Articles | July 5, 2024

Data Mesh vs Data Fabric: Their Purpose, Similarities, Differences & How They Work Together 

In today’s digital world, data is everywhere. Companies of all shapes and sizes are swimming in data, but simply having various data products isn’t enough to create a competitive advantage. Instead, you need to enable teams to use and share data in a way that’s consistent, powerful, and insightful.

data mesh vs data fabric

Say hello to Data Mesh and Data Fabric. These are not just fancy tech buzzwords, but game-changing approaches to data architecture, transforming how organisations handle, understand, and use data. But what exactly are they? How do they work? And most importantly, how can they help businesses drive better results? 

In this article, we’ll explore what a Data Mesh and a Data Fabric technologies are, their similarities and differences, and how they can work together to turbocharge your entire data management approach. 

Let’s get started! 

What is a Data Mesh? Decentralized Data & More 

A Data Mesh is a way of organizing and using data in big companies. Instead of having one central team and system that handles all the data, a Data Mesh splits up the responsibility among the teams or departments who are closest to the data source. For example, this means HR teams owning HR data, and Sales teams managing Sales data.  

Think of it like a family dinner. Rather than one person cooking the entire meal, each family member cooks the part they’re best at. This way, everyone gets to share the meal, with each part being extra delicious as it was created by the expert. 

Here’s a breakdown of some key principles in the data mesh approach, and how they work in practice: 

  1. Data as a Product: Each team treats its data like a product that they offer to the rest of the company. They ensure data is high-quality, easy to use, and valuable for others. 
  2. Domain Ownership: The teams that create or use the data the most are responsible for managing it. After all, they know their data best. 
  3. Self-Serve Data Platform: Each domain manages their data in a system that enables sharing, making it easy for other teams to access and use the data. 

      In our family dinner analogy, as well as creating the dish, each family member also shares the recipe and answers questions about it. This way, everyone gets to enjoy the meal and benefit from new insights and information.  

      Expert insight 
      Piotr Rembowski, Principal Data Engineer at Inetum 

      “In the world of Big Data, it is necessary to choose the right architecture to allow for effective data management, scaling, and resource optimization. In recent years, Data Mesh architecture has become one of the leading concepts for Big Data”.  

      The Advantages & Disadvantages of a Data Mesh 

      Creating a data mesh architecture takes time and effort, with many organizations needing to move from a centralized data model, to a decentralized alternative.  

      Let’s have a look at some of the advantages and disadvantages of a data mesh.  

      Advantages: 

      1. Speed and Flexibility: Because different business teams can work on their data domain independently, they can move quickly and adapt to changes without waiting for a central data team. 
      2. Better Quality Data: The teams that know the data the best can ensure it is accurate and useful, leading to additional benefits of data quality. 
      3. Increased Innovation: When teams have control over their data, they’re more likely to come up with creative ways to use it. 
      4. Scalability: As the company grows, the Data Mesh can easily grow with it by adding new teams and domains. 
      5. Improved Data Literacy: When more people in the company work directly with data, it breaks down data silos and improves overall understanding and use of data improves. 

            Disadvantages: 

            1. Complexity: Setting up a Data Mesh isn’t easy. It requires a big shift in how people think about and work with data, as well as additional effort from experts such as data scientists and enterprise data architects. 
            2. Potential for Inconsistency: With different teams handling data differently, there’s a risk of inconsistencies across data storage, data security, and data virtualization processes. 
            3. Need for New Skills: Teams need to learn new skills to manage their data effectively, which can be challenging and time-consuming. 
            4. Initial Slowdown: Implementing a Data Mesh can slow things down at first as everyone adjusts to the new approach to data management. 
            5. Governance Challenges: Balancing centralized rules with team autonomy can be tricky and may lead to conflicts. 

                  What is a Data Fabric? Data Quality & Consistency 

                  A Data Fabric is an architecture that weaves together different data sources, types, and locations into a unified capability. It’s like an intelligent, invisible layer that sits on top of all your data, making it easier to access, manage, and use, regardless of where it’s stored or what format it’s in. 

                  Here’s how a data fabric architecture brings everything together: 

                  1. Data integration: It connects all the data sources together, whether they’re in the cloud, on your computer, or somewhere in between. 
                  2. Data Automation: It picks up the work of moving and preparing data, using smart algorithms to quickly manage the complexity of data technologies. 
                  3. Data Discovery: It helps you find the data you need, even if you’re not sure where it’s stored – essentially creating a huge data catalog. 
                  4. Data Governance: It sets the governance standards, and keeps track of who’s using what data and makes sure everyone follows the rules.
                  5. AI and Machine Learning: These technologies help the Data Fabric learn and improve over time, making data management smarter and more efficient. 

                          You can think of a data fabric as a super-librarian who not only knows where every book in the library is but can also instantly translate them, combine information from different books, and even suggest books you might find useful – all in the blink of an eye. 

