https://github.com/agile-lab-dev/witboost-practice-shaper-presets
https://github.com/agile-lab-dev/witboost-practice-shaper-presets
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/agile-lab-dev/witboost-practice-shaper-presets
- Owner: agile-lab-dev
- License: apache-2.0
- Created: 2024-07-09T08:36:51.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2025-01-09T15:57:27.000Z (over 1 year ago)
- Last Synced: 2025-03-15T18:52:58.407Z (about 1 year ago)
- Size: 108 KB
- Stars: 0
- Watchers: 5
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
Designed by [Agile Lab](https://www.agilelab.it/), Witboost is a versatile platform that addresses a wide range of sophisticated data engineering challenges. It enables businesses to discover, enhance, and productize their data, fostering the creation of automated data platforms that adhere to the highest standards of data governance. Want to know more about witboost? Check it out [here](https://witboost.com/platform) or [contact us!](https://witboost.com/contact-us)
This repository is part of our [Starter Kit](https://github.com/agile-lab-dev/witboost-starter-kit) meant to showcase witboost's integration capabilities and provide a "batteries-included" product.
# Practice Shaper Presets
- [Overview](#overview)
- [Property Graph](#property-graph)
- [Presets](#presets)
## Overview
The **Practice Shaper** is the main and most impactful witboost setting that models witboost entities (domains, systems, components, templates) as nodes of a fully-configurable **property graph**.
This enables data-oriented organizations to shape witboost based on their unique use cases, structure, and needs.
Thanks to the Practice Shaper, a company can approach any project scenario in data (**Data Landscape**), such as Data Mesh, Business Intelligence, Machine Learning and others, by defining which practices are enabled and regulated, with the possibility to define technological and methodological guardrails.
Refer to the [witboost documentation](https://docs.witboost.com) to learn more about Practice Shaper and Data Landscapes.
## Property Graph
The Practice Shaper property graph is composed of:
- **type nodes** - vertices representing abstract concepts (classes)
- **instance nodes** - concrete entities each instantiating one of the available type nodes
- **relations** - they are used to define relationships between nodes. For example, an instance node will always express an `instanceOf` relation towards a type node
Type nodes and the relations among them are defined by the Platform Team when configuring a Data Landscape.
Instance nodes and their relationships with type nodes (and other instance nodes) are generated by end users as they interact with the platform, within the context of a data landscape.
Both type and instance nodes are registered as witboost entities: their definition is provided by a catalog info YAML file (hosted in a Git repository), and they are part of the **witboost catalog**.
A sample Practice Shaper graph composed of data landscapes, domain types, system types, and component types, with their relationships:
## Presets
| Data Landscape | Description | Installation guide |
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------ |
| Data Mesh | The Data Mesh is a decentralized approach to data architecture that emphasizes domain-oriented ownership, self-serve data infrastructure, and treating data as a product. It shifts the responsibility for data management and governance from a centralized data team to the various business domains that generate and use the data | [Go to the guide](./src/data-landscapes/data-mesh/README.md) |
| Lakehouse Medallion | Medallion architecture, also known as the Lakehouse architecture, is a data architecture pattern that organizes data into different layers, or medallions, to enhance data processing efficiency, reliability, and governance. It typically involves three layers: Bronze, Silver, and Gold | [Go to](./src/data-landscapes/medallion) |
## License
This project is available under the [Apache License, Version 2.0](https://opensource.org/licenses/Apache-2.0); see [LICENSE](LICENSE) for full details.
## About us
From Chaos to Control: Data Experience on Guardrails
Craft your own standards and automate your data practice
[Contact us](https://witboost.com/contact-us) or follow us on:
- [LinkedIn](https://www.linkedin.com/showcase/witboost/)
- [Instagram](https://www.instagram.com/agilelab_official/)
- [YouTube](https://www.youtube.com/channel/UCTWdhr7_4JmZIpZFhMdLzAA)
- [Twitter](https://twitter.com/agile__lab)