Market Data At Scale: Choosing A Time-Series Database And Building A Tick Store For Trading With kdb+, ClickHouse And QuestDB

Billions of rows - A typical hedge fund research database holds billions of rows of tick data; history runs to trillions  ·  Columnar - Time-series and analytical stores are columnar - the layout that makes market-data queries fast  ·  kdb+ ↔ open source - kdb+ remains entrenched at top banks; ClickHouse and QuestDB now cover 80-90% of the need at a fraction of the cost  ·  Ingest + query - A tick store must handle a relentless write firehose and fast historical queries at once

Ask anyone who has built serious trading or quant infrastructure what the system quietly depends on, and the answer is the tick store: the time-series database that captures and serves the staggering volume of data markets generate - trade prints, quote updates, order-book snapshots - accumulating into billions of rows for a single research database and trillions across full history. Everything a quant does downstream - research, backtesting, signal generation, analytics - is gated by how well that store ingests the firehose and how fast it answers questions over history. Choosing and building it is therefore one of the highest-leverage infrastructure decisions a trading-tech team makes, shaping both capability and cost. For years the default answer was kdb+; in 2026 open-source challengers have genuinely changed the calculus, and this playbook is a developer-and-enterprise guide to navigating it.

Why Market Data Needs A Special Kind of Database

Market data breaks ordinary databases because of its shape and volume. It is time-series by nature - an endless, ordered stream of timestamped events - and the questions you ask of it are analytical: aggregate this instrument over this window, compute this statistic across billions of rows, reconstruct the book at this moment. General-purpose row-oriented databases, optimised for transactional reads and writes of individual records, handle this poorly. Time-series and analytical databases are columnar: they store each column contiguously, so a query touching a few columns over a huge range reads only what it needs and compresses beautifully, because adjacent values in a column are similar. That columnar layout is the single technical reason these databases are fast for market data - it turns 'scan billions of rows' from impossible into routine. It is also why the contenders in this space - kdb+, ClickHouse, QuestDB and others - are all, at heart, columnar analytical engines tuned for time-ordered data.

The Incumbent: kdb+

For decades, kdb+ has been the gold standard for tick data, and it remains entrenched at the top of the industry - Goldman Sachs, JPMorgan, Morgan Stanley, Citigroup, Barclays, Deutsche Bank and most major hedge funds run it for tick storage, real-time analytics and time-series research. Its reputation is earned: a well-tuned kdb+ database can query trillions of rows of historical tick data in seconds, and its query language, q, is extraordinarily expressive and fast for exactly the time-series operations quants need. Knowing kdb+ and q remains a genuine differentiator for quant developer and researcher roles at sell-side banks and hedge funds. The catch is cost and accessibility: kdb+ licensing runs to hundreds of thousands of pounds a year (commonly cited in the £250k-£500k range), and q is a famously terse, steep-learning-curve language. For the largest institutions the performance justifies it; for smaller firms and startups, that price is often prohibitive - which is precisely the gap the open-source challengers have moved into.

The Challengers: ClickHouse And QuestDB

The 2026 reality is that kdb+ faces serious open-source competition, and for many teams the challengers are now the right default. ClickHouse is a columnar analytical database built for enormous event datasets with fast columnar aggregation, particularly strong when writes arrive in batches, and it has become a popular choice for financial market data and analytics at very large scale. QuestDB is a time-series database designed for high-throughput ingestion and fast time-based queries with a familiar SQL interface, making it approachable for teams that do not want to learn q. Alongside them sit TimescaleDB, InfluxDB, and increasingly lightweight polars-plus-parquet workflows for research. The pattern that has emerged: the open-source stack can achieve something like 80-90% of kdb+'s functionality at a small fraction of the cost, which makes it the pragmatic choice for startups and mid-sized firms, and increasingly for large firms' less latency-critical workloads. You give up some of kdb+'s peak performance and its ecosystem, but you gain approachability, SQL familiarity, and a cost structure that does not require an institutional budget.

