K2VIEW ebook

Data Mesh Vendors and Buyers Guide

Data mesh is the go-to architecture for distributed data management platforms and systems.
Read this before selecting a data mesh vendor for your business.

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The Most Innovative Data Architecture

Data mesh is an innovative data architecture that enables organizations to manage enterprise data and maximize its value at scale. Because the data mesh market is still evolving, evaluating data mesh vendors, and understanding their differences, is a challenge. Keep reading to understand how top data mesh vendors stack up.

Chapter 01

Evaluating Data Mesh Vendors

The concept of data mesh first appeared in Zhamak Dehghani’s landmark 2019 article. Since then, data mesh has rapidly transformed from a proposal into a functional technology that is revolutionizing how businesses use data, work together, and drive innovation.

The data mesh market is still in its infancy, but already, there are several data mesh vendors with game-changing offerings.

This article is intended for CIOs, CDOs, data architects, data engineers, data scientists, and other stakeholders interested in discovering how a data mesh architecture can improve their business.

Chapter 02

What is Data Mesh?

Data mesh is an emerging data architecture for managing and delivering enterprise data. It is based on the belief that business domains should be able to define, access, and control their own data products, without relying solely on centralized data teams.

The data mesh concept is based on the following 4 principles:

  1. Data ownership and architecture is domain-oriented and decentralized

  2. Data is delivered as a product

  3. Data infrastructure is a self-serve data platform

  4. Data governance is federated

In a decentralized, domain-oriented data mesh architecture, datasets are integrated, processed, and managed by data products, which deliver clean and unified data to authorized data consumers on-demand.

Although data governance is distributed (each business domain governs its own data products), centralized data governance tools are still necessary, with security policies, and compliance standards, fully enforced.

Chapter 03

Why Data Mesh?

Business outcomes are inextricably tied to how organizations manage and utilize data. Here’s how data mesh vendors can help you improves business, data management, and organizational performance.

Business drivers for data mesh

  • Enables faster data delivery
    Data mesh speeds up and democratizes data delivery with a self-service approach to data access. At the same time, the mesh conceals the underlying data complexities from users.

  • Supports data-driven insights
    Since domain teams define and manage their own data products, they are free to analyze and operationalize them according to their needs. This allows domains to accelerate decision making and extract more value from their data.

  • Increases agility and scalability
    Data mesh decentralizes data management and shifts data ownership over to the business domains. As a result, it reduces reliance on centralized IT teams, fosters domain autonomy, and positions organizations to utilize more data.

Data management drivers for data mesh

  • Improves data quality and governance
    Domain-based data operations, combined with automated data governance enforcement, promote easier access to fresh, high-quality data.

  • Ensures compliance
    Improved visibility, quality, and governance models enabled across the data mesh make it easier to respond to emerging regulations. Plus, automated rules related to data anonymity and access controls make it easier to maximize the value of data while remaining compliant with data privacy regulations.

Organizational drivers for data mesh

  • Create cross-functional domain teams
    Unlike centralized data architectures, in which highly-skilled data teams are responsible for creating and maintaining data pipelines, data mesh gives domain experts control over data. This leads to greater IT-business cooperation, enhanced domain knowledge, and greater business agility.

  • Foster data-driven cultures of innovation
    As the custodians and controllers of their own data products, business domain teams have the autonomy to experiment with the data however they like. This experimentation, combined with the motivation to ensure the quality of their own data products, increases analytical capabilities, innovation, and outcomes.

Chapter 04

Capabilities to Look for in Data Mesh Vendors

When evaluating data mesh vendors, make sure they offer all of the following capabilities:

  • Support for the 4 data mesh principles
    This is the starting point, including decentralized domain ownership, treatment of data as a product, self-service platform, and federated data governance.

  • Data cataloging
    The solution must identify, classify, and build an inventory of data assets, and visually display information supply chains.

  • Data engineering
    The vendor should enable the quick assembly of scalable and reliable data pipelines that support analytical and operational workloads – with common data preparation flows, productized for reuse by the domains.

  • Data governance
    The data mesh must distribute certain quality assurance, privacy compliance, and data availability policies and enforcement to the business domains, while maintaining centralized governance over company-wide data policies.

