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What is Data Mesh? Architecture and best practice guide

Lets learn about data mesh architecture , examples, principles and about building a platform.

By

Jatin Solanki

Updated on

November 14, 2024

Data Mesh Concept

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image credit: Photo by Alina Grubnyak on Unsplash

Ever tried to make sense of lots of data spread out in your organization? It's like trying to solve a puzzle with pieces from different boxes. But what if you could turn this mess into a beautiful symphony of information? That's what data mesh does, changing how we manage our most valuable asset: data.

Data Mesh was first introduced by Zhamak Dehghani, a software architect at ThoughtWorks, in a 2019 blog post. Since then, it has gained traction as a promising new approach and as per techtarget, it can be the next big analytics trend.

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As we look at the changing world of data architecture, we see the problems with old systems. They get overwhelmed by too much data and become too complex. Data mesh brings a fresh idea, a decentralized system that lets our data shine.

Let's dive into this new idea. We'll see how data mesh lets domain experts lead, sparks new ideas, and grows our data systems in amazing ways. It's more than just new tech; it's a new way of thinking about managing data in big companies.

Key Takeaways

  • Data mesh introduces a decentralized approach to data management
  • It addresses limitations of traditional centralized architectures
  • Domain experts are empowered as data owners
  • Scalability and innovation are key benefits of data mesh
  • The concept represents a paradigm shift in enterprise data strategies

Understanding the Data Mesh Paradigm

Data mesh is a new way to manage data in big companies. It tackles the complexity and size of data today. We'll look at what data mesh is and how it changes old data systems.

Definition and Core Concepts

Data mesh sees data as a product and uses a shared governance model. It lets domain experts handle their data, ensuring it's good and easy to get. This makes data management faster and more flexible for companies.

Data Mesh vs Traditional Architectures

Data mesh is different from old data systems like lakes or warehouses. It uses a spread-out model where each area looks after its data. This makes things more flexible and helps companies make quick decisions based on data.

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Data Mesh Architecture
Data Mesh architecture

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Implementing Data Mesh - The best way to create a data mesh

The process of implementing Data Mesh involves a combination of technical and cultural changes. It requires careful planning, collaboration, and alignment with business objectives. Some key steps involved in implementing data mesh are:

  1. Mark domains and data products: The first step is to identify the domains and data products that are most critical to your business. This involves mapping out your business processes and identifying the different areas of your organization that generate and use data. Once you have identified these domains and data products, you can start to break down silos and distribute ownership and responsibility for data management across different teams and domains.
  2. Understand domain-driven design principles: Data Mesh is based on domain-driven design principles, which emphasize the importance of aligning data products with business objectives and user needs. To implement Data Mesh, you will need to define domain-driven design principles for each domain and data product. This involves defining the business context, language, and boundaries of each domain, as well as defining the data products and services that will be used to manage data within each domain.
  3. Build a data platform team: To support data mesh, you will need to build a data platform team responsible for providing the necessary infrastructure and tools to support data management across different domains. This team will be responsible for building and maintaining a data platform that provides the necessary data services, APIs, and data pipelines to enable seamless data sharing and collaboration across domains.
  4. Define and Implement data governance: Data governance is a critical component of Data Mesh, as it ensures that data is managed in a consistent and compliant manner across different domains. To implement data governance, you will need to define data quality standards, data security policies, and data privacy regulations that apply to each domain and data product. Establishing a data governance framework that ensures that these standards and policies are being adhered to across the organization is critical.
  5. Create and run data mesh architecture: Data Mesh is built on a modular, decentralized architecture that promotes collaboration and autonomy. To implement Data Mesh, you will need to build a data mesh architecture that supports this decentralized approach to data management. This involves breaking down silos and implementing data products and services that are designed to be interoperable and modular, allowing teams to work independently while still being able to collaborate and share data seamlessly.
  6. Foster a culture of collaboration and autonomy: Finally, implementing Data Mesh requires a cultural shift towards collaboration, autonomy, and data-driven decision-making. To foster this culture, you will need to establish clear communication channels, incentivize collaboration and knowledge sharing, and promote a culture of experimentation and continuous improvement.

