In 2023, most organizations want to move away from a monolithic architecture towards a decentralized system. As data volumes grow, organizations need a real-time data-driven decision-making process to stay relevant in today's competitive market. The "big" question is whether they are able to achieve the same. Unfortunately, most times, the answer is "No."
When it comes to data infrastructures, most organizations invest in experienced data professionals and a central data lake – and expect it to drive their business. However, centralized data systems become a bottleneck in a short time.
The 2022 edition of the “State of Data and What’s Next" report points to the growing trend of companies shifting from a centralized to a decentralized data infrastructure. On average, companies use 4-6 different data platforms (while others have 12 platforms).
A data mesh architecture is now the answer to a decentralized data platform. Here’s everything you need to know about this architecture.
What is a Data Mesh?
A data mesh is a self-serving domain-based data architecture. Essentially, a data mesh architecture provides a fresh approach to data management, where data is handled efficiently irrespective of its physical location. In this innovative architecture, data is no longer siloed and can freely flow between systems and applications.
The data mesh architecture addresses the many flaws of a "monolithic" data warehouse model – and facilitates easier and faster data access. A data mesh follows 4 basic principles namely:
- Domain ownership: This principle mandates that the domain teams take complete responsibility for their data. A domain-based distributed architecture enables data ownership to move from a central data system to individual domains.
- Data as a product: This principle imparts the "product thinking" approach to analytical data. As more domains consume data, each domain team must provide high-quality data to satisfy the needs of the other domains.
- Self-serving data infrastructure: As a self-serving data platform, the data mesh applies “platform-based thinking” to the data infrastructure. This means that a dedicated data platform team provides all the necessary tools and functionality to maintain “data as a product” for all domains.
- Federated governance: This principle achieves standardization, which is required for interoperability across all data products. Through federated governance, organizations can create an efficient data ecosystem that complies with organizational and industry regulations.
Next, let’s discuss the business benefits of the data mesh architecture.
Benefits of a Data mesh architecture
As explained, the data mesh architecture powers decentralized data, which is beneficial in the form of reduced time-to-market, better decision-making, scalability, and improved business agility. In this type of architecture, organizational data is distributed across a cluster of nodes, each of which manages a data subset. This means organizations have finer control over their data access, thus making it easier to observe changes and ensure data quality.
With the use of nodes, organizations can now add more nodes to the mesh to handle more data volumes. This means efficient scalability to satisfy growing data requirements. Besides, organizations do not depend on a single “point of failure” or bottleneck when managing increased traffic or requests. Decentralized data sources are also more resilient to failures and outages.
Thanks to its domain ownership, enterprises can now organize their data based on individual business domains (for example, marketing). This enables domain owners to take full ownership of their data and access rights.
Additionally, a data mesh addresses multiple data-related challenges by:
- Formatting data across disparate data sources.
- Creating a data governance framework for data management.
- Building trust and confidence among data consumers.
- Using automation tools to access and analyze data.
As compared to data lakes, a data mesh "democratizes" data management by freeing up data "locked" in proprietary systems. This also makes it challenging to access data quickly and easily. Next, let's discuss some of the best practices for implementing a data mesh.
Best practices for a Data mesh architecture
In the real world, enterprises find it difficult and time-consuming to implement a data mesh architecture. Primarily, this is because the data mesh is still in its infancy stage. Currently, there are different types of data meshes – some of which are still centralized or partially centralized. Among the “mindset” challenges, companies continue to run their data infrastructures in a centralized and hierarchical mode.
Here are some best practices or recommendations for switching to a decentralized data mesh architecture:
- Start with a small data project: As new adopters of the data mesh architecture, organizations should start small with a single pilot project for a particular domain. For the best results, choose a data product with quantifiable business value. Additionally, ensure that this domain has the necessary skills and resources to build and support the data mesh.
- Adopt an incremental approach: Organizations need a realistic and achievable approach when implementing their data meshes. As a recommendation, do not replace your complete technology tools and stack to facilitate a data mesh. Set realistic KPIs and adopt an incremental approach. This means eliminating each silo at a time – and designing the best practices for each domain team.
- Define the independent domains: To build a decentralized system, organizations must first define the domain teams and hire resources with the right domain experience and cross-functional skills. Additionally, they must integrate the best domain specialists with the technical talent to drive business value.
- Switch to microservices: Another best practice involves companies to understand the working of a domain-driven design (or DDD). Using DDD concepts, they can easily switch to microservices and split microservices across domains. This makes it simpler for them to apply the same ideas to data products.
Conclusion
With the data mesh architecture, organizations can adopt a decentralized approach to managing their data stores. Primarily, a data mesh is more of an organizational change where data responsibilities are with each business stream. However, without technical expertise, organizations find it challenging and time-consuming to implement a data mesh.
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