                          Also read: 

                          The Advantages & Disadvantages of a Data Fabric 

                          Like a Data Mesh, creating a Data Fabric is no small task. Some of the advantages and disadvantages of implementing a data fabric include: 

                          Advantages: 

                          1. Unified Data Access: Regardless of where your data pipelines or data warehouses are stored, a Data Fabric makes it easy to find and use data. 
                          2. Improved Data Quality: With built-in data governance and quality checks, you can trust the data you’re using. 
                          3. Faster Insights: Automation and smart data management bring together various data sources to enable users to get answers faster. 
                          4. Flexibility: A Data Fabric approach creates flexibility, as it can adapt to new data sources and technologies, future-proofing your data infrastructure. 
                          5. Reduced Complexity: While the technology itself is complex, it simplifies data architecture across the board, making life easier for data consumers. 

                          Disadvantages: 

                          1. High Initial Cost: Implementing a Data Fabric can be expensive, requiring significant investment in technology and inputs from data engineering experts. 
                          2. Potential for Over-Reliance: There’s a risk businesses can become too dependent on the Data Fabric, reducing data skills and competencies within the organisation. 
                          3. Security Concerns: With data from multiple sources flowing more freely, ensuring security and privacy can be more challenging. 
                          4. Change Management: Adopting a Data Fabric often requires significant changes in how people work with data, which can be met with resistance.

                          How Do Data Mesh and Data Fabric Technologies Work together to Improve Data Management? 

                          Now, you might be thinking, “If Data Mesh and Data Fabric are both about managing data, do I have to choose between them?”  

                          The good news is, you don’t! As we’ve seen from the descriptions, data mesh focuses on the structure and location of data, where a data fabric enables organizations to bring data together quickly and consistently. 

                          Here’s how the two complement each other to drive data management transformations: 

                          1. Structure: Data Mesh provides the structure of the data, with the Data Fabric determining the standards and rules each domain should apply. 
                          2. Data Discovery: Data Mesh aligns data with its rightful owner, with a Data Fabric there to bring different data across the business together. 
                          3. Centralization vs Decentralization: Data Mesh promotes a decentralized approach to data ownership, while Data Fabric provides a centralized approach to data ingestion to access and manage this decentralized data. 
                          4. Data Quality: Data Mesh puts the responsibility for data quality in the hands of domain experts, while Data Fabric provides the tools to enforce and monitor data quality across the organization. 
                          5. Self-Service: Data Mesh promotes a self-service culture, and Data Fabric solutions provide the technical capabilities to make self-service a reality. 
                          6. Scalability: As the organization grows and data becomes more complex, Data Mesh provides a scalable organizational model, while Data Fabric offers a scalable technical solution. 

                                Where Does Data Governance, Data Lakes and Data Warehouses Fit In? 

                                While this article has focused on the difference between data meshes and data fabrics, other terms such as data lakes and data warehouses come up a lot in conversation – so where do they fit it?  

                                Well, to answer the question, let’s have a look at how they supplement your wider data management processes.  

                                • Data Lakes and Data Warehouses. Both of these are used to store either relational and non-relational data from various sources. While historically used as disparate data sources, under a Data Fabric model, the use and control of data lakes and warehouses is more tightly aligned.  
                                • Data Governance. This refers to your overarching approach for managing data, including the assurances in place to check quality and the management structures to make decisions. Both data meshes and data fabrics form part of your broader governance, shaping the way you manage data within your business.  

                                The Challenges and Considerations of Implementing a Data Fabric and Data Mesh 

                                While Data Mesh and Data Fabric provides a unified approach and exciting possibilities, implementing them isn’t as simple as flipping a switch. Here are some key challenges and considerations to keep in mind: 

                                1. Cultural Shift: Both are two different approaches to data and require a significant change in how people think about and work. This can be met with resistance and requires careful change management. 
                                2. Skills Gap: Data Mesh is an emerging technology, so implementing and maintaining these systems requires new skills. Organizations need to invest in training or hiring people with the right expertise. 
                                3. Technology Investment: Data fabric is technology-centric and, as such, needs a significant investment in sophisticated tools and technologies to make it work. 
                                4. Scalability: While both approaches are designed to be scalable, actually scaling them in practice can be challenging, especially for large, complex organizations. 
                                5. Integration with Existing Systems: Data Mesh focuses on organizational system restructuring, but most organizations already have existing data systems in place. Integrating Data Mesh or Data Fabric with these existing systems can be complex. 
                                6. Measuring Success: It can be difficult to quantify the benefits of these approaches, especially in the short term. Organizations need to think carefully about how they’ll measure success and demonstrate value for money.  
                                7. Choosing the Right Approach: Data Mesh and Data Fabric aren’t one-size-fits-all solutions. Organizations need to carefully consider their specific needs and challenges to decide which approach (or combination of approaches) is right for them. 

                                            Realise The Benefits of Data Mesh & Data Fabric by Working With an Expert Partner 

                                            The challenging of selecting the right approach can be a daunting one, and that’s why we’d always recommend working with a trusted IT partner. At Inetum, we’ve helped hundreds of clients transform the way they store, share, and use data, helping them reach new heights and win more business. 

                                            Our Data Consulting approach starts by helping you define a vision and strategy that’s right for your digital cultural, assets and internal skills. From there, we guide you through implementing sound data governance built around a data architecture that aligns to your local IT constraints, finances, and culture to transform the way you work.  

                                            BigCTA MarekCzachorowski

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