  • Data preparation and orchestration
    The data mesh model ought to enable quick orchestration of source-to-target data flows, including data cleansing, transformation, masking, validation, and enrichment.

  • Data integration and delivery
    The vendor should be able to access data from any source, and pipeline it to any target, in any method: ETL (bulk), messaging, CDC, virtualization, and APIs

  • Data persistence layer
    The vendor should selectively store and/or cache data in the data center, or within individual domains, to enhance data access performance.


Data mesh must-haves

“Data as a product (what we produce) and self-service (how we work) are needed to move data mesh from academic to pragmatic, and realize return on data.”

      Michele Goetz
      VP, Principal Analyst, Forrester

Chapter 05

Comparing Data Mesh Vendors

Vendor Strengths Concerns


  • Self-service Data Product Platform, including all data mesh core capabilities

  • Data is uniquely managed in patented Micro-Databases, delivering real-time performance, at enterprise scale

  • Supports high-volume workloads that require real-time data integration / pipelining

  • Enables both operational and analytical use cases

  • Rapid deployment (i.e., in weeks) and seamless adaption, in support of agility

  • Minimal total cost of ownership (TCO)

  • Works primarily with large enterprises, with relatively few mid-sized customers

  • Deployment focus on the telco, healthcare, and financial services industries to date

  • Few system integration partners outside of Europe and the US


  • Good data integration spanning, on-prem, multi-cloud, and hybrid environments

  • Broad data engineering capabilities

  • Range of connectors to a wide variety of data sources

  • Not applicable for high-volume operational use cases; better suited for analytics

  • Support required for complex data orchestration and operational data pipelining

  • Limited data productization capabilities

  • Java expertise required


  • Data aggregation from multiple sources

  • SQL query engine optimized for data lakes

  • Reduced need for ETL

  • Concurrent scaling

  • Keys to unlocking “hard-to-get” data

  • Centralized security framework
  • Essentially a query engine, as opposed to a data management platform

  • Dependence on Trino (formerly PrestoSQL) query technology

  • Focus on analytics, with no solution for real-time operations


  • Good data integration supporting analytics in multiple architectures

  • Ability to scale to support complex data integration scenarios

  • Uses AI and ML for additional data integration and data quality support

  • Complex and costly deployment and adoption

  • Data productization support limited

  • Inadequate data pipelining capabilities, making it less suitable for real-time operational use cases

  • Array disjointed tools, acquired over time, and not yet integrated into a single platform

  • Difficulties mapping and debugging workflows

  • Transformations are table-based, requiring more memory and CPU


  • Good data virtualization capabilities

  • Data catalog is the entry point for enforcing governance and security

  • Partnership channels

  • Optimizes analytical use cases

  • Managing and operating in a data mesh is complex

  • Difficulty with federated data

  • Not applicable for high-scale operational use cases

  • Effort required to enable distributed query performance

  • Limitations supporting diverse, large-scale data sources (where caching is necessary)


Chapter 06

Why K2View?

data mesh-1

Of all data mesh vendors, the K2View Data Product Platform is outstanding because it:

  • Can be deployed on premises, in the cloud as an iPaaS (Integration Platform as a Service), or across hybrid environments
  • Integrates all data, from all sources, into any number of data products, for secure distribution among the business domains.
  • Aggregates business entity data into a secure, high-performance Micro-Databases, ensuring business entity data is always fresh, and accessible to authorized users.
  • Provides centralized data governance, cataloging, and modeling, while providing autonomy and self-service access to data by business domains.
  • Supports both analytical and operational workloads.
  • Unites all company domains via a federated alliance by providing a single, trusted, and holistic view of all business entity data.
  • Can be deployed in weeks and adapt to change on the fly.

As the first Data Product Platform in the market, K2View is uniquely capable of supporting core operational workloads in a data mesh, including data masking, data anonymization, synthetic data generation, data tokenization, customer 360, legacy application modernization, data migration and more.

Learn from the best

Teresa Tung

Cloud First Chief Technologist, Accenture

Watch Accenture Cloud First Chief Technologist, Teresa Tung, holder of 220 patents, explain the concept of operational data products in a data mesh.

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