Data mesh vs data fabric: What’s the difference?

It goes without saying: data management isn’t getting any easier for businesses. For a start, there’s more data than ever. But that’s only half the story. 

The data complexity challenge

The biggest challenge organizations face is that modern data is diverse and increasingly complex. Previously, businesses had few interconnected systems and stored most of their data on simple relational databases and on-premises data infrastructure. But now? Data is siloed, disconnected and scattered across dozens of platforms, devices and apps.

Artificial Intelligence (AI) has made this problem even more complex, meaning businesses need to come up with a modern architecture solution to ensure their data is aggregated and reliable, ready for compliance and data analytics. 

Data mesh and data fabric are two distinct but similar architectural approaches to achieving this reliability. Let’s talk about how they work and how they differ. 

What is a data mesh? 

A data mesh is a decentralized, domain-oriented design approach to data architecture. It enables different owners — or departments — to handle their own data pipelines while still maintaining interoperability.

Originally coined by Zhamak Delghani in 2019, data mesh is built on four core principles:

• Domain ownership: Data responsibility is shifted to individual business domains and teams, who manage and maintain the data they generate.
• Data as a product: Each data asset is a product. Everything is designed based on clear quality standards to serve end-users.
• Self-serve platform: The disparate data products are unified on a flexible, self-service platform, giving teams access to relevant data as needed. 
• Federated computation governance: Shared standards for data governance ensure teams remain compliant and secure despite the decentralized architecture. 

In essence, the goal of data mesh architectures is to break down centralized, monolithic datasets in favor of a microservice mesh architecture, allowing different departments and teams to manage their own data and control access as they see fit. 

This brings the all-important data consumers closer to the data source and ensures the right teams have access to the right data at the right time. All of this helps to speed up processes and enhance agility for real-time decision-making. 

A classic example where this approach is most effective is in the finance industry. Take a brand like Saxo Bank, for instance. The investment bank used a data mesh to ensure data assets were easily discoverable via a simple search bar. This meant reps could instantly have reliable information on hand to provide to customers in less time, helping clients make more confident investment decisions. 

The key benefits and drawbacks of a data mesh

Is a data mesh right for you? Here’s a table you can use to find out.

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What is a data fabric? 

A data fabric is a centralized data architecture that creates a unified, virtual operational layer of data assets. It uses AI and ML to transform siloed unstructured, and structured data, bringing it together to provide a real-time view of all business data, regardless of its initial source. 

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This allows a business to apply its shared data discovery, management, governance and access protocols universally across the entire data platform, in turn creating a single source of truth that upholds data security and quality while still facilitating self-service data accessibility. 

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Critical principles of this architecture include:

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• Unification: A data fabric creates a single point of access for all company data regardless of its source, which helps to alleviate data silos.
• Self-service: As with data meshes, a data fabric provides a self–service data catalog to help route relevant data to departments that need it. 
• Security and compliance: Data fabric also commonly features data encryption, data lineage tracking, identity access management, and role-based access control to support security and compliance.

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Many data fabric architectures also include AI and ML solutions for data analytics and data visualization, which lets businesses immediately derive insights from the unified data assets they possess. 

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For instance, motorcycle manufacturer Ducati used a data fabric model to aggregate an enormous amount of performance data from motorcycles around the world. In turn, they could use this wealth of information to optimize their own motorbikes and provide a better end service to customers. 

The key benefits and drawbacks of a data fabric

Here are some advantages and disadvantages to help you decide whether this approach is the right decision. 

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Data mesh vs data fabric: the key differences

Data mesh and data fabric are both cut from the same cloth. Both aim to solve data management challenges to provide data access, security, scalability, and governance amidst complex data ecosystems. But the way they approach this problem differs. Here’s a table to recap the important details. 

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How do you decide which approach is right for you? Here’s a brief overview. 

Data mesh concepts could be the right choice if you:

• Have a large domain network and want teams to handle their own data management. 
• Have diverse and complex datasets from various sources that are difficult to unify. 
• Want to scale data usage across departments without introducing bottlenecks. 
• Have mature teams that understand how to implement their own data governance best practices. 

In contrast, a data fabric may be the right option if you:

• Have a mass of siloed data that you want to unify.
• Want to benefit from data governance, integration, access automation through AI, and enhanced data integrity across your organization. 
• Aim to simplify data access without taking a granular approach. 
• Need to achieve universal compliance at scale with various data types. 

As you might expect, the decision of which architecture to choose can be difficult. Let’s dive deeper into how to determine the right approach for your organization. 

How to choose the right approach

The best way forward will depend on your structure and culture, as well as your current data landscape and security posture. Let’s dive in. 

1. Take stock of business structure

The first and perhaps most obvious element to consider is your company’s size and structure. Do you operate across dozens of different decentralized business operations, like marketing, sales, HR, and finance? Or are you a single unified team? 

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While there are other factors at play, data meshes are usually most appropriate for large, diverse teams that operate independently, such as a global retailer with dozens of departments. Data fabric, on the other hand, is often a good choice for smaller, more close-knit teams that are all operating on the same wavelength. 

