If you’re well-versed in the world of computing, you may already have an understanding of these two terms. However, it’s not uncommon for business owners to be unaware of the differences between data mesh and data fabric.
Both terms refer to two unique approaches to tackling the challenge of efficiently managing, organising and extracting value from your data, with each method differing from the other in several ways. There’s nothing to worry about if you’re new to the world of data mesh and data fabric though, as we’ve created this page to guide you through each concept in full.
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Data Mesh vs Data Fabric: What’s the Difference?
So, data mesh vs data fabric – what are these two data products and how do they compare to one another?
Essentially, data mesh takes a decentralised approach to data management, focusing primarily on domain-driven design. Instead of relying on a central team or a data lake (which is a centralised system that stores, processes, analyses and secures huge amounts of data), data mesh principles empower individual business teams to own their data as a product.
In simple terms, data mesh is an approach to managing data and data products where each team in your company is responsible for handling their own data. Every team handles their own part of the data, instead of having one central team to manage it all, making the process much easier to use. With this approach, your teams can work independently from one another, using the power of big data to make key decisions.
A data mesh approach offers a great deal of scalability and flexibility for modern organisations, based on four key principles:
- Domain-driven decentralisation: This principle ensures your data is owned by those users who understand it the most
- Data as a product: Data is treated as a product (instead of using data management as a service), whilst other teams within your business are treated as customers.
- Self-service data platform: A self-serve design is used that allows decentralised data domains to communicate with each other
- Federated governance: Data governance standards are implemented to allow the many decentralised domains and data products within your data mesh design to work together.
How Does Data Fabric Compare?
Data fabric, on the other hand, is a unifying logical architecture designed specifically to create seamless data integration across diverse systems and environments. Advanced technologies like AI (artificial intelligence), automation, and metadata management can be used to create a connective layer that enables data to be accessed and used in real time. Your data operations can be simplified with a data fabric approach, ensuring that all your data, (regardless of where it came from) is interconnected and readily available to all users and applications who might need it.
In layman’s terms, a data fabric system connects all the different data sources in your business, helping your teams to find and use data from anywhere in your company – even if some of the data is stored in different formats or systems. Essentially, this form of data platform architecture works like a network of data, making it much easier to analyse and share as needed.
Both data mesh and data fabric help businesses and organisations to use and manage their data more effectively, but in different ways.
How Do Data Mesh and Data Fabric Differ?
Both data fabric and data mesh are different ways in which businesses can address the challenges that arise in data management and architecture. However, one of the main differences between both data processes is that data fabric largely uses a centralised model, with data mesh instead advocating for decentralisation and a domain-driven design.
The other key differences between data mesh and data fabric you should know about are:
- A unique architectural approach: As previously stated, data mesh processes prioritise decentralisation, with data being owned by the individual teams that use this data the most. However, data fabric creates a centralised system that connects all potential data sources within your business into a data network.
- Distinct focus areas: Data mesh and data fabric also have distinct focus areas. With data fabric, modern technologies like AI and automation are used to collate and organise data, whereas data mesh is more concerned with how your teams are organised and work with your data.
- Scalability and flexibility: A data mesh approach usually suits businesses that have clear team structures and require flexible data needs. On the other hand, data fabric methods are often suited to companies with numerous, scattered data sources that need to be connected.
- Complexity and challenges: Data mesh implementation is complex and typically requires a significant cultural change for your business, as well as a high level of cross-team collaboration and teamwork. On the other hand, data fabric instead requires advanced technology and a suitable infrastructure, as well as expertise in automation and AI.
These are the main differences between these two forms of data technology. Hopefully, you should now have a greater understanding of which option is best suited for your organisation.
Data Mesh vs Data Fabric: Are There Any Similarities?
While data mesh and data fabric are both very different data solutions, they do share several key principles and goals, aiming to improve data usability and accessibility for the user:
- Data access: Both methods aim to make data more accessible to teams and stakeholders within an organisation, enabling them to use data and gain new insights with ease
- Scalability: As your business expands, so do your data capabilities. Both data mesh and data fabric are scalable, enabling your teams to handle more data as your company grows.
- Eliminate silos: Both approaches have a core objective of breaking down silos, connecting data across your business through either integrated data systems (data fabric) or team ownership of data (data mesh). Connectivity is promoted with both options, unifying data to make better decisions when required.
- Governance and security: Data governance is essential for ensuring compliance, governance and cyber security within your business. With both data solutions, you can ensure your data is always managed securely and complies with legal regulations, as well as keep your data private.
- Increased efficiency: Reducing the reliance on silos and streamlining your data processes naturally increases the efficiency of your business, removing unnecessary barriers and making data integration and accessibility much easier.
Data mesh and data fabric may involve two rather different methods but it’s clear to see that their long-term goals are largely the same.
The Benefits of Data Mesh vs. Data Fabric Explained
Now that you understand exactly what data mesh and data fabric are, we should go through the main advantages of both systems to ensure you utilise the right solution for your data needs.
Data mesh has several key benefits, making it particularly useful for organisations that need to prioritise agility and domain-specific data management programs:
- Improved scalability: With data mesh, data ownership is spread across your team, enabling them to manage their own data and quickly scale as needed.
- Gain faster data insights: Decisions can be made much more quickly without waiting for central approval due to the decentralised data governance on offer with data mesh.
- Increased accountability: Each team is responsible for the reliability and quality of their data, fostering a positive work culture of accountability and honesty.
- Improved resilience: The risk of bottlenecks is decreased due to the decentralised ownership used with a data mesh approach, improving IT resilience. This means that your data mesh teams can operate independently from one another, so challenges in one domain do not slow down other teams.
However, data fabric also offers a range of unique benefits for organisations that manage diverse and distributed centralised data environments:
- Integration made seamless: With data fabric, all your data sources are connected smoothly to ensure a consistent flow of information across your systems and platforms.
- Real-time access on demand: Advanced technologies can be leveraged in real-time when using data fabric, ensuring that your users and applications always have access to the most up-to-date data, making for much faster decision-making processes.
- Reduced complexity: AI and automation-based processes are used to make managing complex hybrid and multi-cloud environments easier, saving you valuable time and resources.
- Get ready for the future: Data fabric is flexible enough to handle and manage new data challenges as they arise, adapting well to evolving data landscapes to ensure your business can meet future data challenges with ease.
Choosing between data mesh and data fabric will depend largely on your organisation’s goals, size, and technical capabilities, but it isn’t a decision that should be taken lightly. If your business prioritises domain-specific agility, then you may benefit more from data mesh. On the other hand, if you have several diverse data sources that require seamless integration, data fabric could be ideal.
You may even find that combining both approaches gives you a competitive edge in the market!
Making the Choice Between Data Mesh and Data Fabric
We hope this page has clearly outlined the key differences and similarities of these two data solutions, showing you which system might best suit your business needs. Ultimately, there is no one-size-fits-all solution, as both approaches offer their own unique advantages and requirements.
However, by understanding these differences, you should be able to determine which method is right for your needs. Regardless of whether you choose to go for data mesh or data fabric (or even a combination of the two), sourcing the best approach can help you modernise your data architecture and business.
At M247, we’re an expert team of connectivity and data consultants, using our combined skills to boost the stature of every business that works with us. We can help keep your data safe through our managed cyber security services, as well as offering data management for a range of companies.
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