Best Data Products and Data Contracts: The New Face of Data Platforms 2026?
Data teams are moving away from the “dashboards on request” mindset and are now focusing on creating data products that are reusable, reliable, and other teams can simply take it for granted. Data Products and Data Contracts To facilitate such work in large scale, companies are implementing concepts like data products and data contracts.
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What is a data product?
A data product refers to a curated, dependable, and documented dataset or API which caters to a specific business use case, with clearly defined ownership and SLAs. It is marketed as a product and not a one-time data dump: in other words, it is versioned, monitored, and eventually improved over time.
Examples:
“Customer 360” table that provides data for marketing, sales, and support. “Revenue by cohort” mart that is utilized by finance and growth teams. Feature store tables that serve multiple ML models. The point of a data product is that it has a particular consumer, clear semantics, and a definite standard of quality and availability.
What are data contracts?
Data contracts refer to agreements between data producers (source systems, app teams) and data consumers (analytics, ML, downstream apps) that provide a detailed description of:
- Schemas and field meanings
- Data freshness and latency expectations
- Quality rules (e.g. nullability, allowed values, uniqueness, etc.)
- Change management (e.g. the way and time of breaking changes)
By collaborating through contracts, which are usually supported by tooling that detects and enforces them, producers and consumers avoid the situation of column-changing silently in a production database that consequently breaks downstream pipelines.
Reasons why this trend is important now
The cost of “silent breakage” is on the rise as more and more teams create reports, ML models, and reverse ETL workflows from shared data. Data products and contracts are means to lessen the following risks:
Unexpected schema changes that result in breakage of dashboards and features. Uncertainty about which table is the “source of truth.” Repetition of work due to each team rebuilding similar transformations. Moreover, they are consistent with the concept of data mesh, which implies that domain teams have control over “their” data products but, at the same time, adhere to platform-wide standards.
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Ways to begin the implementation of data products and contracts:
- Select 2–3 districts of high value (e.g. customers, orders, events) and create official data products for each.
- Define contracts for these products: schemas, meanings, SLAs, owners, change processes.
- Put monitoring and alerts in place for schema and quality changes; let the alerts be accessible to both producers and
