What market data systems do you build?

Kilobytez offers feed handlers, normalization pipelines, time-series storage, replay tooling, analytics and dashboards. No latency or throughput figures are published, because none has been measured on a production system of ours.

Which data stores do you use?

Kilobytez commonly uses ClickHouse, PostgreSQL, TimescaleDB, and streaming systems depending on the workload and query shape.

Services

Market Data Infrastructure

Capture every tick. Analyze in real-time.

Kilobytez builds data pipelines that ingest, normalize, and store market data, from consolidated vendor feeds to direct exchange connections, with replay and observability treated as part of the build rather than an afterthought.

Key Deliverables

Real-time feed handlers (UDP multicast, TCP, WebSocket)
Data normalization and symbology mapping
Time-series storage (ClickHouse, PostgreSQL, TimescaleDB)
Historical data management and replay
Analytics dashboards and alerting
Cloud and on-premise deployments

Vendor feeds a pipeline can consume

Databento

Polygon

IEX Cloud

Bloomberg

Refinitiv

Direct Exchange Feeds

These are the vendors a feed handler would be written against. Kilobytez has no partnership, reseller arrangement, or existing integration with any of them.

From the wire to the research desk

Illustrative pipeline

1Exchange feeds
Multicast and TCP feeds, each in the venue’s own format.
2Feed handler
Decodes every packet, detects gaps, requests recovery.
3Normalize
One internal message shape for every venue and vendor.
4Order book
Builds and checks depth per symbol, in sequence.
5Time-series store
Ticks and books written with exchange and receive timestamps.
6Replay and research
Any day replayed exactly, for backtests and incident review.

Kilobytez publishes no latency or throughput figures; each pipeline is sized and measured against the client’s own feeds.

What the book looks like downstream

Simulated data

Technologies we build these on

ClickHouse

Blazing-fast analytics for tick data

PostgreSQL

Relational storage with time-series extensions

Kafka

Real-time streaming backbone

AWS

Scalable cloud infrastructure

How we choose the stack

ClickHouse is designed for high-volume columnar analytics and is the usual starting point for tick storage
PostgreSQL with time-series extensions covers a lot of workloads that do not need ClickHouse
Kafka handles the streaming backbone where ingestion and processing need to be decoupled
The right answer depends on your volume and query shape, and we will size it against your data, not a brochure

Kilobytez does not publish ingestion, latency, or storage figures. It has not run a production market data system of its own, and quoting a platform vendor's benchmark as if it were a delivered result is the kind of claim this site was rewritten to remove.