Data mesh
A data mesh is a decentralized data architecture and operating model in which domain teams provide data as products through shared self-service infrastructure and federated governance.
Apache Kafka in Action
A data mesh is a decentralized data architecture in which domain-oriented teams own and provide data as products through shared self-service infrastructure and federated governance.
- How it is used
- Organizes departmental data sharing around owned data products on shared infrastructure instead of central-team-managed integration.
- How deep it goes
- Explains four principles and departmental responsibilities, then identifies Kafka’s publishing, schema, and governance roles.
- Examples
- Contrasts broken central ETL pipelines after structural changes with department-owned products whose owners update and document schemas.
- What you can do after
- Apply it via Kafka to exchange governed departmental data products in near real time.
Kafka for Architects
A data mesh architecture is a decentralized data architecture in which domain teams own and provide data products under federated governance.
- How it is used
- Uses decentralized data products to reduce central analytical bottlenecks while retaining enterprise governance.
- How deep it goes
- Defines four principles and maps them to Kafka, but stops short of prescribing an implementation technology or detailed operating procedures.
- Examples
- Contrasts a central data warehouse with domain-owned analytical data published through Kafka topics and schemas.
- What you can do after
- Evaluate whether Kafka can support domain ownership, data products, federated governance, and self-serve analytical access.
Architecture Modernization: Socio-technical alignment of software, strategy, and structure
Data mesh is a decentralized data architecture and operating model in which domain-oriented teams own and provide data as products through shared self-service infrastructure and federated governance.
- How it is used
- Deploys a decentralized operating model for governing, producing, and consuming data across domain-oriented teams.
- How deep it goes
- Explains four principles, data quanta, contracts, experience planes, and adoption steps, while deferring technology-specific implementation details.
- Examples
- Uses dashboards, reports, risk analysis, fraud detection, AI, data quanta, contracts, sidecars, and experience planes as concrete reference points.
- What you can do after
- Build it from scratch by defining principles, prototyping data products, composing data quanta, and evolving shared platform capabilities.
Related concepts
- Apache Kafkaused by · Apache Kafka is a distributed event-streaming platform for publishing, storing, processing, and consuming streams of records.
- domain ownershiphas part · Domain ownership is the assignment of responsibility for a business domain’s data, including its meaning, structure, quality, documentation, and governance, to the domain that produces it.
- data producthas part · A data product is a product, service, or managed data asset whose value is delivered through data, processing, analysis, or data-driven functionality.
- decentralized governancehas part · Decentralized governance is a governance model in which data standards and controls are established collaboratively across domains rather than imposed solely by a central team.
- self-service data infrastructurehas part · Self-service data infrastructure is shared technical infrastructure that lets domain teams independently publish, consume, modify, and operate data capabilities without case-by-case intervention from a central data team.
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