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Top 5 Data Mesh Tools: Discover the Right One For You

Explore the best right data mesh tools and find the right one for your organization's needs to unlock the full potential of data mesh implementation.

From disconnected data producers to impatient data consumers and backlogged data teams, many companies struggle with their data management. 

A data mesh is a modern, decentralized approach to collecting and managing enterprise data, where domain experts who work closest to each subset of data are responsible for maintaining specialized data products. 

But data meshes are just a data management methodology – not a product you can buy. So the right technology is needed to support the mesh. 

Here we'll break down the pros and cons of the top data mesh tools, and help you decide which is best for your organization's needs.

What is a data mesh tool?

Thoughtworks' Zhamak Dehghani coined the term “data mesh” in 2019, and it has become increasingly popular since, marking a major paradigm shift in enterprise data architecture.

Data mesh tools provide the infrastructure and functionality to support distributed (or federated) data management, allowing domain data owners to share access to data products with data consumers, build self-service interfaces, and enforce enterprise-wide standards for data governance and quality.  

Essentially, a data mesh decentralizes data ownership, so that business domains like marketing, sales, and finance can access and control their own data. It's basically a self-serve data platform that ensures federated computational governance.

Data mesh tools help “the right data to reach the right people,” so your teams can uncover insights from enterprise data across business use cases. 

Top 5 data mesh tools

1. is an end-to-end platform with a data catalog built on a knowledge graph architecture. Domain teams can have their own metadata model, delivering distributed data ownership while enabling agile data governance across distributed domains. Essentially, data teams can build a “storefront” to share data products, analysis, and use case recipes, all with clearly-defined owners and SLAs. 

Key features

Here are some of the stand-out features of 

  • Knowledge graph architecture: This architecture gives each domain autonomy over its metadata model, data structuring, and usage.

  • Federated query: Avoid data silos by accessing federated queries that offer a holistic view of enterprise-wide data sources.

  • Automations: Balance centralized and decentralized data governance, document interoperability standards, and enforce global policies with’s Eureka Bot. 

  • Specifically architected for data mesh: The best access on the market, allowing users to search for data even without having direct access to it.


In terms of what users are saying in reviews, one customer said the platform is “user-friendly, easy to access, and very helpful for working on projects on data analysis.” Another user said they had difficulty ordering search results chronologically. recently helped White Lion Interactive to have a decentralized data catalog. The agency said, " has given us the ability to have greater awareness of the universe of data that’s out there and available to us and our clients. The other thing it gives us is there’s direct access to data analysis and visualization." pros & cons


  • Knowledge graph enables decentralized metadata management

  • Federated governance capabilities help find the right mix between centralized and decentralized control 

  • Uncover insights faster with a user-friendly interface, simple access controls, and a powerful search engine that scans data, discussions, and analysis across domains

  • Manage data access with role-based access controls

  • Supports self-serve data enablement through collaborative request access workflows


  • Initial setup and data integrations can be time-consuming

  • Can be costly, especially for enterprises requiring a larger data mesh

Download our data governance report to learn more about how to establish a framework for governing the data mesh. 

Pricing’s pricing is simple and customizable. Plans range from Essentials, to Standard, to Enterprise, to Enterprise+. Users can contact the team to customize a package specifically for their needs. 

Book a demo today to explore for data mesh. 

2. Informatica

Informatica’s AI-powered Intelligent Data Management Cloud (IDMC) supports a data mesh implementation. Informatica was created to aid the data management lifecycle. 

Key features

Here are some of the stand-out features of Informatica: 

  • Data quality: Provides tools for data observability and ensuring high-quality data standards

  • AI-powered cloud data management: Uses artificial intelligence for enhanced data management and governance in the cloud

  • Cloud data governance and catalog: Provides teams with the ability to locate, comprehend, and use governed data efficiently

  • Modern data architecture expertise: Provides resources and tools to become adept in modern data architectures quickly

  • Cloud data marketplace: Facilitates data democratization with an easy-to-use platform for accessing and sharing trusted data


Informatica helped CVS Health automate tedious tasks with its advanced data analysis features, which make it easier to search through data. A CVS executive advisor noted, “In the past, it took 6 months to generate files that are used for client reporting that can now be done in 2-3 days—a 95% reduction in manual effort to analyze data—allowing us to expand the scope of our project effort for critical clinical operations.”

On customer review platform G2, one user said, "We use this product every day and when any issues occur the team replies promptly." On a less positive note, one user said, "There are still operational bugs present in the tool which sometimes makes life difficult for the developers." 

