data-mesh
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| ====== Data Mesh ====== | ====== Data Mesh ====== | ||
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| + | ===== Resources ===== | ||
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| + | * **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, | ||
| + | * The **quality** of data products **is measured in data quality, lead time to data consumption, | ||
| + | * **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** | ||
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| + | ===== Designing data products ===== | ||
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| + | * "Data products **package structured, semi-structured or unstructured analytical data for effective consumption** and data driven decision making, keeping in mind specific user groups and their consumption pattern for these analytical data." | ||
| + | * "One of the most prevalent misunderstandings is **conflating data products with data-driven applications**. Data products are natively **designed for programmatic access and composability**, | ||
| + | * "**No single data product should be owned by multiple domains**, as this can lead to confusion and finger-pointing over quality issues." | ||
| + | * "While it's important to consider domain ownership early, **it’s often more efficient to have a single team develop all the necessary data products** to realise the use case at the start of your data mesh journey. Splitting the work among multiple teams too early can increase coordination overhead, which is best delayed. Our recommendation is to begin with a small, cohesive team that handles all data products for the use case. As you progress, use “team cognitive load” as a guide for when to split into specific domain teams." | ||
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| + | * "**For structured data, this usually means a single denormalized table**, and for semi-structured or unstructured data, a single dataset. Anything larger is likely trying to do too much, making it harder to explain its purpose in a clear, concise sentence and reducing its composability and reusability." | ||
| + | * "In our experience, **it's beneficial for each data product identified earlier to have its own source control repository and associated deployment pipeline**, allowing for independent management of its lifecycle." | ||
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| + | * **Data products should not be monolithic** as data warehouses and data lakes. | ||
| + | * **Follow domain-driven design**: Decouple architecture, | ||
| + | * **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 | ||
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| + | ===== Governing data products using fitness functions ===== | ||
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| + | * Decentralization requires governance at scale | ||
| + | * Small autonomous teams need more governance than a central team | ||
| + | * "The term “data product“ has unfortunately taken on various self-serving meanings [...]" | ||
| + | * Data product: [[https:// | ||
| + | * " | ||
| + | * Fitness function | ||
| + | * Test used to evaluate how close a given implementation is to its stated design objectives | ||
| + | * Shift left on governance: identify potential governance issues early to enable proactive improvement | ||
| + | * Prioritization | ||
| + | * Goverance by rule over Governance by inspection | ||
| + | * Empowering teams to discover problems over Independent audits | ||
| + | * Continuous governance over Dedicated audit phase | ||
| + | * Organization wide data catalog to index data products with rich metadata | ||
| + | * Assertions within the catalog: Tools like Collibra and Datahub provide hooks | ||
| + | * Assertions outside of the catalog | ||
| + | * Assessing metadata with LLMs | ||
| + | * Reporting of results | ||
data-mesh.1642596649.txt.gz · Last modified: by mrks