Data Fabric vs Data Mesh: Find the Right Fit for Your Organization
Organizations are increasingly adopting data-driven strategies to enhance company operations and gain a competitive edge in today’s digital market. This makes it essential for businesses to continuously improve how they access and manage their data. “Data fabric” and “data mesh” are two prominent developments that have surfaced in this field, and if you’re willing to walk the cutting edge to maximize the value you get from your data, it’s essential that you become familiar with them.
Due to their potential to fundamentally transform data management within an organization, the distinction between a data fabric and a data mesh is crucial to understand — yet often left unclear. So let’s make sure you’re equipped to pick the one that’s right for your organization.
What’s a Data Fabric?
Data fabric is an integrated data architecture with security, adaptability and versatility as its primary benefits. It enables teams and departments to access and use data in a consistent and unified way, regardless of where the data is stored or how it is structured.
One of the key benefits of a data fabric is that it makes it easy for organizations to manage and govern their data. This in turn can help improve data quality, reduce data silos and increase data security.
Additionally, a data fabric can also enhance collaboration between different teams and departments , as it ensures that they can access and use the same data in a consistent way.
What’s a Data Mesh?
Data mesh is becoming increasingly popular as an alternative approach to data management. A data mesh differs from a data fabric in that it’s decentralized and modular, whereas a fabric is centralized and monolithic.
In a data mesh, data is treated as a product and is managed by independent teams who are responsible for the data they own. These independent teams make the decisions about how the data is stored and processed without having to rely on a central team to manage the data infrastructure and pipelines.
A primary advantage of data meshes is that they can greatly boost the organization’s agility. This is because they enable teams to quickly and easily access and use the data they need, without having to navigate through complex data architectures or rely on centralized infrastructure teams to make changes.
Additionally, a data mesh can improve data quality, as teams are more closely collaborating with the data they own and are better positioned to understand and manage it.
Cheat Sheet: Differentiating between Data Fabric and Data Mesh
- Centralized vs decentralized: A data fabric is a centralized approach to data management, where a single team or department manages and governs the data infrastructure. A data mesh, on the other hand, is a decentralized approach where multiple teams or departments are responsible for managing the data they own.
- Monolithic vs modular: A data fabric is a monolithic approach, where data services are integrated to create a single, unified data environment. Meanwhile, a data mesh is modular, with the data managed by independent teams responsible for the data they own.
- Organizational vs team governance: A data fabric empowers the organization to easily manage and govern their data, which can help to improve data quality and reduce data silos. A data mesh leaves it to teams to govern the data they own, yielding a more decentralized approach to data governance. This latter approach can make it more challenging to ensure consistent data quality and security across teams, but can also lead to more involved ownership and accountability for data.
So, Should You Go With a Fabric or a Mesh?
There are multiple advantages and disadvantages with both data mesh and data fabric, and they can both be used to improve data management within an organization. However, you should base your decision on the specific needs and goals of your organization — see the following list for some of the main factors that should influence your pick.
Data complexity: A data mesh is best suited for complex data environments where a decentralized approach benefits data usage goals at multiple levels — or is simple the far more viable option, while a data fabric is more appropriate for simpler data architectures that can and should be centralized to serve business needs.
Governance requirements: If your enterprise requires strict data governance — e.g. due to being involved in an industry with strong compliance — or you simply want to ensure uncompromising data security, a data fabric may be the better option due to its ability to promote a consistent and unified approach to data management.
Data volume: For large-scale data environments that require high-volume data processing and analysis, a data fabric can represent a step up from traditional data warehouses and data lakes — as well as from a mesh — due to its superior capability to boost data discoverability.
Organizational structure: The more decentralized your organization and the more autonomy your teams have in their day-to-day operations, the more likely it is that a data mesh will prove more effective than data fabric, which is better suited for centralized organizations with a more hierarchical structure.
Collaboration: Organizations that struggle with data silos and want to improve collaboration between different teams and departments may a data fabric a good fit. A fabric’s centralized framework for data management is conducive to reducing silos and, in turn, making teams more closely aligned with the data they own. This helps drive data engagement, sharing and, ultimately, collaboration.
Agility and flexibility: As a general rule, a data mesh provides greater agility and flexibility in accessing and using data — while promoting localized ownership of and accountability for it. A mesh’s decentralized approach to data governance empowers teams to quickly and easily access and use the data they need without having to navigate complex data architectures.
If you could use expert advice on which architecture is right for your organization, we at Starschema are here to leverage our expertise in building data platforms for Fortune 500 companies and beyond to help you make the right choice and get the most value out of it. Get in touch — we’d love to talk to you.
About the author
Anjan Banerjee is the Field CTO of Starschema. He has extensive experience in building data orchestration pipelines, designing multiple cloud-native solutions and solving business-critical problems for multinational companies. Anjan applies the concept of infrastructure as code as a means to increase the speed, consistency, and accuracy of cloud deployments. Connect with Anjan on LinkedIn.
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