FenWarehouse

What FenWarehouse is for

Reporting, look-through, and AI fail for a boring reason: they query whatever happened to land in the warehouse, including uncleansed vendor files and conflicting internal extracts. FenWarehouse exists so the analytical plane is fed from data DataHub has already validated and mastered. Client reporting, management reporting, and downstream models then share the same cleaned foundation.

It covers the investment data domains firms expect to analyse together: reference and static data, market data, analytics, transactions and valuations, risk and performance measurement and attribution, across public and private markets — equities, fixed income, cash, derivatives, OTC, and alternatives. The warehouse is designed for fast reads of large volumes, with entities linked for look-through rather than for operational edit.

Not a second operational data store

The operational data store in DataHub is where quality rules, mastering, alerts, overrides, and approvals happen. FenWarehouse is a projection of what you choose to publish from that store. Tables and fields are included explicitly. Primary keys required for a published table go with it. If a table is not marked for the warehouse, it stays operational-only.

That separation is the point. Analysts and AI applications should not compete with operational workloads, and they should not see a private copy of data that bypassed four-eye control. FenWarehouse is the read-optimised, historically tracked side of the same governed model.

How data gets there

• Minimal extra configuration once the operational model is in place: mark dictionaries, tables, and fields for inclusion.
• Automatic push from DataHub when mastered data changes, so the warehouse is not a weekend batch project unless you want it to be.
• Update modes that fit the use case: real-time, near-real-time, or batch.
• Bi-temporal tracking with valid-from and valid-to dates so a later correction does not silently rewrite history.

The result is a warehouse that stays consistent with the operational golden record without asking a separate integration team to rebuild every mapping. FenRecon can still compare warehouse extracts to other books if the firm needs that control; FenWarehouse does not replace recon.

Investment data model

FenWarehouse is described on the product landing as a robust investment data model covering essential domains from research and analytics through valuations and accounting. It is designed to be adapted to the firm’s process rather than forcing a single vendor’s hard-coded industry model. The physical layout stays in the warehouse platform the client operates; Fencore does not publish a field-level schema as marketing material.

What matters commercially is the contract: cleansed and validated market, reference, and investment data is what gets pushed; look-through and advanced analytics sit on that set; reporting and AI inherit the same quality bar so “garbage in, garbage out” is not reintroduced at the last hop.

Snowflake by default, other hosts on request

Snowflake is the default host because it is the usual choice for an analytical data plane in this industry. If the client’s standard is Databricks, Amazon Redshift, or another supported warehouse technology, FenWarehouse can target that instead. The product claim is flexibility of host, not a requirement to standardise on Snowflake if the firm already has a different analytical standard.

Cori and downstream AI

Cori is Fencore’s in-product assistant inside DataHub. It queries documentation, configuration, metadata, authorised data, and alerts, and it builds pipelines from natural language. FenWarehouse is the dataset you want other AI and BI tools to use once Cori and human operators have done the operational work. Cori does not replace warehouse governance: inclusion flags, licences, and access still decide what is published. See /products/datahub/full.

Where it sits in the suite

FenWarehouse is licensed separately from the core DataHub operational store. Brochure landing: /products/fenwarehouse. DataHub full overview: /products/datahub/full.