Breaking Data Mesh

Data Mesh: A Decentralized Approach to Data Management

The traditional data management approach relies on centralized data warehouses and data lakes. These systems often become cumbersome and difficult to maintain due to:

• Complexity: Data sprawl and outdated information accumulate over time.
• Fragile Pipelines: Data pipelines (ETLs) can be unreliable and require constant monitoring.
• Centralized Bottleneck: The data engineering team gets overwhelmed managing data for various departments.

Inspired by Microservices

Data Mesh borrows the concept of microservices from the application development world. Here, domains (e.g., HR, Sales) own their data, similar to how microservices own specific functionalities.

• Domain-Owned Data: Each domain manages its data sources and creates data products for others to consume within the company. Think of the HR domain owning data from recruiting, payroll, and benefits systems, and providing datasets to other departments.
• Decentralized Governance: Domains are responsible for data governance within their area.
• Focus on Infrastructure: The engineering team focuses on building and maintaining data infrastructure (data lake, metadata) used by all domains, but doesn't manage domain-specific data.

Key Benefits

• Decentralized Storage: Data is distributed across domains, reducing reliance on a central system.
• Decentralized Ownership: Domains own and govern their data, fostering accountability.
• Scalability: The approach scales well for companies with a growing data demand that a centralized team can't handle.

Data Mesh is a data management philosophy that emphasizes domain ownership and decentralized governance. It's ideal for companies treating data as a product and facing limitations with traditional centralized approaches.