• kdb+ - peak performance and a deep institutional ecosystem; entrenched at top banks; very expensive and built on the terse q language.
• ClickHouse - columnar analytical powerhouse for huge datasets and batch writes; excellent for large-scale market-data analytics, SQL-friendly.
• QuestDB - time-series database optimised for high-throughput ingest and fast time queries, with an approachable SQL interface.
• TimescaleDB / InfluxDB / polars + parquet - further options across the spectrum from Postgres-compatible to research-workflow lightweight.

How A Tick Store Is Actually Architected

Whatever engine you choose, a production tick store solves the same architectural tension: it must simultaneously absorb a relentless real-time write firehose and serve fast queries over vast history. The common pattern separates these concerns. A hot path ingests live market data at high throughput, often buffering and batching writes because most of these engines ingest far more efficiently in batches than row-by-row. A historical store holds the accumulated tick history in compressed columnar form, partitioned by time (and often by instrument) so queries prune to just the relevant slices instead of scanning everything. Careful schema and partitioning design is where much of the real performance comes from - getting the time and symbol partitioning right can be the difference between a query that returns in a second and one that takes minutes. And the whole thing must be operationally sound: reliable ingestion that does not lose data, monitoring of write and query performance, and a storage strategy that keeps cost sane as the data grows without bound. The engine choice matters, but the architecture around it - ingest design, partitioning, schema, operations - is what determines whether the tick store actually performs in production.

How To Choose

The decision comes down to matching the tool to your scale, budget and use case honestly. If you are a large institution with the most demanding low-latency, high-scale needs, deep existing q expertise, and the budget, kdb+'s peak performance and ecosystem may well justify its cost. If you are a startup, a mid-sized firm, or a large firm's less latency-critical workload, the open-source stack - ClickHouse for big analytical workloads, QuestDB for high-ingest time-series with SQL approachability - very likely gives you the great majority of what you need at a fraction of the cost and with a far gentler learning curve, and is the sensible default. And for research and backtesting specifically, don't overlook lightweight polars-plus-parquet workflows, which can be remarkably effective without any database server at all. The mistake is defaulting to kdb+ because it is what the big banks use when you are not a big bank, or reaching for the cheapest option when your workload genuinely needs institutional-grade performance. Choose by honest assessment of your actual requirements, not by prestige or by price alone.

The tick store is the quiet foundation everything in a quant stack rests on. For years the answer was 'kdb+, if you can afford it'. In 2026 the honest answer is 'match the engine to your scale and budget' - and for most teams that no longer means the six-figure licence.

- BraivIQ Engineering

The Bottom Line

Market data at scale demands a specialised, columnar, time-series database, and the tick store built on it is one of the most consequential pieces of trading infrastructure a team owns - it gates the speed and cost of all the research and analytics above it. kdb+ remains the entrenched, blistering, expensive incumbent; ClickHouse and QuestDB have made open-source tick stores genuinely viable, covering most of the need at a fraction of the cost and opening serious market-data infrastructure to firms that could never justify a six-figure licence. But the engine is only half the job: the architecture around it - batched ingest, time-and-symbol partitioning, careful schema, sound operations - is what makes a tick store actually fast and reliable in production. For trading-tech teams, choosing and building this well is exactly the kind of unglamorous, high-leverage infrastructure work that BraivIQ takes seriously - because everything downstream depends on it. Educational engineering guidance only - not financial advice.

References & Further Reading

• QuestDB - The best time-series databases in 2026 (and how to choose): https://questdb.com/blog/best-time-series-databases/
• ClickHouse - How to use ClickHouse for financial market data: https://clickhouse.com/resources/engineering/what-is-time-series-database
• Quantt - kdb+/q tutorial 2026: time-series database for quants: https://www.quantt.co.uk/resources/kdb-q-tutorial
• Time-series databases for quant: kdb+, ClickHouse, InfluxDB and what quant firms use: https://www.techinterview.org/post/3233474600/time-series-databases-quant/
• Storing and analyzing 160B quotes in ClickHouse (a real-world tick store): https://rafalkwasny.com/clickhouse-tick-store