Data Products FAQ

Alation Cloud Service Applies to Alation Cloud Service instances of Alation

This Frequently Asked Questions (FAQ) topic addresses common questions about the Alation Data Products App, Marketplace, and the data product operating model.

What's the difference between a data product Marketplace and a data catalog?

The data catalog documents all your data assets. It’s essential for governance, compliance, and for technical users who work with data sources, including raw data.

The data product Marketplace is where curated, reusable data products are published. These products can be built from catalog assets but are packaged with clear purpose, quality, and structure for easier consumption.

You can think of it this way:

• The catalog is like a warehouse of raw materials.

• The Marketplace is like a store, where items are packaged and ready to use.

Who benefits from the Marketplace?

• Technical Data Consumers (Humans & AI): Discover and access trusted data for decision-making, automation, and analytics.

• Data Producers: Gain visibility into how data is used and deliver consumer-ready data with clear demand alignment

• Data Governance Teams: Enforce quality and compliance standards and guide investment toward high-value products

• CIOs and Business Leaders: Ensure data is treated as a strategic asset and support AI readiness across the organization

What's the difference between data products and data sets?

A data set is a raw, structured collection of data. It often lacks documentation, ownership, or stability.

A data product is designed for reuse and consumption. It includes:

• A clear purpose

• Metadata and documentation

• Access instructions

• A contract for how the data can be used

Are dashboards data products?

No, they aren’t. Dashboards aren’t data products. They are built on top of data products to visualize insights. Data products serve as the reusable, governed foundation.

How do dashboards fit into the Marketplace?

Dashboards remain a key way to consume data. To support discoverability, data product pages can include a Built With This Data Product section, showing dashboards, reports, or data apps built on top of the product. This lets users find both the source data and how it is used, while keeping the data product as the core reusable asset.

Can non-technical data consumers use data products?

Supporting non-technical users is part of the long-term vision. However, the initial focus is on supporting technical users and AI agents. We expect AI to increasingly act as an interface for non-technical users. So, the priority is ensuring data is:

• Reusable

• Well-defined

• Trustworthy

With that foundation, AI tools can deliver user-friendly access without compromising governance.

How many data products should I create?

There’s no one-size-fits-all answer. Better questions to ask:

• What are my organization’s key data needs?

• What data products meet those needs?

• Are the existing products delivering value?

Organizations vary: some have a few large data products, others have many small ones. If you’re managing over 1,000, regularly assess whether they are being actively used and maintained.

How many assets should be in a data product?

A data product should include assets that are:

• Related in purpose

• Maintained by the same team

• Built and delivered together

Avoid overloading a data product: too many assets can make it harder to manage and update. Instead, favor smaller, focused units with clear contracts, similar to a micro-services approach in software.

What kinds of data assets can be included in a data product?

Any reusable data asset, including:

• Database tables or sets of related tables

• Structured spreadsheets

• APIs

• Streaming data (for example, Kafka topics)

• File-based datasets (CSV, Parquet)

• BI assets

The form of storage doesn’t matter. What matters is consistent, documented access and a clear structure that supports reuse.