To fully appreciate the innovative approach of data mesh compared to traditional monolithic structures, we must delve into its foundational principles.
1. Treating Data as a tangible product
Central to the data mesh philosophy is the transformation of mere data into ‘data products.’ These are distinct, standalone units of data curated to address specific business challenges. The complexity of a data product can vary, from simple elements like tables or reports to intricate constructs such as full-fledged machine learning models.
A defining characteristic of data products is their well-specified interfaces, supplemented by endorsed contracts and version control. This framework not only facilitates seamless integration for those utilizing the data product but also safeguards against abrupt disruptions. In the data mesh ecosystem, any modifications are meticulously packaged and rolled out as updated versions by the data domain team.
2. Domain-centric data ownership and management
Within a data mesh, ownership isn’t a mere label; teams genuinely possess their data, encompassing every associated process from ingestion to processing and finally to distribution. Put simply, each team becomes the guardian of its unique data pipelines.
Just as one doesn’t need to grasp the intricacies of a method’s execution within an object-oriented programming construct, data product consumers needn’t concern themselves with the minutiae of data processing. This genuine ownership fosters a heightened sense of accountability and guardianship among teams, cultivating more precise and superior data quality in the long run.
3. Infrastructure for data: A Self-service paradigm
While each team taking charge of its data is the way forward, it’s not about constantly reinventing basic functions. Essential data and foundational data operations – like storage tools, data pipeline construction, or analytics generation – remain within the purview of the core data engineering team.
However, under the data mesh model, there’s a twist. These resources are readily accessible to all domain-specific teams. This approach not only democratizes data access but also ensures uniformity and reliability when teams craft their individual data products.
4. Robust security within a decentralized governance structure
Shifting to a self-service model for data signals the end of centralized, monolithic control over data. However, abandoning this centralized authority doesn’t imply total decentralization into chaos.
It remains paramount for businesses to delineate and uphold standards, especially concerning secure access, data structure, and quality. Additionally, continuous monitoring for compliance with sectoral and legal mandates, such as GDPR, is non-negotiable.
The data engineering sphere, within its self-service provision, incorporates a coherent framework emphasizing security and governance. This framework includes features like data cataloging to index and search data, tools for tagging and identifying sensitive data components, and automated systems to identify inconsistencies and validate regulatory adherence.
Get more useful articles on dbt
