Data Mesh
Data mesh is a decentralized architecture approach for data management that distributes accountability and ownership of data at the domain level.
What is Data Mesh?
Data Mesh is a relatively new approach, but one that has great potential for companies that want to optimize their data storage and management processes. Implementation requires careful planning and preparation, but can result in significant improvements in agility, scalability, data quality and Governance lead. As opposed to traditional, centralized data warehouse approaches Data Mesh promotes flexibility, scalability, and agility by defining data as products developed and managed by data-owned teams.
Key terms in data mesh
- Domains: Data is organized and managed along business domains (e.g. product, customers, finances) that can best understand and use the data.
- Product data: Each data domain team is responsible for creating and deploying their data as products that are available to other teams in the organization.
- Self-government: Data domain teams are solely responsible for quality, security and consistency of their data products.
- Decentralization: There is no central data store or controller. Instead, data is stored and managed across a distributed network of domains.
- Platform: A common data platform provides infrastructure, tools, and services that help domain teams build, manage, and deploy their data products.
- Data products: Data is packaged as curated, consistent, and consumable data sets with defined usage guidelines and interfaces.
- API: Domains make their data products available to other teams via APIs to enable access and use.
- Metadata: Comprehensive metadata describes the data products, their origin, meaning, quality and usage guidelines.
- More about Data and system architecture
Benefits of Data Mesh
- Increased agility and flexibility: Domain teams can respond faster to new data requirements and deliver data products faster.
- Improved scalability: The architecture can be scaled as the volume of data and the number of users grows.
- Lower complexity: The decentralized nature reduces complexity and maintenance costs.
- Increased data security and Governance: Domain teams are in control of their data and can provide clear Governance guidelines implement.
- Promoting data as a product: Domain teams view their data as products that need to be developed and optimized for internal and external use.
Data mesh use cases
- Large companies with complex data landscapes: Data mesh can address the challenges of managing and using data in large enterprises with multiple departments and types of data.
- Organizations with rapidly changing data requirements: Data mesh enables companies to respond quickly to new data requirements and deliver new data products.
- Organizations that want to make data-driven decisions: Data mesh can help companies make better use of their data to make data-based decisions.
Challenges of implementing data mesh
- Cultural change: Implementing data mesh requires a cultural shift towards a data-centric mindset and a distributed ownership model.
- Organizational structure: The organizational structure must be adjusted to support domain orientation and decentralized decision-making.
- Technical challenges: Integrating legacy systems and implementing a scalable data platform can be a technical challenge.
- Data governance: It must be clear Data quality policies and procedures, security and data protection are established throughout the company.
The topic of data mesh is addressed in Data Organization, Data architecture and Data Governance intense.
Note: Our team benefited from the support of AI technologies while creating and maintaining this glossary.
Is data mesh more than just a hype?
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