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‍What are the tools and technologies that can be used to implement data mesh?

There are a variety of tools and technologies that can be used to implement data mesh like containerization, microservices, event-driven architecture, domain-driven design frameworks, and data mesh platforms. These tools and technologies with flexible data architecture that aligns with business objectives can empower teams to make data-driven decisions.

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Common challenges and how to overcome them: 

Implementing Data Mesh can be challenging, especially for organizations that are accustomed to more traditional approaches to data management. Common challenges include cultural resistance, technical complexity, and the need for ongoing governance and maintenance. To overcome these challenges, it's important to establish clear communication and collaboration channels, provide training and support for teams, and implement a robust governance framework that promotes accountability and ownership.

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Benefits of Data Mesh and why you may need it:

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1. Improved data quality and accuracy: 

By distributing data ownership and responsibility across different teams and domains, data mesh can help improve data quality and accuracy. This is because each team is incentivized to ensure that their data products meet high standards and are fit for purpose. Additionally, data mesh encourages the use of domain-driven design principles, which can help ensure that data products are aligned with business objectives and reflect the needs of their intended users.

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2. Increased scalability and flexibility:

Traditional approaches often struggle to keep up with the pace of change and scale to meet growing demand. Data Mesh is designed to be more scalable and flexible helping businesses respond more quickly to changing business needs and support new use cases and data sources.

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3. Better alignment with business objectives:

Data Mesh encourages a more outcome-focused approach to data management, where teams are incentivized to create data products that deliver value to the business. This can help ensure that data management efforts align with business objectives and that teams work towards shared goals. By focusing on outcomes over outputs, Data Mesh can help businesses achieve more meaningful insights and impact from their data.

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4. Cost-effectiveness and reduced overheads: 

Traditional approaches to data management can be expensive and resource-intensive, requiring dedicated teams and infrastructure to manage. On the other hand, Data Mesh is designed to be more cost-effective and efficient by distributing data management responsibilities across different teams and domains. 

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5. Future applications of Data Mesh:

Data Mesh could be applied to emerging technologies like AI and machine learning or used to support new business models like the sharing economy. By embracing Data Mesh, businesses can stay ahead of the curve and ensure that their data management practices remain flexible and adaptable to changing business needs.

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What’s in store for Data Mesh:

It is clear that conventional strategies for data management are no longer adequate. Data Mesh offers a promising new approach that can help businesses stay agile, flexible, and competitive in a rapidly changing landscape. 

However, the thing to consider here is, data mesh is not the only approach to data management, and there are several other methodologies and frameworks that can be used in similar use cases. Traditional data warehousing approaches may be more appropriate in cases where there is a need for a single source of truth, while event-driven architectures may be more appropriate for real-time data processing needs.

It is vital to realize that executing data mesh is not a one-size-fits-all solution. Organizations will need to customize their approach to their specific needs and business objectives. Implementing Data Mesh requires a cultural shift towards collaboration, autonomy, and data-driven decision-making. It may take time and effort to fully embrace these principles, but the benefits of data mesh make it a worthwhile investment for forward-thinking organizations.

Data Mesh is an approach that you can't afford to ignore.

Lets start with laying down the foundation for data mesh architecture and decube is a perfect tool for that - Signup here