Informatica pros & cons


  • Comprehensive data management for integration, governance, quality, and cataloging

  • Simplifies ingestion and integration of data from multiple sources and databases

  • Scalable, cloud-native architecture well-suited for large-scale data mesh implementations 

  • AI-driven automation increases efficiency across the data mesh lifecycle 

  • Robust data governance and lineage for maintaining control in a decentralized environment


  • High costs, especially for advanced features and larger deployments 

  • Steep learning curve for new users. Can be complex, even for experienced data analysts

  • Can lead to vendor lock-in due to over-reliance on Informatica's ecosystem

  • Limited support for some data mesh use cases and industries


Customers pay for Informatica based on a consumption-based and volume-based pricing model, so that users are not paying for services they don't use. Users can get a custom quote based on their individual subscription plan.

3. Databricks

Like the other tools listed here, Databricks is a data intelligence platform that supports data mesh architecture and federated data governance systems. Databricks users can also build and implement custom AI models.

Databricks is a relatively new platform that is still developing its full capabilities. Databricks’ customers particularly like its automation and AI capabilities. Their Dolly 2.0 is regarded as a strong open-source LLM option.

Key features

Here are some of the stand-out features of Databricks: 

  • Centralized governance: A central catalog to simplify data discovery and access control

  • ML modeling: The ML model registry allows domains to publish, discover, and manage ML models as actual products

  • Cluster policies: Enforce centralized computing rules across different data domains and workflows within an organization’s data mesh

  • Notebooks: For facilitating collaborative work, analysis, and documentation of data products

  • Delta Lake: This storage layer enables approved users to define secure data products as Delta tables using ACID transactions


Databricks customers on the user review platform G2 noted that they particularly liked the "Clear architecture that follows a pattern makes the tool easier to use. Customer service is also fantastic; they helped me with every problem I ran across." On a less positive note, one user mentioned that "Sometimes there are unplanned downtime for the platform which irritates us. Some documentation pages lacks examples. The AI assistant right now only able to solve/give sql responses only for simple sql asks. In cases of complex sql and sql failures, the `Diagnose error` is not relevant." 

Databricks pros & cons


  • Open, unified data platform supporting batch, streaming, ML, and BI workloads

  • Reliable, governed data sharing and updates throughout organizational domains

    Hybrid cloud capabilities with strong security, governance, and access control

  • Simple and easy integration with popular data tools and programming languages


  • Primary cloud data warehouse integrations are limited to AWS and Azure

  • Can be especially costly for larger organizations with proprietary components

  • Concepts around data mesh implementation patterns can be difficult to understand, with frequent updates and changes in documentation

  • Can be time-consuming to learn, especially given the periodic updates to the platform’s UI


Databricks pricing offers a "pay-as-you-go approach with no up-front costs" so that users reportedly only pay for the products they use, at a per-second granularity. They have a general framework for pricing, depending on the features you use. For example, their "Workflows" start at $0.15 / DBU, while their Delta Live Tables start at $0.20 / DBU. 

4. Denodo

Similar to the first few tools listed here, Denodo allows users to integrate and deliver data in one comprehensive platform. The platform aims to reduce compliance and business risks through automatic governance systems. Users who implement Denodo are usually looking to mitigate the risks of silos and encourage transparent collaboration. 

Key features

The following features make Denodo a standout product on the market: 

  • A virtualized and integrated data layer: that works across distributed sources 

  • Dynamic data catalog: Facilitates self-service data access and discovery across an organization’s data mesh

  • Centralized security: Simplifies enforcement of authentication, authorization, and auditing policies

  • Automated DevOps: Streamlines deployment of new data services automatically 


Denodo’s customer reviews note some of the features and drawbacks of the tool. One user noted, "What I highlight the most is its ability to bring together dispersed and heterogeneous data sources into a unified and coherent view, without the need to physically move the data." 

Another user said, "Some users might find the learning curve steep, especially when dealing with complex data integration scenarios." A few other people mentioned that the user interface could be easier to navigate. 

Denodo pros & cons


  • Virtually integrates data to abstract complexities existing in an organization’s data mesh

  • Decouples data consumers from data sources, allowing domain ownership autonomy over data

  • Provides centralized security, governance, and access control across an entire data mesh

  • Reduces data movement costs compared to traditional data integration approaches


  • Performance is often degraded for complex workloads compared to physical data replication

  • Upfront implementation can be difficult and time-consuming

  • Pricing model is based on data volumes, meaning it can become quite costly at larger scales


Denodo pricing isn't listed on the website, but pricing tiers are as follows: 

  • Denodo Professional: Supports 5 data sources, for small single use-case projects within individual departments

  • Denodo Standard: for multiple use cases within individual departments

  • Denodo Enterprise: Enterprise-wide deployment for multiple use cases and groups and large data volumes 

  • Denodo Enterprise Plus: Comprehensive collaboration and automation, plus advanced security for enterprise-wide deployments

5. Snowflake

Snowflake is a preeminent tool that makes it easy to analyze and share data throughout an organization. Snowflake can help create an interconnected data mesh that eliminates data silos. Like, Snowflake is cost-efficient as you scale. As data warehouses, workloads, and active users grow, Snowflake uses cloud elasticity to scale data storage without impacting platform performance.

