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Modern Data Architecture: Data Mesh vs. Data Fabric – Part I

Picture of Kellyn Cochell By   ·  2 minute read

By 2026, 66% of data used as candidate master data will be identified as data fabric or data mesh. In this blog post, we will explain these two approaches to data management – focusing specifically on data mesh strategy development, best practices, and challenges – and how to identify what is right for your firm. For more on data fabric, please see Part II of this blog series.

Definitions: Data Mesh & Data Fabric

Data Mesh: Data mesh is an architectural framework that involves people, process, and technology with the goal to create easy access to data.

Data Fabric: Data fabric is a data management design that aims to reduce the time to integrated data delivery though active metadata-assisted automation.

Data Mesh Overview

Goals:

  • Distributed and decentralized data ownership
  • Data is treated as a product with specific owners
  • Data is organized by business domain, teams manage their own data pipelines under an established central framework
  • Self-service data & scalable data operations for faster time to insight

Considerations:

  • Are bottlenecks for data implementation and access inhibiting growth?
  • What is the current architectural state, are you ready to support data mesh?
  • What is the current state of your data governance program? Do you have data stewards and owners?
  • Are there key stakeholders to champion the change?

DataMeshDataFabric-Image1

Source: https://data.world/blog/how-to-scale-your-data-culture-with-data-mesh/

 

Data Mesh: Develop a Strategic Road Map

To begin developing a foundational data mesh strategy, your organization should explore the following questions.

Why?

  • What problem are we solving? Are we struggling with data silos? Are our current practices error-prone?
  • What about our data strategy and integration is inhibiting our growth and success?
What?
  • What are we implementing?
  • What are the common use cases we will address?
  • What are the quick wins vs. the more challenging problems we aim to fix?
How?
  • Who needs to support this project initially vs long-term? Who are the stakeholders and future owners?
  • What skills do we need? Are we staffed to support this? Do we need an outside partner?
Where?
  • What is our current architecture, and do we need to change it to support this initiative?
  • What vendors do we use?

 

Data Mesh: Best Practices

  • Start by evaluating your existing tools
  • Use well-established integration technology and standards
  • Establish defined data domains and data products that align with your business
  • Prioritize implementation for business lines with the most data expertise and governance
  • Communicate standards across teams and business lines
  • Set a realistic timeline to implement and add value as you go

 

Lessons from the Early Adapters of Data Mesh

DataMeshDataFabric-Image2

 

Data Management Operating Model for Data Mesh


DataMeshDataFabric-Image3

 

Common Mistakes and Challenges with Data Mesh

  • Difficult Organizational Silos: Lack of leadership buy-in, no clear data ownership and governance
  • Lack of Adoption and Confidence: Hesitation to alter current architecture and adopt new framework
  • Decreased Data Quality: Lack of monitoring or ownership of data across domains can lead to poor data quality, the opposite effect as each team should be domain experts managing their data sets
  • Security and Compliance Challenges: Centralizing security and compliance is necessary while maintaining independence in each business domain

Snowflake as a Key Tool for Data Mesh

  • Centralized, Scalable Platform: Provides data lakes, data warehousing, and data sharing under a single, integrated platform that tracks the lifecycle of your data and stores it in the cloud
  • Secure Real-Time Data: Supports multi-cloud architectures across accounts, regions, and clouds within the Snowflake ecosystem 
  • AI Capabilities: Leverage open-source LLMs for data analytics through Snowflake’s Cortex to add unique value for business domains

Ready to Modernize Your Data Architecture?

Partner with Continuus to identify whether data mesh or data fabric is the right approach for your organization, and redefine what’s possible for your business. Contact us today to explore how we can help you stay ahead in an ever-evolving data landscape.