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What is a Data Trust Platform in financial services?
A Data Trust Platform is a unified framework that combines data observability, governance, lineage, and cataloging to ensure financial institutions have accurate, secure, and compliant data. In banking, it enables faster regulatory reporting, safer AI adoption, and new revenue opportunities from data products and APIs.
Why do AI initiatives fail in Latin American banks and fintechs?
Most AI initiatives in LATAM fail due to poor data quality, fragmented architectures, and lack of governance. When AI models are fed stale or incomplete data, predictions become inaccurate and untrustworthy. Establishing a Data Trust Strategy ensures models receive fresh, auditable, and high-quality data, significantly reducing failure rates.
What are the biggest data challenges for financial institutions in LATAM?
Key challenges include: Data silos and fragmentation across legacy and cloud systems. Stale and inconsistent data, leading to poor decision-making. Complex compliance requirements from regulators like CNBV, BCB, and SFC. Security and privacy risks in rapidly digitizing markets. AI adoption bottlenecks due to ungoverned data pipelines.
How can banks and fintechs monetize trusted data?
Once data is governed and AI-ready, institutions can: Reduce OPEX with predictive intelligence. Offer hyper-personalized products like ESG loans or SME financing. Launch data-as-a-product (DaaP) initiatives with anonymized, compliant data. Build API-driven ecosystems with partners and B2B customers.
What is data dictionary example?
A data dictionary is a centralized repository that provides detailed information about the data within an organization. It defines each data element—such as tables, columns, fields, metrics, and relationships—along with its meaning, format, source, and usage rules. Think of it as the “glossary” of your data landscape. By documenting metadata in a structured way, a data dictionary helps ensure consistency, reduces misinterpretation, and improves collaboration between business and technical teams. For example, when multiple teams use the term “customer ID”, the dictionary clarifies exactly how it is defined, where it is stored, and how it should be used. Modern platforms like Decube extend the concept of a data dictionary by connecting it directly with lineage, quality checks, and governance—so it’s not just documentation, but an active part of ensuring data trust across the enterprise.
What is an MCP Server?
An MCP Server stands for Model Context Protocol Server—a lightweight service that securely exposes tools, data, or functionality to AI systems (MCP clients) via a standardized protocol. It enables LLMs and agents to access external resources (like files, tools, or APIs) without custom integration for each one. Think of it as the “USB-C port for AI integrations.”
How does MCP architecture work?
The MCP architecture operates under a client-server model: MCP Host: The AI application (e.g., Claude Desktop or VS Code). MCP Client: Connects the host to the MCP Server. MCP Server: Exposes context or tools (e.g., file browsing, database access). These components communicate over JSON‑RPC (via stdio or HTTP), facilitating discovery, execution, and contextual handoffs.
Why does the MCP Server matter in AI workflows?
MCP simplifies access to data and tools, enabling modular, interoperable, and scalable AI systems. It eliminates repetitive, brittle integrations and accelerates tool interoperability.
How is MCP different from Retrieval-Augmented Generation (RAG)?
Unlike RAG—which retrieves documents for LLM consumption—MCP enables live, interactive tool execution and context exchange between agents and external systems. It’s more dynamic, bidirectional, and context-aware.
What is a data dictionary?
A data dictionary is a centralized repository that provides detailed information about the data within an organization. It defines each data element—such as tables, columns, fields, metrics, and relationships—along with its meaning, format, source, and usage rules. Think of it as the “glossary” of your data landscape. By documenting metadata in a structured way, a data dictionary helps ensure consistency, reduces misinterpretation, and improves collaboration between business and technical teams. For example, when multiple teams use the term “customer ID”, the dictionary clarifies exactly how it is defined, where it is stored, and how it should be used. Modern platforms like Decube extend the concept of a data dictionary by connecting it directly with lineage, quality checks, and governance—so it’s not just documentation, but an active part of ensuring data trust across the enterprise.
What is the purpose of a data dictionary?
The primary purpose of a data dictionary is to help data teams understand and use data assets effectively. It provides a centralized repository of information about the data, including its meaning, origins, usage, and format, which helps in planning, controlling, and evaluating the collection, storage, and use of data.
What are some best practices for data dictionary management?
Best practices for data dictionary management include assigning ownership of the document, involving key stakeholders in defining and documenting terms and definitions, encouraging collaboration and communication among team members, and regularly reviewing and updating the data dictionary to reflect any changes in data elements or relationships.
How does a business glossary differ from a data dictionary?
