What's the difference between a data product and a data feed for financial data providers?

Summary

• A data feed is a delivery mechanism: a continuous stream or scheduled file of records handed to a consumer. A data product is a managed, owned asset built for consumers, with quality guarantees, documentation, discoverability, and access controls that travel with the data.
• For a financial data provider, a feed transfers raw records and leaves cleaning, schema changes, and entitlement enforcement to the customer. A data product ships the governance and trust alongside the data.
• The five marks of a data product are quality and observability, semantic consistency, security, discoverability, and a named owner accountable for its lifecycle.
• A data product is defined by a data contract: schema, usage and PII policies, quality checks, security and entitlements, freshness SLAs, and clear ownership.
• Databricks lets financial data providers publish governed data products with Unity Catalog, share them in place through Delta Sharing, list them on Databricks Marketplace, and collaborate in Clean Rooms, without copying data to each consumer.

What's the difference between a data product and a data feed for financial data providers?

A data feed is how data moves: a continuous stream or scheduled file of records delivered to a consumer. A data product is data treated as a managed product, with an owner, quality guarantees, documentation, discoverability, and access controls that stay attached to it. For a financial data provider, that difference is decisive. A feed hands over raw records and leaves the customer to clean them, absorb schema changes, reconcile gaps, and enforce entitlements. A data product delivers the same data with the governance, quality SLAs, and lineage built in, so consumers can find it, trust it, and use it without reverse-engineering a spec. The data can be identical; the product thinking around it is what changes.

What is a data feed?

A data feed is the raw delivery of records, a transport and a format. Market prices, reference data, corporate actions, and ESG signals have long shipped this way: a file drop or a streaming connection the consumer subscribes to. A feed says nothing about quality, ownership, or how to discover what is inside. The recipient carries the burden: parsing the payload, validating values, absorbing schema drift when the provider changes a column, and enforcing who may see which fields. Feeds still matter as a delivery channel, but on their own they push integration and trust downstream to every customer.

What is a data product?

A data product applies product thinking to a data asset. It goes beyond raw delivery to add ownership, quality guarantees, and active lifecycle management, and it can be more than a table: a model, a dashboard, or another derivative that helps a consumer reach an end goal. A well-built data product meets five criteria: quality and observability (accuracy, timeliness, and documented, monitored quality metrics); semantic consistency (shared definitions so datasets combine meaningfully); security (explicit access, sharing, and acceptable-use policies); discoverability (published in a catalog or marketplace with sample notebooks); and ownership (a named owner accountable for the full lifecycle).
Underneath sits a data contract, designed by the producer with consumers in mind. It formalizes the schema, usage and PII policies, quality checks and constraints, security and entitlements, freshness and retention SLAs, and the responsibilities for change. The product then moves through a defined lifecycle from inception and design to publish, govern, consume, and graceful retirement.

Key differences

Dimension	Data feed	Data product
Primary unit	A stream or file of records	A managed asset (tables, and often models or dashboards)
Governance	Left to the consumer after delivery	Access controls, entitlements, and lineage travel with the data
Quality	Best-effort; the consumer validates	Defined quality metrics, monitored against stated SLAs
Documentation and discovery	An out-of-band spec	Cataloged, searchable, with sample notebooks
Ownership	Often none after the handoff	A named owner accountable for the lifecycle
Change management	Schema changes can break consumers silently	Versioning and a formal change process protect backward compatibility
Distribution	Copied or replicated to each consumer	Can be shared in place, without copying

How Databricks approaches data products for financial data providers

Databricks lets a financial data provider build, govern, and distribute data products on one platform. Unity Catalog is the governance substrate: centralized metadata, column-level lineage, access controls, and data contracts expressed through tags, comments, and schema, with versioning and change management to keep consumers stable as products evolve.
Delta Sharing distributes those products without copying. It is an open protocol, so recipients can consume a share with the tools they already use, even without a Databricks account, while the provider keeps row and column policies, freshness control, revocation, and full audit trails on their side. Databricks Marketplace is the discovery and publication surface: a provider prepares a share with supporting notebooks and documentation, lists it with terms and metadata, then reviews access requests and tracks usage. For joint analysis with counterparties or partners, Clean Rooms let multiple parties run approved code over shared data with no raw-row exposure on either side.
Quality is enforced upstream. Lakeflow Pipelines build the datasets with declarative quality expectations and constraints, so a product can meet the freshness and accuracy SLAs its data contract promises. For the design pattern, see Building high-quality and trusted data products on Databricks and the partner and data-provider architectures.

FAQs

Is a data feed still useful if I build data products?

Yes. A feed is a delivery channel, and a data product can be delivered as a stream when consumers need continuous updates. The point is not to abandon feeds but to wrap the data in ownership, quality, governance, and discoverability so consumers get a trustworthy product, not just bytes on a schedule.

Why do financial data providers care about the distinction?

Because their customers pay for trust and usability, not just records. Entitlements, quality SLAs, lineage, and clear change management cut integration cost and support risk for subscribers, and let a provider monetize governed products through a catalog rather than shipping raw files to each client.

What is a data contract?

A data contract is the agreement a data product publishes for its consumers: the schema, usage and PII policies, quality checks, security and entitlements, freshness and retention SLAs, and who owns and maintains it. It is designed by the producer with the consumer's needs in mind, and it is what makes a product dependable.