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In the realm of real-time analytics and Online Analytical Processing (OLAP) systems, ClickHouse has emerged as an undisputed leader, redefining how developers build scalable, efficient, and lightning-fast analytics pipelines. As we move through 2025, its practical applications have expanded across a variety of data-intensive industries, from AdTech and financial services to IoT analytics, observability platforms, and embedded BI solutions.
ClickHouse’s true strength lies in its ability to support high-concurrency analytical workloads, incredibly fast data ingestion, and sub-second query execution across massive datasets. Built from the ground up for speed and scalability, ClickHouse's columnar storage engine, vectorized query processing, and distributed architecture offer a compelling alternative to traditional OLAP databases and cloud-native analytics tools.
This blog explores the most dominant ClickHouse use cases in 2025, emphasizing its value for developers, engineers, data architects, and decision-makers who are focused on building and maintaining high-performance data platforms.
Digital advertising platforms rely on extremely fast analytics engines to remain competitive in real-time bidding (RTB) environments. ClickHouse in 2025 has become the backbone of many AdTech systems, enabling developers to process, aggregate, and analyze billions of ad impressions, bid requests, and user interactions per day, all while delivering real-time insights.
Companies like Cognitiv and similar programmatic advertising providers leverage ClickHouse to create offline feature stores that power machine learning pipelines in real-time. ClickHouse allows these platforms to update bidding models, adjust campaign logic, and optimize targeting strategies on the fly. Its blazing-fast performance supports sub-second querying across billions of rows, empowering data scientists and engineers to iterate on ML models quickly and efficiently.
Thanks to ClickHouse’s column-oriented design, developers can conduct multi-dimensional queries like CTR, conversion rates, user cohorts, and geo-based segmentation with incredible speed. Developers also benefit from ClickHouse’s ability to compress large ad log datasets (reducing storage overhead), and its ability to scale horizontally with ease across cloud-native infrastructure.
The importance of these features cannot be overstated. AdTech is fundamentally about speed and precision, and ClickHouse in 2025 delivers exactly that, an ideal choice for real-time analytics in a sector where milliseconds can mean millions.
Financial services is another sector where ClickHouse shines in 2025, thanks to its ability to execute complex analytical queries on time-series data at lightning speed. In a market driven by up-to-the-microsecond decisions, ClickHouse provides the low-latency querying and high ingestion rates necessary for:
ClickHouse’s asof joins, window functions, and parallel processing capabilities make it a perfect fit for developers building systems that monitor price changes, detect trading anomalies, or manage risk exposure in real-time. One of ClickHouse’s biggest differentiators in this space is its ability to handle enormous volumes of tick data with near-zero performance lag.
Unlike traditional relational databases that choke under massive time-series workloads, ClickHouse enables quant teams and financial engineers to interact with billions of financial records using queries that complete in milliseconds. Whether it's calculating the latest NAV of a portfolio or comparing thousands of instruments across time slices, ClickHouse accelerates insights and decision-making.
Furthermore, its advanced data compression algorithms significantly reduce the cost of storing high-resolution financial data, making it ideal for hedge funds, trading firms, and neobanks looking for an edge in both performance and infrastructure efficiency.
In 2025, the rise of digital transactions has also brought a surge in fraud attempts, necessitating smarter, faster detection mechanisms. ClickHouse provides an excellent foundation for fraud detection systems across fintech and e-commerce platforms due to its ability to:
Fraud detection systems must process and correlate diverse event streams such as device fingerprints, login attempts, IP geolocation data, and transaction history. ClickHouse’s native support for complex joins, high-performance filtering, and sub-second analytics empowers developers to build rule-based or ML-enhanced pipelines that flag potential fraud without delaying transactions.
Compared to legacy data warehouses, ClickHouse provides real-time intelligence that’s both cost-effective and scalable. Its materialized views allow the pre-aggregation of high-cardinality metrics, which can be used for dashboards or immediate pattern recognition. This capability is crucial for identifying trends like sudden spikes in failed payments, duplicated logins, or international transaction mismatches.
As a result, ClickHouse has become an integral part of fraud detection pipelines for banks, payment processors, and digital wallets, offering a modern and efficient alternative to traditional batch-processing fraud engines.
Infrastructure and application observability have become cornerstones of DevOps and SRE workflows. Tools like Prometheus, Grafana, and OpenTelemetry generate enormous amounts of telemetry data, logs, metrics, traces, that must be ingested, stored, and queried in real-time.