A business glossary covers business terminology and concepts for an entire organization, ensuring consistency in business terms and definitions. It is a prerequisite for data governance and should be established before building a data dictionary. While a data dictionary focuses on technical metadata and data objects, a business glossary provides a common vocabulary for discussing data.
What is the difference between a data catalog and a data dictionary?
While a data catalog focuses on indexing, inventorying, and classifying data assets across multiple sources, a data dictionary provides specific details about data elements within those assets. Data catalogs often integrate data dictionaries to provide rich context and offer features like data lineage, data observability, and collaboration.
What challenges do organizations face in implementing data governance?
Common challenges include resistance from business teams, lack of clear ownership, siloed systems, and tool fragmentation. Many organizations also struggle to balance strict governance with data democratization. The right approach involves embedding governance into workflows and using platforms that unify governance, observability, and catalog capabilities.
How does data governance impact AI and machine learning projects?
AI and ML rely on high-quality, unbiased, and compliant data. Poorly governed data leads to unreliable predictions and regulatory risks. A governance framework ensures that data feeding AI models is trustworthy, well-documented, and traceable. This increases confidence in AI outputs and makes enterprises audit-ready when regulations apply.
What is data governance and why is it important?
Data governance is the framework of policies, ownership, and controls that ensure data is accurate, secure, and compliant. It assigns accountability to data owners, enforces standards, and ensures consistency across the organization. Strong governance not only reduces compliance risks but also builds trust in data for AI and analytics initiatives.
What is the difference between a data catalog and metadata management?
A data catalog is a user-facing tool that provides a searchable inventory of data assets, enriched with business context such as ownership, lineage, and quality. It’s designed to help users easily discover, understand, and trust data across the organization. Metadata management, on the other hand, is the broader discipline of collecting, storing, and maintaining metadata (technical, business, and operational). It involves defining standards, policies, and processes for metadata to ensure consistency and governance. In short, metadata management is the foundation—it structures and governs metadata—while a data catalog is the application layer that makes this metadata accessible and actionable for business and technical users.
What features should you look for in a modern data catalog?
A strong catalog includes metadata harvesting, search and discovery, lineage visualization, business glossary integration, access controls, and collaboration features like data ratings or comments. More advanced catalogs integrate with observability platforms, enabling teams to not only find data but also understand its quality and reliability.
Why do businesses need a data catalog?
Without a catalog, employees often struggle to find the right datasets or waste time duplicating efforts. A data catalog solves this by centralizing metadata, providing business context, and improving collaboration. It enhances productivity, accelerates analytics projects, reduces compliance risks, and enables data democratization across teams.
What is a data catalog and how does it work?
A data catalog is a centralized inventory that organizes metadata about data assets, making them searchable and easy to understand. It typically extracts metadata automatically from various sources like databases, warehouses, and BI tools. Users can then discover datasets, understand their lineage, and see how they’re used across the organization.
What are the key features of a data observability platform?
Modern platforms include anomaly detection, schema and freshness monitoring, end-to-end lineage visualization, and alerting systems. Some also integrate with business glossaries, support SLA monitoring, and automate root cause analysis. Together, these features provide a holistic view of both technical data pipelines and business data quality.
How is data observability different from data monitoring?
Monitoring typically tracks system metrics (like CPU usage or uptime), whereas observability provides deep visibility into how data behaves across systems. Observability answers not only “is something wrong?” but also “why did it go wrong?” and “how does it impact downstream consumers?” This makes it a foundational practice for building AI-ready, trustworthy data systems.
What are the key pillars of Data Observability?
The five common pillars include: Freshness, Volume, Schema, Lineage, and Quality. Together, they provide a 360° view of how data flows and where issues might occur.
What is Data Observability and why is it important?
Data observability is the practice of continuously monitoring, tracking, and understanding the health of your data systems. It goes beyond simple monitoring by giving visibility into data freshness, schema changes, anomalies, and lineage. This helps organizations quickly detect and resolve issues before they impact analytics or AI models. For enterprises, data observability builds trust in data pipelines, ensuring decisions are made with reliable and accurate information.
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