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Data Mesh Starter Kit

$69

Organizational and technical toolkit for data mesh: maturity assessment, domain decomposition, self-serve templates, and federated governance.

📁 16 files🏷 v1.0.0
PythonTerraformMarkdownJSONDatabricksCI/CD

📁 File Structure 16 files

data-mesh-starter-kit/ ├── README.md ├── assessment/ │ ├── maturity_assessment.md │ └── scoring_calculator.py ├── case_study/ │ └── transition_case_study.md ├── communication/ │ ├── data_product_catalog.md │ └── domain_agreements.md ├── governance/ │ ├── federated_governance_model.md │ └── platform_team_responsibilities.md ├── templates/ │ ├── data_product_scorecard.md │ └── data_product_template.py ├── terraform/ │ └── domain-workspace/ │ ├── main.tf │ ├── outputs.tf │ └── variables.tf └── workshops/ ├── domain_decomposition.md └── stakeholder_alignment.md

📖 Documentation Preview README excerpt

Data Mesh Starter Kit

Product ID: data-mesh-starter-kit

Version: 1.0.0

Price: $69 USD

Author: [Datanest Digital](https://datanest.dev)

Category: Data Engineering

---

Overview

The Data Mesh Starter Kit is a comprehensive, practitioner-focused toolkit for organizations transitioning from centralized data architectures to a domain-oriented data mesh. It provides assessments, workshop templates, Terraform infrastructure modules, governance frameworks, and communication templates — everything a data engineering team needs to plan, execute, and sustain a data mesh adoption.

This kit is designed for:

  • Data Platform Engineers building self-serve infrastructure for domain teams
  • Data Architects designing federated ownership models
  • Engineering Managers aligning stakeholders on data mesh principles
  • CTOs and VPs of Engineering evaluating data mesh readiness

What's Included

Assessment Tools

| File | Description |

|------|-------------|

| assessment/maturity_assessment.md | 50-question data mesh maturity assessment with scoring rubric across 5 capability dimensions |

| assessment/scoring_calculator.py | Python CLI tool that calculates maturity scores, generates radar charts, and produces recommendations |

Workshop Templates

| File | Description |

|------|-------------|

| workshops/domain_decomposition.md | Facilitated workshop for identifying domain boundaries, data product candidates, and ownership mapping |

| workshops/stakeholder_alignment.md | Workshop template for building consensus on data mesh adoption across leadership and engineering |

Infrastructure as Code

| File | Description |

|------|-------------|

| terraform/domain-workspace/main.tf | Terraform module for provisioning isolated domain workspaces with storage, compute, catalog, and access controls |

| terraform/domain-workspace/variables.tf | Configurable variables for cloud provider, domain naming, resource sizing, and tagging |

| terraform/domain-workspace/outputs.tf | Module outputs for integration with CI/CD pipelines and service catalogs |

Data Product Templates

| File | Description |

|------|-------------|

| templates/data_product_template.py | Standardized data product interface for Databricks notebooks with schema contracts, SLA definitions, and quality checks |

| templates/data_product_scorecard.md | Quality scorecard for evaluating data products across discoverability, usability, trustworthiness, and interoperability |

Governance Framework

| File | Description |

|------|-------------|

| governance/federated_governance_model.md | Federated governance framework balancing central standards with domain autonomy |

| governance/platform_team_responsibilities.md | Platform team role definitions, RACI matrix, and interface contracts with domain teams |

Communication Templates

... continues with setup instructions, usage examples, and more.

📄 Code Sample .py preview

assessment/scoring_calculator.py #!/usr/bin/env python3 """ Data Mesh Maturity Assessment — Scoring Calculator =================================================== Datanest Digital (https://datanest.dev) Data Mesh Starter Kit v1.0.0 Reads a JSON answer file produced from the maturity assessment and generates scored reports in JSON, text, or CSV format. Usage: python scoring_calculator.py --input answers.json python scoring_calculator.py --input answers.json --format text python scoring_calculator.py --input answers.json --format csv --output scores.csv python scoring_calculator.py --input answers.json --output report.json """ from __future__ import annotations import argparse import csv import io import json import sys from dataclasses import dataclass, field from datetime import datetime from pathlib import Path from typing import Any # --------------------------------------------------------------------------- # Constants # --------------------------------------------------------------------------- DIMENSIONS: dict[str, dict[str, Any]] = { "D1": { "name": "Domain Ownership & Decomposition", "questions": [f"Q{i}" for i in range(1, 11)], }, "D2": { "name": "Data as a Product", "questions": [f"Q{i}" for i in range(11, 21)], }, "D3": { "name": "Self-Serve Data Platform", "questions": [f"Q{i}" for i in range(21, 31)], }, "D4": { "name": "Federated Computational Governance", "questions": [f"Q{i}" for i in range(31, 41)], }, # ... 376 more lines ...
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