2. How’s your company culture?

In the same vein, take a look at your company culture. 

Is your business collaborative by nature, founded on data and motivated to take responsibility? A data mesh approach can give your teams the freedom they require to take ownership of their own data and drive change for their department. 

Do your teams lack the data literacy to manage data independently? A structured, top-down approach like a data fabric will help you leverage data while being easier to implement for teams that lack knowledge of data management. 

3. Understand your data landscape

Another issue to consider is the structure and complexity of your data. Namely, how many diverse systems and environments is your data spread across? 

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A data fabric is ideal for complex, distributed data sources, such as when you have unstructured and structured data spread across a mixture of cloud, hybrid solutions, data lakes, and on-premises systems that need to be unified.

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When it comes to data meshes, they could be a viable choice if you have complex data spread across a variety of domains, where the data doesn’t necessarily need to be aggregated to be helpful. 

4. Know your security

It’s important to consider your organization’s requirements for data security and compliance. Do strict requirements necessitate that you keep overarching control over data, such as in the healthcare industry? In that case, data fabric can provide you with centralized control to maintain strong governance over your assets. 

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A data mesh keeps security at the forefront while allowing your teams the freedom to make their own decisions. But remember you’re also putting more reliance on your business domains to meet particularly strict compliance frameworks. If ensuring consistent compliance with particularly stringent regulations like the GDPR, a data fabric could be the better choice. 

5. Consider combining both options

What if you didn’t have to choose? Why not both?

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Combining both options can bring the best of both worlds. Data fabric is essential for unifying and democratizing data — a data mesh is optimal for self-service data that promotes agile decision-making across departments. 

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One possible way this approach can be implemented is by using data fabric as a unified access layer while using data mesh to govern domain-specific data products. For instance, a financial services business could use this to unify legacy and cloud data while still using a data mesh solution at the departmental level to support teams in managing financial datasets.

How does each approach impact data governance and security?

Both options have strong approaches to data governance and security. Still, they both go about it in different ways. 

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Data meshes use “federated governance.” This means domain teams need to manage their own security and compliance within the shared framework. As you can imagine, this kind of approach requires clear, standardized policies to ensure all domains are taking accountability for their data and checking for compliance regularly. 

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Data fabric, on the flip side, centralizes governance with automated AI security and policies driven by activated metadata rather than trust. This can require significant infrastructure investments but removes the need for teams to manage security independently. Data security and governance are instead enforced universally across the entire company.

How does each approach impact handle data quality?

Data integrity and quality are paramount to both approaches, but they both achieve it differently.  

With a data fabric solution, the quality of data is managed centrally through automated tools (often powered by AI and ML). A unified data access layer lends itself naturally to stringent data quality standards — making it simpler to manage large volumes of data and diverse teams in real time when compliance is a constant concern. 

Data meshes hand over data quality responsibility to domain teams that must ensure data is accurate and usable. This can introduce problems if certain teams lack the data literacy to maintain quality standards. 

But equally, provided domains have access to the right tools, it can also help to improve quality standards by ensuring each team can focus on their specific domain’s specific data needs and apply contextual quality measures. 

RecordPoint can help with either approach

In reality, a data mesh and data fabric are two sides of the same coin. Both aim to improve data management but differ in how they approach the problem. So why not have both? 

One of the core concerns with data fabric vs data mesh is deciding which to implement. Another is knowing where to begin when you’re ready to get started. RecordPoint can help with both of those concerns. 

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RecordPoint is the best way to combine a fabric and data mesh approach within your organization. We can help you maintain centralized governance and compliance. 

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With AI-driven automation and seamless integration across 900+ data sources, RecordPoint will make data management effortless for your operation. Book a demo today to learn more. 

FAQs

What is a business domain? 

A business domain within this context is a specific area, team, or department of a business that has unique data needs. For instance, a sales department’s information for lead nurturing compared with a finance department that handles transactional data as well as personally identifiable information (PII). 

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A data mesh requires each of these domains to look after its own data to keep it safe, accessible, and secure. 

What is the difference between a data fabric and a data mesh?

A data mesh is decentralized, which means it’s split among multiple business domains. This is great for agility and is beneficial for departments that want autonomy over their own data. 

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On the other hand, a data fabric unifies disconnected and siloed data into one unified layer using AI and ML. While this doesn’t allow for the same level of flexibility, it generally offers better data consistency and reliability, meaning it is easier to handle compliance. 

Why implement a data mesh? 

A data mesh is a brilliant choice if you have large teams that can work independently and diverse datasets that are difficult to unify. If you want to scale business domains and provide individual departments with more autonomy and flexibility, this is an excellent option. 

Why implement a data fabric? 

This approach is optimal if you have a mass of disparate, siloed data across a variety of sources. Because it creates unified access later, it allows for centralized governance and quality control. This could be the best option if you need standardized, governed data for a specific compliance requirement or if you want to unify data to create a single source of truth.

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