Purpose
This is the repository for the Data Tech Virtues.
Definitions
Virtue
Behaviour showing high moral standards.
All virtues maybe recommended, suggested or mandatory.
Examples (fictitious):
- eating only rocks
- adhering to the database naming convention
- implementing a micro-service architecture
Principle
A broad, less defined virtue. Compliance to a principle will be difficult to measure and may be subjective.
Principles maybe recommended, suggested or mandatory.
Examples (fictitious):
- we all eat rocks {a principle because we do not define what a rock is}
- all JSON should be laid out in a pretty format when saved to file {a principle because we do not state the formatting and is therefore subjective }
- all database column names should have a business meaning {a principle because it is hard to measure compliance }
Standard
A clearly defined virtue to which an actor is only either compliant or not compliant.
Standards maybe recommended, suggested or mandatory.
Examples (fictitious):
- when placed in a basin of water a rock displaces at least 500ml of water
- all JSON files should be compressed using GZIP
- all database column names will follow the the pattern “column prefix convention #6”
Pattern
A general, reusable solution or set of instructions that will within a specific context solve a commonly occurring problem.
Patterns maybe recommended, suggested or mandatory.
Examples (fictitious)
- Raw Data Vault
- Micro-Service Architecture
- Scrum
- all database column names should start with the prefix “COL_”