At Coalesce, Tristan Handy announced "dbt mesh". dbt Mesh emerges as a significant pattern for organizations grappling with complex data transformation challenges. It's not a standalone product but a convergence of features within dbt (data build tool) that collectively empower data teams to manage their workflows more effectively.
dbt Mesh is a mix between dbt & Data Mesh
What is data Mesh?
Data Mesh is a decentralized approach to managing data that contrasts sharply with traditional monolithic data architectures. It's a design that distributes data ownership across different domains within an organization, allowing teams to manage their own data pipelines and processes. This shift mirrors the evolution in software engineering from monolithic applications to service-oriented architectures, emphasizing small, autonomous teams responsible for well-defined components.
Data Mesh is built upon several foundational principles:
- Data as a Product: Data Mesh treats data not just as an asset but as a product, with defined interfaces, contracts, and versioning, ensuring reliability and minimizing unexpected disruptions.
- Domain-oriented Ownership: Teams have end-to-end ownership of their data, including ingestion, processing, and serving, fostering a sense of responsibility and improving data quality.
- Self-service Data Infrastructure: A central data engineering team provides a suite of tools for data storage, pipeline creation, and analytics, enabling domain teams to build their data products independently.
- Federated Governance: While promoting autonomy, Data Mesh also enforces a consistent framework for security and governance across the organization.
What is dbt Mesh?
dbt Mesh is a pattern that facilitates better coordination and governance of multiple dbt projects, especially as organizations scale. It addresses the need for managing dependencies, governance, and workflows between these projects.
Key Components of dbt Mesh
dbt Mesh is built on several key functionalities that together create a robust environment for data teams:
- Inter-Project Linking: By enabling references across different dbt projects, this feature lays the groundwork for deploying multiple projects simultaneously. Enabling ref() functions to work across dbt Cloud projects on Enterprise plans.
- dbt Explorer: A centralized platform that offers a complete overview of project lineage, ensuring transparency and traceability. Can be linked easily to CastorDoc data catalog for cross-system lineage & governance.
- Governance features: These new additions help manage access to dbt models both within and across projects.
- Groups and Access Configs: They allow for the assignment of models to subsets within a project and control who can reference these models.
- Model Versions and Contracts: These features treat data models as stable APIs, allowing for the graceful adoption and deprecation of models as they evolve. Data contracts set explicit expectations on the shape of the data to ensure changes don't break downstream consumers' data products.
Who Benefits from dbt Mesh?
The adoption of a Data Mesh architecture, complemented by dbt Mesh's tooling, offers scalability and democratization of data. It empowers domain teams to develop data products more efficiently, with improved time-to-market and data quality. However, it requires a high level of organizational maturity, robust data platforms, and a strong governance policy to avoid "data anarchy."
dbt Mesh is particularly beneficial for organizations with mature dbt implementations facing performance degradation due to a large number of models, the need for decoupled development workflows among teams, or increasing security and governance requirements. It's designed to simplify the complexity of coordinating these advanced features to solve such problems.
For those new to dbt, the advice is to not rush into a multi-project architecture. The features of dbt Mesh can be adopted incrementally as an organization scales. Each feature can function effectively as an independent tool, and familiarizing with them can aid in making informed decisions for future growth.
Learning Goals with dbt Mesh
The dbt Mesh guide aims to help users:
- Understand the purpose and tradeoffs of building a multi-project architecture.
- Develop an intuition for various dbt Mesh patterns.
- Design a multi-project architecture tailored to their organization.
- Establish steps to incrementally adopt these patterns in their dbt implementation.
This article has provided a foundational understanding of dbt Mesh, outlining its components, benefits, and the types of organizations that would most benefit from its implementation. As data ecosystems grow in complexity, dbt Mesh stands as a robust solution for managing that complexity effectively.
Superpower dbt Mesh with a data catalog
dbt Mesh and data catalogs are complementary tools in the modern data stack that together enhance the organization and accessibility of data within an organization. dbt Mesh facilitates the management of dependencies and workflows across multiple dbt projects, ensuring that data transformations are consistent and well-governed. On the other hand, data catalogs serve as a centralized repository for metadata, providing data practitioners with a searchable inventory of data resources. When integrated, dbt Mesh's governance and cross-project coordination capabilities are augmented by the data catalog's ability to index and classify data assets, making it easier for teams to discover and understand the data products they need. This synergy not only streamlines data operations but also promotes a culture of data discovery and literacy, ensuring that the right data is used in the right way across the enterprise.
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Fantastic tool for data discovery and documentation
“[I like] The easy to use interface and the speed of finding the relevant assets that you're looking for in your database. I also really enjoy the score given to each table, [which] lets you prioritize the results of your queries by how often certain data is used.”
Michal, Head of Data, Printify