ClickHouse In Capital Markets

ClickHouse is a high-performance OLAP database currently experiencing a wave of adoption. Open-sourced in 2016 and commercialised in 2022, ClickHouse now has over 4,000 customers including many financial services businesses.

ClickHouse is experiencing rapid adoption in capital markets. This includes technology-driven buy-side firms such as hedge funds, market makers and systematic trading firms, but also large sell-side banks adopting it for use cases that span both front and back office.

As with many industries, these businesses are capturing and managing increasing amounts of data, and using that data in increasingly sophisticated ways driven by the demands of their users, customers and the markets they serve.

Capital markets businesses are also under growing pressure from regulators in areas such as market abuse, compliance, fraud detection and best execution monitoring. This generates significant regulatory demands on data infrastructure and drives the need for scalable real-time analytics.

Against this backdrop, firms are increasingly needing to modernise their data infrastructure, and are increasingly selecting ClickHouse as their next-generation platform due to its low-latency capabilities, ease of use and attractive cost/performance metrics.

A Differentiated Database

At its core, ClickHouse is a highly capable OLAP database.

For vanilla requirements such as storing historical information about trades, positions, market data or derived analytics, it is highly performant at an attractive cost point. It is relational and administered and accessed via SQL, which makes skills easy to find and integration straightforward. This democratises data access across the business.

ClickHouse is however much more capable than other cloud data warehouse offerings, and especially so wherever latency matters. ClickHouse can ingest data extremely quickly, process and transform it, then make it available for querying immediately at massive scale. This makes ClickHouse suitable for real-time analytics and supporting use cases where low-latency responses are essential.

ClickHouse also handles extremely high concurrency in terms of both ingestion and readers. This makes it feasible to power user-facing analytics and applications which are highly interactive in a way that other data warehouses cannot serve.

ClickHouse can be deployed flexibly to match a firm's risk and operational profile, whether self-managed on-premise, bring-your-own-cloud (BYOC) inside your own cloud account, or as the fully managed ClickHouse Cloud service. The managed and BYOC options in particular remove much of the operational overhead of running a cluster, while keeping data and compute under your control.

Why ClickHouse Is Different

ClickHouse's features make it uniquely suited to capital markets workloads, bringing significant architectural benefits:

• It's Fast. Query complex real-time datasets of billions of rows in milliseconds.
• It's Scalable. Built for big-data volumes and high concurrent access from many users.
• Simplifies Your Stack. Consolidate on one database and avoid complex ETL and transformation work.
• It's Simple. SQL and relational, with no awkward JSON APIs or proprietary languages.
• Strong Cloud Product. Fully managed ClickHouse Cloud that autoscales to your workload.
• It's Open Source. A fully open-source, self-managed option with no licensing lock-in.
• It's Cost Effective. A scalable cost model, up to ~50% lower TCO than competitors like Snowflake.
• It's Secure. PCI DSS, SOC 2 and ISO 27001, with RBAC, encryption and audit logging.

Real-Time Analytics

The low-latency capabilities of ClickHouse make it suitable for real-time use cases that reach deep into the front office:

Materialised Views. A common challenge is users pulling large volumes of raw, unaggregated data. ClickHouse's incremental materialised views pre-calculate aggregations as data is ingested: total traded in a book, OHLC candlesticks, even simple risk metrics like value at risk, or algo metrics such as VWAP and TWAP.

Temporal & ASOF Joins. Specialised ASOF joins match each row to the nearest preceding value in time, ideal for aligning a trade with the prevailing quote or market-data snapshot, a notoriously awkward operation in conventional warehouses.

Streaming Ingestion. ClickHouse integrates directly with streaming sources such as Apache Kafka, ingesting events as they arrive so data is queryable within moments, with no separate batch pipeline required.

Low-Latency Serving. Because it sustains high concurrency at low latency over the freshest data, the same engine can power live dashboards, monitoring and user-facing analytics without a separate serving layer.

Built-In Functions. ClickHouse ships with over a thousand functions for numerical processing and array manipulation. Complex calculations that would otherwise be pushed into application code can run directly in the database.

Front Office Use Cases

ClickHouse can support a broad range of front-office workloads where latency is measured in milliseconds and where data volume and concurrency are the dominant challenges.

ClickHouse does not have to be adopted wholesale. Thanks to its open integration architecture (standard SQL, native connectors and broad support across the data ecosystem), it can be deployed in a targeted way alongside existing systems, proving its value on a single workload before expanding into others.

Simplification & Consolidation

Simplification and consolidation comes up in a great many conversations. A typical financial services business might run tens of different data stores: legacy data warehouses, more modern data platforms, time-series databases, search databases, and observability and security platforms. Each tends to come with its own licence, its own operational burden and its own specialist team, and the cost and complexity of running them all quickly adds up.

ClickHouse is unique in that it can credibly serve several of these at once. You could bring your machine-generated log data, your data warehousing data and your time-series data, and put it all in the same database, accessed through a single, familiar SQL interface.

Consolidating in this way reduces the number of moving parts your teams have to learn, secure and maintain. It also removes much of the data movement and duplication that exists purely to copy information from one specialised store into another, which in turn cuts down on the pipelines that so often break and need looking after.

You then have the ability to query and join those data sets, for example understanding the impact of an outage on your algo metrics, or correlating a spike in market data volume with downstream latency. Bringing previously siloed data together in one place often surfaces insights that were simply not visible when it was spread across separate systems.

Powering AI Workloads

Every financial services business is thinking about how to use AI, giving traders and portfolio managers the ability to question their data and model scenarios, helping compliance and client teams work more efficiently, or embedding it within trading strategies and risk management.

Many databases struggle, or see costs spike, under AI-generated load. A single agentic question can fire 10 to 20 queries in parallel which all require a rapid response. The volume of agentic analytics and agentic workflows is only going to increase.

ClickHouse is uniquely suited here: it handles the concurrency, responds with low latency over very fresh data, and offers strong observability through integrations like Langfuse, part of the ClickHouse family.

Reduced TCO

The cost conversation around ClickHouse is highly relevant. Many financial services businesses are stuck with legacy vendors that are expensive to run and have raised prices as they have fallen out of favour.

Specialist databases such as kdb+ are famously expensive, and rely on costly, hard-to-find skills.

Meanwhile, more modern platforms like Snowflake and Databricks are capable, but they are not designed for low-latency, high-concurrency workloads. Scaling them up can break the cost profile, to the point where the platform costs more to run than it generates.

ClickHouse sits in the sweet spot. Its unit costs are highly competitive, and thanks to its concurrency, scalability and performance, each unit of compute carries more of a workload. With a relatively small deployment you can do a great deal, and save a significant amount of money.

Conclusion

ClickHouse is a genuinely differentiated database for capital markets. It combines the low latency and high concurrency demanded by the front office with the cost efficiency, scalability and SQL familiarity needed across the wider business.

Firms are using it to power real-time analytics, consolidate fragmented data estates, support emerging AI workloads, and materially reduce the cost of their data infrastructure, often replacing several legacy and specialist systems with a single platform.

For most firms the right next step is a focused proof of value: pick one workload where latency, concurrency or cost is a live pain point, stand ClickHouse up alongside the existing stack through its open integration architecture, and measure the difference.

From there, adoption can expand workload by workload at a pace that suits the organisation.