Data Mesh vs. Data Fabric: Which solution is better for your company? Malte Mrotzek December 5, 2023

Data Mesh vs. Data Fabric: Which solution is better for your company?

Data is the lifeblood of any modern organization, but managing it effectively can be a real challenge. Due to the increasing volume, variety and velocity of data, traditional data architectures often struggle to cope with the demands of data consumers and producers. How can you ensure that your data is accessible, reliable and secure, while enabling flexibility, scalability and innovation?

This article presents two new data architectures designed to address these challenges: Data Fabric and Data Mesh. Both aim to address the challenge of scaling data infrastructure, but follow different principles, especially in terms of data management. We will explain what they are, how they differ and what to consider when choosing one or the other.

 

What is Data Fabric?

Gartner defines Data Fabric as a design concept that represents an integrated layer of data and connectivity processes. A data fabric supports the development, delivery and use of integrated and reusable data in all environments through continuous analysis of existing, discoverable and referenced metadata assets.

Data Fabric supports on-site data access and consolidation through human and machine capabilities. It continuously recognizes and connects data from different applications to identify unique, business-relevant relationships between available data points. Through rapid access and understanding, insights provide more value than traditional data management practices and enable improved decision making.

 

You can think of Data Fabric as a layer that covers your entire data ecosystem like a blanket. It brings together different data and makes it available for different use cases, e.g. for analytics, reporting, machine learning and operational applications.

 
What is Data Mesh?

Data Mesh is an organizational and architectural strategy that decentralizes data ownership and management, transforming the model from a centralized data monolith to a decentralized model. The main goal is to give each business unit or entity autonomy and ownership of its data to increase the overall efficiency of the organization.

In a data mesh architecture, data is viewed as a distributed, domain-oriented product and each business domain or department takes responsibility for its data. In this approach, data products are created, managed and used in different areas, promoting a distributed governance and quality system. This allows each area to manage its data according to specific requirements and reduce bottlenecks in centralized data processing.

 

The data mesh can be thought of as a network of interconnected data nodes, each of which is specialized and optimized for a specific area, like a net (or mesh). Each data node is managed by a team that is familiar with the data and its use cases. Each data node makes its data available via APIs or data stores that adhere to common standards and protocols.

 
Data Fabric vs. Data Mesh: How should you decide?

Data Fabric and Data Mesh are not mutually exclusive, but complementary approaches that can be combined to achieve optimal data management and integration. However, depending on your company's data maturity, complexity and goals, you may prefer one or the other solution or choose a hybrid solution that combines the best of both worlds.

Below are some points to consider when choosing between the two options. It explains which option is generally better in which situation.

 

A hybrid approach combines data fabric and data mesh to benefit from both. It is suitable for companies looking for both data integration and decentralization, depending on the preferences of different areas and use cases. This approach helps to balance trade-offs such as data duplication versus latency and governance versus autonomy.

However, the implementation of a hybrid approach is associated with challenges:

  • A complex and expensive infrastructure that supports both centralized and decentralized data management in different environments may be required.

  • Lack of clarity over ownership and accountability can arise between divisions and teams with different levels of control over their data.

  • Problems with data quality and management can arise due to duplication and a lack of consistent data sequencing and metadata management.

  • Security and data protection problems can occur when data is shared across different nodes without a uniform protection framework.

 

Steps to follow after choosing the data solution

After choosing between Data Fabric and Data Mesh or a hybrid solution, you need to follow a few steps to successfully implement and execute your data architecture. Here are some general steps and questions that can guide you through this process:

 

Data Fabric and Data Mesh are two new data architectures that can help you to manage and integrate your data effectively and efficiently. Depending on your company's data requirements and preferences, you can choose one or the other solution. We are here to support you! Don't hesitate to contact us for an initial consultation. With our end-to-end approach, we can guide you from decision making to successful implementation.