Infrastructure of some of the largest internet properties. We know very well the best practices for building optimal, scalable, highly reliable and secured Database Infrastructure @ scale.
  • Lean Team Culture: Startup-friendly and specialists in DevOps. and Automation for Database Systems Maintenance Operations.
  • Transparent pricing and no hidden charges – We have both fixed-priced and flexible subscription plans.
  • Based out of San Francisco Bay Area. But, we have global teams operating from 11 cities worldwide to deliver 24*7 Consultative Support and Managed Services for ClickHouse.

To know more about Clickhouse and Kafka, do consider reading the following articles:


About Shiv Iyer 225 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.