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[SERIES] Building a Marketplace for data mesh Part 1: Facilitating data product consumption through metadata

May 28, 2024
May 28, 2024
28 May 2024

Over the past decade, data catalogs have emerged as important pillars in the landscape of data-driven initiatives. However, many vendors on the market fall short of expectations with lengthy timelines, complex and costly projects, bureaucratic data governance models, poor user adoption rates, and low-value creation. This discrepancy extends beyond metadata management projects, reflecting a broader failure at the data management level.

Given these shortcomings, a new concept is gaining popularity, the internal marketplace, or what we call the Enterprise Data Marketplace (EDM) at Zeenea.

In this series of articles, get an excerpt from our Practical Guide to Data Mesh where we explain the value of internal data marketplaces for data product production and consumption, how an EDM supports data mesh exploitation on a larger scale, and how they go hand-in-hand with a data catalog solution:

  1. Facilitating data product consumption through metadata
  2. Setting up an enterprise-level marketplace
  3. Feeding the marketplace via domain-specific data catalogs



Before diving into the internal marketplace, let’s quickly go back to the notion of a data product, which we believe is the cornerstone of the data mesh and the first step in transforming data management.


Sharing and exploiting data products through metadata


As mentioned in our previous series on data mesh, a data product is a governed, reusable, scalable dataset offering data quality and compliance guarantees to various regulations and internal rules. Note that this definition is quite restrictive – it excludes other types of products such as machine learning algorithms, models, or dashboards.

While these artifacts should be managed as products, they are not data products – They are other types of products, which could be very generally termed “Analytics Products”, of which data products are one subset.

In practice, an operational data product consists of two things:

  • Data (1)1. Data - materialized on a centralized or decentralized data platform, guaranteeing data addressing, interoperability, and access security.
  • Metadata (1)2. Metadata - providing all the necessary information for sharing and using the data.

Metadata ensures consumers have all the information they need to use the product.

It typically covers the following aspects:


Schema – providing the technical structure of the data product, data classification, samples, and their origin (lineage).


Governance – identifying the product owner(s), its successive versions, its possible deprecation, etc.


Semantics – providing a clear definition of the exposed information, ideally linked to the organization’s business glossary and comprehensive documentation of the data product.


Contract – defining quality guarantees, consumption modalities (protocols and security), potential usage restrictions, redistribution rules, etc.

In the data mesh logic, these metadata are managed by the product team and are deployed according to the same lifecycle as data and pipelines. There remains a fundamental question: where can metadata be deployed?

Using a data marketplace to deploy metadata


Most organizations already have a metadata management system, usually in the form of a Data Catalog.

But data catalogs, in their current form, have major drawbacks:


Dont Support Data Product

They don’t always support the notion of a data product – it must be more or less emulated with other concepts.

Complex To Use

They are complex to use – designed to catalog a large number of assets with sometimes very fine granularity, they often suffer from a lack of adoption beyond centralized data management teams.

Rigid Organization

They mostly impose a rigid and unique organization of data, decided and designed centrally – which fails to reflect the variety of different domains or the organization’s evolution as the data mesh expands.

Limited Search Capacities

Their search capabilities are often limited, particularly for exploratory aspects – it’s often necessary to know what you’re looking for to be able to find it.

Lacks Simplicity
The experience they offer sometimes lacks the simplicity users aspire to – search with a few keywords, identify the appropriate data product, and then trigger the operational process of an access request or data delivery.

The internal marketplace, or Enterprise Data Marketplace (EDM) is therefore a new concept gaining popularity in the data mesh circle. Like a general-purpose marketplace, the EDM aims to provide a shopping experience for data consumers. It is thus an essential component to ensure the exploitation of the data mesh on a larger scale – it allows data consumers to have a simple and effective system to search for and access data products from various domains.


In our next article, learn the different ways to set up an internal data marketplace, and how it is essential for data mesh exploitation.

The Practical Guide to Data Mesh: Setting up and Supervising an enterprise-wide Data Mesh


Written by Guillaume Bodet, co-founder & CPTO at Zeenea, our guide was designed to arm you with practical strategies for implementing data mesh in your organization, helping you:

✅ Start your data mesh journey with a focused pilot project
✅ Discover efficient methods for scaling up your data mesh
✅ Acknowledge the pivotal role an internal marketplace plays in facilitating the effective consumption of data products
✅ Learn how Zeenea emerges as a robust supervision system, orchestrating an enterprise-wide data mesh

Signature Data Mesh En

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At Zeenea, we work hard to create a data fluent world by providing our customers with the tools and services that allow enterprises to be data driven.

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Chez Zeenea, notre objectif est de créer un monde “data fluent” en proposant à nos clients une plateforme et des services permettant aux entreprises de devenir data-driven.

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Das Ziel von Zeenea ist es, unsere Kunden "data-fluent" zu machen, indem wir ihnen eine Plattform und Dienstleistungen bieten, die ihnen datengetriebenes Arbeiten ermöglichen.

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