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Creating a Data Mesh with mu-pipelines

Overview

In a modern data architecture, a Data Mesh is an emerging approach to decentralizing data ownership, treating data as a product, and enabling scalable data pipelines. mu-pipelines, a configuration-driven data pipeline platform, helps you implement a Data Mesh by providing an easy-to-use, open-source toolset that integrates multiple technologies while offering flexibility and automation.

This document will guide you through the steps to create a Data Mesh using mu-pipelines.

Prerequisites

Before you start, ensure you have the following:

  • mu-pipelines installed.
  • Knowledge of the tools that will be integrated into mu-pipelines (e.g., Kafka, Delta Lake, Iceberg).

Understanding of the Data Mesh principles:

  • Domain-Oriented Data Ownership
  • Data as a Product
  • Self-Serve Data Infrastructure
  • Federated Computational Governance

Define Your Data Domains

In a Data Mesh, data is organized into domains—individual business or operational units responsible for their data products. To create a Data Mesh with mu-pipelines, you first need to define the domains and ensure that each domain can independently own, manage, and expose data.

Example:

  • Sales Domain: Sales data that includes transactions, customers, and products.
  • Marketing Domain: Campaigns, website traffic, and customer engagement data.
  • Finance Domain: Billing, revenue, and financial reporting data.

Each domain should have:

A data owner responsible for managing and curating the data.

A data product that other domains can consume or interact with.

Set Up Data Pipeline Repositories

Each domain is responsible for creating a repository that will house the data in its specific layers. These repositories are where all the data processing takes place.

Example repository structure for the Sales Domain:

sales/
├── raw/
│   ├── sales_transactions/
│   └── sales_leads/
├── silver/
│   ├── cleaned_sales_transactions/
│   └── enriched_sales_leads/
└── gold/
    ├── aggregated_sales_data/
    └── sales_performance_reports/

Each domain will have:

  • A raw layer for unprocessed data straight from the source system.
  • A silver layer for intermediate transformations (e.g., data cleansing, enrichment).
  • A gold layer for refined data, optimized for reporting and business decision-making.

Above is an example, each organization can decide what is needed based on their ways of working.

Each domain team writes configuration files that define the ingestion, transformation, and destination steps for their respective data products. mu-pipelines uses a JSON configuration file format for flexibility and ease of use.

Self-Service for Domain Teams

By organizing data into clear, consistent layers and empowering domain teams to build and manage their own repositories, mu-pipelines enables self-service data infrastructure. Domain teams can manage their entire data lifecycle, from ingestion through transformation to storage, without needing to rely on a centralized data platform team for each change.

The data platform team provides best practices for writing configurations, handling security, ensuring data quality, and governing access. Domain teams follow these practices to ensure their data products meet the organization's overall data quality, security, and performance standards.

Enterprise-Wide Repository Management

At the end of the day, the enterprise will have multiple repositories—one for each domain. These repositories, organized into raw, silver, and gold layers, enable easy access and management of domain-specific data products while preserving the data product ownership model of Data Mesh.

Example of an enterprise-wide repository structure:


enterprise_data/
├── sales/
│   ├── raw/
│   ├── silver/
│   └── gold/
├── marketing/
│   ├── raw/
│   ├── silver/
│   └── gold/
├── finance/
│   ├── raw/
│   ├── silver/
│   └── gold/

Supplement the setup with data governance best practices.