Navigating the Build vs. Buy Dilemma With OneTick Market Data

Stop building market data infrastructure. Start building alpha.

Clean, high-quality market data is a prerequisite for quant research, trading analytics, backtesting, execution analysis, and AI/ML feature engineering. But building the infrastructure to source, store, normalize, validate, and query that data is a major undertaking.

In this webinar, Mick Hittesdorf, Product Architect for OneTick Cloud, shares practical lessons from both sides of the build-versus-buy decision. Having previously led an in-house market data platform build, Mick explains what the build path really involves — and how a managed cloud approach can help teams accelerate time to value.

Summary

In this on-demand session, Mick explains the build-versus-buy dilemma through the lens of real capital markets data infrastructure.

He covers what is required to build a robust market data platform supporting historical and real-time data, including infrastructure deployment, tick data sourcing and validation, symbol cross-referencing, corporate action adjustments, consolidated feeds, real-time reliability, and ongoing maintenance.

He then shows how OneTick Cloud provides an alternative: a fully managed cloud environment with institutional-grade market data, automatic updates, daily data loads, scalable compute, and access through OneTick Py, SQL, REST, and optional bulk delivery formats such as CSV or Parquet.

The session includes both strategic guidance and practical examples, including a Jupyter notebook walkthrough using OneTick Py.

What you will learn:

• What it really takes to build in-house: Mick explains the engineering effort required to stand up a market data platform, from deploying infrastructure and loading tick history to integrating real-time feeds, reconciling schemas, handling timestamps, and maintaining pipelines as exchange specs and regulations change.
• Why clean data is the real bottleneck: The webinar explores why quants and other data users often spend substantial time validating, curating, cleaning, aligning, and reconciling data before they can perform the higher-value work they were hired to do.
• How corporate actions and symbology affect analysis: Mick shows why corporate action adjustments, symbol mapping, Bloomberg tickers, ISINs, CUSIPs, FIGI, exchange identifiers, calendars, and reference data matter when preparing market data for reliable analytics.
• How OneTick Cloud changes the time-to-value equation: Learn how a managed cloud environment can provide access to the datasets, infrastructure, and analytics needed to start working with market data faster, without beginning from an empty platform or waiting months for internal build work.
• How to work with market data through OneTick Py: The webinar includes practical examples of querying trades and quotes, accessing NBBO, joining trades with quotes, applying corporate action adjustments, querying order books, and analyzing book imbalance.
• How to evaluate vendor options: The Q&A covers how to think about vendor evaluation, including data quality, coverage, delivery format, technology model, interoperability, real-time versus T+1 data, and how cloud data can be combined with proprietary in-house datasets.