Practice area · Market Data & Analytics Infrastructure
Designed for the queries desks actually run.
Reference data architecture, real-time market data pipelines, and analytical warehouses. Curation, normalization, and big-data integration designed for trading-desk query patterns.
Overview
Market data is only as valuable as the curated layer sitting between raw feeds and the desks that depend on them.
What it is
From feed to fill, with curation in between.
Market data infrastructure is the substrate underneath every desk, model, and report in capital markets. Vendor feeds, direct exchange connections, and OTC data each arrive with their own schemas, latencies, and quirks. The work is to ingest, normalize, curate, and store them so analytical workloads can run without surprises.
We design architectures around the query patterns trading desks actually use — not around generic data-platform marketing. The result is a tick store and a warehouse that answer the questions you ask in the time you have.
Workflow
Sources, pipeline, consumers — connected by curation.
- Sources: vendor feeds, direct exchange connections, and OTC dealer flows.
- Pipeline: ingest, normalize, then curate (highlighted as the value-add step).
- Storage: a time-series tick store and an analytical warehouse.
- Consumers: trading desks, models, and compliance reports.
Deliverables
What you walk away with.
- Source map: vendor feeds, exchange direct, and OTC data — with redundancy and licensing accounted for.
- Ingest and normalization layer: schemas, time-stamping, and corporate-action handling that survive audit.
- Curated reference data: securities master, calendars, and corporate-action history that desks can trust.
- Storage architecture: tick store optimized for time-series queries, warehouse for analytical workloads.
- Consumer-side query patterns: pre-trade, post-trade, compliance, and model-training surfaces — designed deliberately.
Pitfalls
How we don't do it.
- Treating market data as a single project instead of an operational practice with continuous data quality.
- Storing ticks in a general-purpose RDBMS and discovering the cost only at scale.
- Skipping curation — the curated layer is where the value is added, not where it is added later.
- Ignoring corporate actions until a back-test silently lies to you.
Engagement
How we work with you.
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01
Map
Sources, licensing, redundancy, and the desks consuming each feed.
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02
Architect
Ingest, normalize, curate, and storage tuned to the queries desks actually run.
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03
Build
Pipelines, schemas, and observability — wired into your existing platform.
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04
Operate
Continuous data-quality, vendor-change tracking, and capacity reviews.
Want a market-data layer your desks can trust?
Tell us your sources, your asset classes, and the queries your desks run. We'll come back with an architecture and a curation plan tuned to the work.
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