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Data Mesh

Resources, personal insights and notes.

Resources

How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh

How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh – Zhamak Dehghani (2019-05-20, martinfowler.com)

The data mesh is a technical and organizational architecture pattern to solve the organizational problem of needing multiple teams to develop multiple components and applications due to the size of the data analytics application landscape. The pattern describes how to break a data analytics landscape into services which can be owned by business and technical teams.
View data as a product
The business department is responsible for the data product
The product needs to have a certain quality to be attractive for others
- Discoverable
- Addressable
- Trustworthy and truthful
- Self-describing
- Inter-operable and governed by global standards
- Secure and governed by a global access control
- Built by a cross-functional domain data team with data product owner and data engineers
The data product must support
- General
  - Product evolution and product versioning
  - Unified data access control and data access logging
  - Pipeline implementation and orchestration
  - Data discovery
  - Data catalogue registration
  - Publishing
  - Data lineage
  - Monitoring, alerting, logging
  - Product quality metrics: definition, evaluation, collection and sharing of the metrics
- Use-case specific
  - Caching
  - Data de-identification
  - Data pseudonymization

Data Mesh Principles and Logical Architecture

Data Mesh Principles and Logical Architecture – Zhamak Dehghani (2020-12-03, martinfowler.com)

Define data consumer roles: e.g. data analyst, data scientist
Design interfaces to meet the data consumer roles' needs
It is more important to have a sovereign data product than to trade infrastructure synergies for dependencies.
Domain-oriented teams own decentralized data products
Data product consists of code, data, metadata, and infrastructure
Data products are discoverable, secure, explorable, understandable and trustworthy
The quality of data products is measured in data quality, lead time to data consumption, data user satisfaction
Data infrastructure enables teams to be autonomous
Teams can autonomously build, deploy, execute, monitor and access their data product
Data products can be used in combination
The emergent graph of data products must be managed
Data products have independent lifecycles, builts, deploys and teams
Data products must interoperate to get value in forms of higher order datasets and insights

Data Mesh: an Architectural Deep Dive

Data Mesh: an Architectural Deep Dive – Zhamak Dehghani (2021-11-26, infoq.com)

Data products should not be monolithic as data warehouses and data lakes.
Follow domain-driven design: Decouple architecture, data and data ownership by business domain
- Decomposition by business domain necessary to manage business and technical complexity.
- As with microservices one team should have a slice of business functionality and data while maintaining the full stack of their product.
Create interfaces to the domain, capabilities and APIs
Data products offer a baseline usability
Analytical APIs
- Provide historical data over time
- Polyglot data means that you might have many different spatial technologies of that time variant data.
  - Events for app developers
  - SQL for reporting
  - Columnar files for data scientists
- Design your API to be only read-only and be temporal time variant. Data needs to be immutable, otherwise it would have cascading effects.
A data product is an architectural quantum consisting of
- Code for
  - Transformations
  - Serving data
  - Embedding policies
- Data
  - Actual data
  - Metadata
- Storage
- SLOs
- Schema
- Syntax and semantics
- Documentation
- Policy configuration
One operational system can have one or more data products
Data products can be based on other data products creating a cascade
Multi-Plane data platform
- Utility plane
  - Orchestation and provisioning of Spark jobs
  - Creating a Kafka topic
  - Provisioning service accounts and storage accounts
- Provisioning and management of the data product lifecycle
  - Declaratively create them
  - Read data products and versions
- Consume
  - Product API
  - Schema
  - Semantics links to other data products they are related to
  - Browse and search data products
Three layers of interconnectivity
- Flow of data between input and output ports, the lineage
- Links between schemas, semantic links between the schema so that you can join this thing
- Link of the data

You Can't Buy Integration

You Can't Buy Integration – Brandon Byars (2021-12-14, martinfowler.com).

You Can't Buy Integration argues that the focus on integration architecture must be put on the interfaces to abstract capabilities and hide implementation details. One must not build the integration architecture from bottom up, i.e. from tools to interfaces, but top-down from interfaces to tools. This is the application of general application principles like SOLID and package principles to integration.
You Can't Buy Integration argues that integration is not just arrows between systems but that it is systems itself which need to follow system design principles like abstraction and reuse. Integrating two systems means abstracting the system details of the first system to generalize it's capabilities to have a reusable interface on that system. The second step is then to integrate the second system with this generalized capabilities interface. It's basically creating two adapters, one abstracting the first system and another to use this abstraction with the details of the second system.
Architect responsibility: Create a concept of the capabilities interfaces for integration implementation.
Don't be afraid of channel APIs like backends for frontends who's purpose it is to translate one interface into another interface better suiting the needs of the user because it separates system and channel which change for different reasons at different times and speed.
Focus on interfaces, interfaces are more important than implementation
Interfaces abstract capabilities not systems
Every interaction between two processes/components/systems is through interfaces
If a part of a data analytics application grants access to multiple down-stream components, then this is done via an interfaces and this interface must abstract the capabilities and not the systems. This means that the interface must add value and not just pass through data.