Key features

The following features make Snowflake a favored product when it comes to data mesh: 

  • Centralized, cloud-based platform: Provides data lakes, data warehousing, and data sharing. 

  • Secure real-time data: Shared across accounts, regions, and clouds within the Snowflake ecosystem 

  • AI capabilities: Leverage open-source LLMs for data analytics through Snowflake’s Cortex


One user on the customer review platform G2 noted, "Snowflake stands out as a data powerhouse, particularly for professionals delving into analytics and reporting across multiple sources. Its stand out feature lied in its unwavering reliability, thanks to a well-designed architecture that seamlessly handles heavy data workloads. What sets Snowflake apart it its cloud-native versatility, effortless integrating with major providers." 

Chief complaints about Snowflake on the same platform mostly centered around the user interface being overwhelming, confusion around the documentation, and difficulty with soaring costs. One critic said, "You need to be proactive in managing cost. As an owner of a Snowflake deployment, I appreciate the tools given to manage cost, but would like even more options." 

Snowflake pros & cons


  • An industry-dominant solution that is well-known, with many integrations

  • Provides a single, integrated platform that tracks the lifecycle of your data and stores it in the cloud

  • Allows for easy scalability using virtual data warehouses

  • Combines centralized and decentralized data governance for better data safety and control

  • Supports hybrid and multi-cloud architectures

  • Simplifies data sharing across organizational boundaries


  • Starting fees can be high for smaller or newer companies

  • Initial integration and platform setup can be complex

  • Dependence on Snowflake's proprietary platform can lead to vendor lock-in


Snowflake charges a monthly fee for data stored in the platform. Charges are calculated using the average amount of storage used per month, after compression, for data ingested into Snowflake.

In addition, for AWS, Azure, and Google Cloud Platform, Snowflake lists its pricing as follows:

  • The Standard Edition: The introductory offering providing access to core platform functionality: $2 USD/per credit

  • The Enterprise Edition: For companies with large-scale data initiatives looking for more granular enterprise controls: $3 USD/per credit

  • The Business Critical Edition: Specialized functionality for highly regulated industries, especially those with sensitive data: $4 USD/per credit

  • Virtual Private Snowflake: Includes all the features of Business Critical Edition, but in a completely separate Snowflake environment, isolated from all other Snowflake accounts

Benefits of a data mesh tool

Unlike data lakes, which are used to consolidate disparate enterprise data in a single, central location, data meshes distribute ownership and management of specific data pipelines to the functional experts who know that data best. This approach helps make data more understandable, accessible, and useful to non-expert data consumers across business lines.  With the right data mesh tool, organizations can gain:

Greater autonomy and flexibility 

When domain teams manage their own data pipelines, they have greater autonomy and flexibility to build data products that fit their specific needs and requirements, and organization-wide data consumers are empowered to self-serve. Top-down, centralized governance can create bottlenecks around data access and use that slow down insight discovery. 

Increased data experimentation and innovation

The data-as-a-product framework treats data as an asset that can be improved and iterated upon. With the autonomy to manage their own data, domains are encouraged to experiment, develop custom data products, and leverage data in bespoke ways to meet analytics and operational needs. This “bottom-up” approach helps teams make concrete improvements to practical, day-to-day processes.


When your organization reaches a certain scale, producing and managing a large amount of data, a single team — or gulp person — holding all the keys can become overwhelmed by your data’s volume or complexity. When domain teams own domain data pipelines, they are more nimble and empowered to create high-quality, purpose-built data products for their functional areas. 

Improved data trust

In a data mesh architecture, data is viewed as a product each domain publishes, which other functional teams are internal customers of. When functional experts are accountable for the accuracy and quality of data products, every team in your organization can trust that each dataset rests in the hands of the domain team that understands that data best. 

Reduced burdens on data teams

Data meshes alleviate the burden on central data teams to meet the needs of every data consumer in the enterprise through one pipeline. With data ownership distributed, they’re freed up to focus on regulatory compliance, enforcement of enterprise-wide policies and domain-agnostic standards, and ensuring interoperability between the various domains.

Common data mesh use cases

What common business questions does a data mesh program solve? Here are some of the most common use cases. 

Data analytics

The data mesh enables domain teams to curate domain data, create analytical data products to match their business needs, and enable self-service analytics across functional teams. 

Customer support

Domain teams responsible for customer support can access and control data related to customer interactions, preferences, and behavior, identify new insights, and offer more personalized customer experiences.

How to select the right data mesh tool for your organization

Treating data as a product means caring deeply about whether end users are getting value from the data. The data mesh tool you choose should be the one that maps back to your business needs. It should allow both technical and non-technical data consumers to realize value from the enterprise data.

 Identify goals and critical use cases before selecting a specific data mesh tool. Then, understand how data mesh principles map to the tool's various features and functionalities to help you identify what elements you’ll want in your mesh implementation. 

To learn more about how can support your data mesh implementation, schedule a demo today.

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