ClickHouse has firmly established itself in 2025 as a top-tier backend for observability platforms, especially those managing petabyte-scale logging or tracing data. It’s used by companies to build internal observability stacks that rival commercial APM solutions, offering:
ClickHouse supports ingestion rates that far surpass traditional log aggregation tools. Developers can design log analytics dashboards that slice and dice data by service, response code, request path, or region without compromising speed.
Use cases include:
Its sparse indexing, time-range filtering, and column-based architecture enable engineers to monitor their systems with unprecedented granularity and minimal resource consumption. In fact, ClickHouse is often chosen as the default storage backend for companies building custom observability platforms tailored to their stack.
IoT devices, from smart thermostats and agriculture sensors to manufacturing robots, generate continuous data streams that require fast ingestion, processing, and analytics. In 2025, ClickHouse has become a vital component of modern IoT data platforms, providing the scalability, reliability, and efficiency needed to manage billions of sensor readings per day.
ClickHouse’s native support for time-series data, windowed aggregations, and TTL policies allow developers to build robust data pipelines that can ingest data from edge devices, perform on-the-fly aggregations, and drive downstream alerting or visualization systems.
Example use cases include:
Its high compression rates mean that you can store years of granular telemetry at a fraction of the storage cost compared to traditional time-series databases. Additionally, ClickHouse enables developers to run complex statistical models directly on historical sensor data to derive insights without offloading to external processing engines.
By using ClickHouse, companies building IoT platforms achieve both operational efficiency and data-driven agility at scale.
Every digital product today needs analytics. In 2025, ClickHouse has become a cornerstone of product analytics and user behavior tracking systems across SaaS, media, and e-commerce platforms.
Developers use ClickHouse to ingest clickstream data, session logs, user interactions, and A/B test results, all in real-time. Its exceptional performance on high-cardinality datasets makes it ideal for aggregating data across millions of users, thousands of events, and multiple dimensions (geo, device, cohort, page type).
ClickHouse enables:
Compared to Google Analytics or Mixpanel, ClickHouse offers complete flexibility, control over your data, and no sampling. Developers can run granular queries, segment traffic, and visualize trends using Superset, Redash, or Grafana.
Moreover, thanks to its compatibility with Apache Kafka and Fluent Bit, ClickHouse can integrate into most modern data ingestion pipelines, giving full visibility into the health and usage of web and mobile applications.
As machine learning (ML) and Generative AI (GenAI) workloads grow in complexity, the need for scalable feature stores and vector databases becomes paramount. In 2025, ClickHouse is increasingly being used to serve as:
Developers leverage ClickHouse’s aggregate functions, materialized views, and low-latency lookups to manage and serve features that feed real-time models, such as recommendation engines, fraud detectors, or personalization algorithms.
For GenAI applications, ClickHouse is used to store vector embeddings of content, documents, or users, enabling efficient semantic search, clustering, or similarity queries. Its support for vector indexing, ANN (approximate nearest neighbor), and multithreaded execution makes it a compelling alternative to standalone vector databases.
This dual capability, managing structured features and unstructured embeddings, allows ML engineers to consolidate their infrastructure, simplify pipelines, and gain real-time model feedback.
Digital marketers depend on real-time performance metrics to adjust ad spend, tune campaigns, and optimize user funnels. In 2025, ClickHouse is a preferred backend for campaign analytics due to its ability to:
With ClickHouse, marketers gain instant access to CTR, CPM, conversion rates, and ROI calculations, essential metrics for evaluating campaign success. Its multi-tenancy support and isolation between workspaces also make it ideal for agencies and platforms serving many clients concurrently.
Whether it's measuring ad fatigue, audience overlap, or optimizing landing pages, ClickHouse allows real-time experimentation, A/B testing, and deep segmentation with minimal cost.
SaaS companies and analytics vendors often need to provide BI capabilities directly within their products. ClickHouse is now widely used in 2025 as the engine behind embedded analytics due to:
Developers use ClickHouse to power user-facing dashboards, self-service reporting tools, and complex visualizations, without compromising performance or scalability. Unlike monolithic data warehouses that impose query latency, ClickHouse supports interactivity, live data refresh, and on-demand analysis, all at the speed users expect.
The platform also integrates smoothly with REST and GraphQL APIs, allowing developers to build custom analytical interfaces tailored to each client or use case.
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