Usually, we're more inclined to go for centralized approaches, pretty much like a big block of data that everyone had access to and everyone could access. This is a great approach but with how much data we use and all the changes and security coming along data, this has become a relatively unwieldy way to go about data.
Enter Data Mesh—a decentralized approach to managing and scaling data. Moving away from the previously mentioned big block of data, it focuses more on domain-oriented modules. Every team or department handling their own data allows agile, easy-to-scale, and convenient data management that stays at a smaller level but still maintains the holistic view.
The Core Principles of Data Mesh
Data Mesh is built on four main principles:
- Domain-Oriented Decentralization: Each business domain (like marketing, finance, or HR) owns its own data pipeline and datasets. This implies you will have a data-specific person who is familiar with domain data.
- Data as a Product: Data isn't just a byproduct of actions. Data itself are valuable products that can bring tangible results and should be treated for their value.
- Self-Serve Data Infrastructure: As each domain handles its own data, these teams need tools and platforms to actually handle their data on both entry, usage, and maintenance without relying on a central team.
- Federated Computational Governance: It goes without saying that there's a balance of freedom and control when it comes to data. A fine line between controlled use and abuse. While teams control these domains, there will still be overarching standards to retain for the sake of consistency and compliance.
Zooming In: The De-Centralized Module
Now, let’s get into the fun part—the De-Centralized Module of a Data Mesh. This is where the magic happens, allowing data to be managed, processed, and accessed independently across different domains.
1. Domain Data Ownership:
In the De-Centralized Module, data is broken down into domain-specific modules. For example, the Sales department would manage its own Sales Data Module, while the Product team would handle the Product Data Module. Each domain is responsible for the data pipeline—from ingestion, processing, storage, to serving data. This approach allows for quicker, more domain-relevant decision-making, as each team has the flexibility to optimize their data processes.
2. Interoperability Through APIs:
Though each domain owns its data, these domains don’t exist in isolation. They need to communicate and share data. This is where interoperability comes in, enabled by APIs (Application Programming Interfaces). These APIs ensure that data can be shared seamlessly between domains, maintaining consistency and avoiding data silos.
3. Polyglot Persistence:
One size does not fit all in the world of data storage. Different data types require different storage solutions—this is known as Polyglot Persistence. In a De-Centralized Module, each domain can choose the best storage technology for its specific needs, whether it’s a relational database for structured data, a NoSQL database for unstructured data, or a data lake for vast amounts of raw data.
4. Data Product Thinking:
In the decentralized approach, data is treated as a product. Each domain must consider the data lifecycle—from creation and maintenance to deprecation. Teams are encouraged to think of their data as a product that serves other teams, stakeholders, and even external customers. This mindset fosters better data quality, documentation, and usability.
5. Event-Driven Architecture:
The de-centralized module complements the event-driven architecture. This means the data captured are propagated through real-time event streams. For instance, when you make a new sale you'll have trigger-based events that auto-update sales data, finance data, and also inventory data. Event-driven architecture specializes in handling responsive data ecosystems.
The Benefits of De-Centralization
- Scalability: As organizations grow, a centralized system can become a bottleneck. By decentralizing, each domain can scale independently, ensuring that growth doesn’t come at the cost of performance.
- Agility: Teams can make decisions faster without waiting for a central authority. This agility is especially crucial in fast-paced industries where quick data-driven decisions are a competitive advantage.
- Resilience: If one module encounters an issue, the entire system doesn’t come to a halt. Decentralization allows for more robust and resilient data architecture.
- Domain Expertise: Who better to manage data than the people who use it daily? Decentralization empowers domain experts to take control, leading to better data accuracy and relevance.
Challenges to Consider
The biggest challenges in running data domains are consistency, governance, and integration. However, this can be contained to a level by ensuring there are core requirements and security rules across all data domains to make sure they work together when they should and have no issues at integration. This also benefits governance as we now will have a standardized data method that covers all grounds with no blind spots.
Conclusion
Data Mesh has become increasingly popular despite challenging the status quo of data handling. We at one point were so used to centralized systems we didn't even think we would resort to decentralized systems that handle data as departmentalized domains. Of course, it is not without any cons. We see the glaring issues in consistency, governance, and how you'll integrate these data between domains. However, with thoughtful implementation and a balance of autonomy with governance, there is a good chance you will most likely only see the benefits.